diff --git a/README.md b/README.md
index c595b2d..5b0c021 100644
--- a/README.md
+++ b/README.md
@@ -1,46 +1,137 @@
 # LibreFang Registry
 
-Community-maintained content registry for [LibreFang](https://github.com/librefang/librefang) -- the open-source Agent Operating System.
+Community-maintained content registry for [LibreFang](https://github.com/librefang/librefang) — the open-source Agent Operating System.
 
-This repository is the single source of truth for all installable content definitions. Anyone can submit a PR to add new agents, hands, integrations, skills, or provider models -- no changes to the LibreFang binary required.
+This repository is the **single source of truth** for all installable content definitions. Anyone can submit a PR to add new agents, hands, integrations, skills, or provider models — no changes to the LibreFang binary required.
 
-## Structure
+## Overview
+
+| Type | Count | Description |
+|------|------:|-------------|
+| [Hands](#hands) | 14 | User-facing "apps" — agent + tools + settings + dashboard |
+| [Agents](#agents) | 32 | Autonomous agent definitions with model config and tools |
+| [Integrations](#integrations) | 25 | MCP server connections (GitHub, Slack, DBs, etc.) |
+| [Providers](#providers) | 48 | LLM provider & model metadata with pricing |
+| [Models](#providers) | 223 | Individual model definitions across all providers |
+| [Aliases](#aliases) | 70 | Short names mapped to canonical model IDs |
+| [Plugins](#plugins) | 10 | Memory, guardrails, and conversation plugins |
+| [Skills](#skills) | 2 | Reusable prompt templates and Python scripts |
+
+## Repository Structure
 
 ```
 librefang-registry/
-├── agents/             # Agent definitions (TOML manifests)
-│   ├── hello-world/agent.toml
-│   ├── researcher/agent.toml
-│   └── ...             (33 agents)
-├── hands/              # Hand definitions (TOML + docs)
-│   ├── browser/HAND.toml
-│   ├── trader/HAND.toml
-│   └── ...             (14 hands)
-├── integrations/       # MCP server integration templates
+├── agents/                # Agent definitions (TOML manifests)
+│   ├── hello-world/
+│   │   └── agent.toml
+│   ├── researcher/
+│   │   └── agent.toml
+│   └── ...                (32 agents)
+├── hands/                 # Hand definitions (app bundles)
+│   ├── browser/
+│   │   ├── HAND.toml      # Metadata, tools, settings, i18n (6 languages)
+│   │   └── SKILL.md       # Domain expert knowledge injected at runtime
+│   ├── trader/
+│   │   ├── HAND.toml
+│   │   └── SKILL.md
+│   └── ...                (14 hands)
+├── integrations/          # MCP server integration templates
 │   ├── github.toml
 │   ├── slack.toml
-│   └── ...             (25 integrations)
-├── skills/             # Reusable skill definitions
-│   ├── custom-skill-prompt/skill.toml
-│   └── custom-skill-python/
-├── providers/          # LLM provider & model metadata
+│   └── ...                (25 integrations)
+├── providers/             # LLM provider & model metadata
 │   ├── anthropic.toml
 │   ├── openai.toml
-│   └── ...             (46 providers, 190+ models)
-├── plugins/            # Plugin packages (10 plugins)
-├── aliases.toml        # Global model alias mappings
-├── schema.toml         # Provider/model schema reference
+│   └── ...                (48 providers, 223 models)
+├── plugins/               # Memory, guardrails, and utility plugins
+│   ├── episodic-memory/
+│   ├── guardrails/
+│   └── ...                (10 plugins)
+├── skills/                # Reusable skill definitions
+│   ├── custom-skill-prompt/skill.toml
+│   └── custom-skill-python/
+├── aliases.toml           # Global model alias mappings (70 aliases)
+├── schema.toml            # Provider/model schema reference
 ├── scripts/
-│   └── validate.py     # Validation script
+│   └── validate.py        # Content validation script
 ├── CONTRIBUTING.md
-└── LICENSE             # MIT
+└── LICENSE                # MIT
 ```
 
 ## Content Types
 
+### Hands
+
+Hands are the **user-facing "apps"** in LibreFang. Each hand bundles an agent, tools, user-configurable settings, dashboard metrics, dependency checks, and i18n translations into a single deployable unit.
+
+Every hand includes a `SKILL.md` — domain-specific expert knowledge that is injected into the agent's context at runtime, giving it deep expertise in its domain.
+
+| Icon | Hand | Category | Description |
+|:----:|------|----------|-------------|
+| 📈 | analytics | data | Data collection, analysis, visualization, dashboards, and automated reporting |
+| 🔌 | apitester | development | Endpoint discovery, request validation, load testing, and regression detection |
+| 🌐 | browser | productivity | Web navigation, form filling, and multi-step web tasks with user approval |
+| 🎬 | clip | content | Turns long-form video into viral short clips with captions and thumbnails |
+| 🔍 | collector | data | Intelligence collection, change detection, and knowledge graphs |
+| 👷 | devops | development | CI/CD management, infrastructure monitoring, deployment, and incident response |
+| 📊 | lead | data | Lead generation, enrichment, scoring, and scheduled delivery |
+| 💼 | linkedin | communication | Profile optimization, content creation, networking, and engagement |
+| 🔮 | predictor | data | Signal collection, calibrated predictions, and accuracy tracking |
+| 📢 | reddit | communication | Subreddit monitoring, content posting, and engagement tracking |
+| 🧪 | researcher | productivity | Deep research, cross-referencing, fact-checking, and structured reports |
+| 🎯 | strategist | productivity | Market research, competitive analysis, and strategic planning |
+| 📈 | trader | data | Multi-signal analysis, adversarial reasoning, and risk management |
+| 𝕏 | twitter | communication | Content creation, scheduled posting, engagement, and analytics |
+
+**HAND.toml format:**
+
+```toml
+id = "browser"
+name = "Browser Hand"
+description = "Autonomous web browser"
+category = "productivity"
+icon = "🌐"
+tools = ["browser_navigate", "browser_click", "browser_type"]
+
+[routing]
+aliases = ["browse", "open website"]
+weak_aliases = ["web", "url"]
+
+[[requires]]
+key = "chromium"
+requirement_type = "binary"
+check_value = "chromium"
+
+[[settings]]
+key = "headless"
+setting_type = "toggle"
+default = "true"
+
+[agent]
+name = "browser-hand"
+module = "builtin:chat"
+system_prompt = """You are an autonomous web browser agent..."""
+
+[dashboard]
+[[dashboard.metrics]]
+label = "Pages Visited"
+memory_key = "pages_visited"
+format = "number"
+
+# i18n — 6 languages supported: zh, ja, ko, es, fr, de
+[i18n.zh]
+name = "浏览器 Hand"
+description = "自主网页浏览器"
+category = "生产力"
+
+[i18n.zh.settings.headless]
+label = "无头模式"
+description = "在后台运行浏览器"
+```
+
 ### Agents
 
-Agent definitions in `agents/<name>/agent.toml` describe autonomous agents with their model config, tools, capabilities, and routing aliases.
+Agent definitions describe autonomous agents with model configuration, tools, capabilities, and routing aliases.
 
 ```toml
 name = "hello-world"
@@ -56,30 +147,11 @@ system_prompt = "You are a helpful assistant."
 tools = ["web_search", "file_read"]
 ```
 
-### Hands
-
-Hands in `hands/<name>/HAND.toml` are higher-level application bundles -- the user-facing "apps" in LibreFang. Each hand bundles an agent config, tools, settings, dashboard metrics, and dependency requirements.
-
-```toml
-id = "browser"
-name = "Browser Hand"
-category = "productivity"
-tools = ["browser_navigate", "browser_click", "browser_type"]
-
-[agent]
-name = "browser-hand"
-module = "builtin:chat"
-system_prompt = "You are an autonomous web browser agent..."
-
-[[settings]]
-key = "headless"
-setting_type = "toggle"
-default = "true"
-```
+**32 built-in agents:** academic-researcher, analyst, architect, assistant, code-reviewer, coder, customer-support, data-scientist, debugger, devops-lead, doc-writer, email-assistant, health-tracker, hello-world, home-automation, legal-assistant, meeting-assistant, ops, orchestrator, personal-finance, planner, recipe-assistant, recruiter, researcher, sales-assistant, security-auditor, social-media, test-engineer, translator, travel-planner, tutor, writer
 
 ### Integrations
 
-Integration templates in `integrations/<name>.toml` define MCP server connections (GitHub, Slack, databases, etc.) with transport config, required env vars, and setup instructions.
+Integration templates define [MCP](https://modelcontextprotocol.io/) server connections with transport configuration, required environment variables, and setup instructions.
 
 ```toml
 id = "github"
@@ -96,9 +168,47 @@ name = "GITHUB_PERSONAL_ACCESS_TOKEN"
 is_secret = true
 ```
 
+**25 integrations across 6 categories:**
+
+| Category | Integrations |
+|----------|-------------|
+| DevTools | bitbucket, github, gitlab, jira, linear, sentry |
+| Data | elasticsearch, mongodb, postgresql, redis, sqlite |
+| Productivity | dropbox, gmail, google-calendar, google-drive, notion, todoist |
+| Communication | discord, slack, teams |
+| Cloud | aws, azure, gcp |
+| AI Search | brave-search, exa-search |
+
+### Providers
+
+Provider files define LLM providers and their models with pricing, context windows, and capability flags. See [schema.toml](schema.toml) for the full field reference.
+
+**48 providers** including: Anthropic, OpenAI, Google Gemini, DeepSeek, Groq, Mistral, Cohere, xAI, Together, Fireworks, Ollama (local), LM Studio (local), vLLM (self-hosted), and many more.
+
+**223 models** with metadata for each: pricing (input/output per token), context window size, capability flags (vision, function calling, streaming), and tier classification.
+
+### Aliases
+
+Global model alias mappings in [aliases.toml](aliases.toml) let users reference models by short names:
+
+```toml
+"sonnet" = "claude-sonnet-4-6"
+"gpt4" = "gpt-4o"
+"flash" = "gemini-2.5-flash"
+"deepseek" = "deepseek-chat"
+```
+
+Models can also define aliases directly in their provider TOML files, which are auto-registered at load time.
+
+### Plugins
+
+Plugins extend agent capabilities with memory systems, safety guardrails, and conversation utilities.
+
+**10 plugins:** auto-summarizer, context-decay, conversation-logger, episodic-memory, guardrails, keyword-memory, sentiment-tracker, todo-tracker, topic-memory, user-profile
+
 ### Skills
 
-Skills in `skills/<name>/skill.toml` are reusable prompt templates or Python scripts that agents can invoke.
+Reusable prompt templates or Python scripts that agents can invoke.
 
 ```toml
 [skill]
@@ -112,13 +222,9 @@ type = "promptonly"
 template = "Create a meeting agenda for: {{topic}}"
 ```
 
-### Providers
+## Usage
 
-Provider files in `providers/<name>.toml` define LLM providers and their models with pricing, context windows, and capability flags. See [schema.toml](schema.toml) for the full field reference.
-
-## How LibreFang Uses This Registry
-
-LibreFang ships with built-in content compiled into the binary. This repository serves as the upstream source for updates and community contributions.
+### Install from Registry
 
 ```bash
 # Update all registry content
@@ -133,15 +239,15 @@ librefang integration install github
 
 ### Custom Local Content
 
-You can also create custom content locally without submitting a PR:
+Create custom content locally without submitting to this registry:
 
 ```bash
-# Create a custom agent
+# Custom agent
 mkdir -p ~/.librefang/agents/my-agent
 # Edit ~/.librefang/agents/my-agent/agent.toml
 
-# Add custom models to your config
-# ~/.librefang/model_catalog.toml
+# Custom model aliases
+# Add to ~/.librefang/model_catalog.toml
 ```
 
 ## Validation
@@ -150,9 +256,9 @@ mkdir -p ~/.librefang/agents/my-agent
 python scripts/validate.py
 ```
 
-This validates all provider TOML files for correctness: required fields, valid tiers, non-negative costs, no duplicate IDs.
+Validates all content files for correctness: required fields, valid types, non-negative costs, no duplicate IDs.
 
-## How to Contribute
+## Contributing
 
 1. Fork this repository
 2. Add or edit content in the appropriate directory
@@ -161,19 +267,6 @@ This validates all provider TOML files for correctness: required fields, valid t
 
 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed instructions for each content type.
 
-## Current Stats
-
-| Type | Count |
-|------|-------|
-| Agents | 33 |
-| Hands | 14 |
-| Integrations | 25 |
-| Skills | 2 |
-| Plugins | 10 |
-| Providers | 46 |
-| Models | 220+ |
-| Aliases | 80+ |
-
 ## License
 
 MIT License. See [LICENSE](LICENSE).
diff --git a/hands/README.md b/hands/README.md
index 8802579..6ca99e0 100644
--- a/hands/README.md
+++ b/hands/README.md
@@ -10,7 +10,7 @@ Hand definitions for LibreFang. Hands are the user-facing "apps" -- higher-level
 hands/
 ├── browser/
 │   ├── HAND.toml    # Hand definition
-│   └── SKILL.md     # Documentation
+│   └── SKILL.md     # Expert knowledge for the agent
 ├── trader/
 │   ├── HAND.toml
 │   └── SKILL.md
@@ -47,6 +47,17 @@ name = "hand-agent"
 module = "builtin:chat"
 system_prompt = """..."""
 
+# Optional: i18n for name, description, category, and settings
+# Supported languages: zh, ja, ko, es, fr, de
+[i18n.zh]
+name = "浏览器 Hand"
+description = "自主网页浏览器"
+category = "生产力"
+
+[i18n.zh.settings.headless]      # Per-setting label/description translation
+label = "无头模式"
+description = "在后台运行浏览器"
+
 [dashboard]                       # Dashboard metrics
 [[dashboard.metrics]]
 label = "Tasks Completed"
@@ -58,15 +69,24 @@ format = "number"
 
 | Hand | Category | Description |
 |------|----------|-------------|
-| browser | productivity | Autonomous web browser |
-| trader | data | Crypto/stock trading assistant |
-| researcher | productivity | Deep research automation |
-| analytics | data | Data analysis and dashboards |
-| ... | | See each directory for details |
+| analytics | data | Data analytics, visualization, dashboards, and automated reporting |
+| apitester | development | API testing, endpoint discovery, load testing, and regression detection |
+| browser | productivity | Web navigation, form filling, and multi-step web tasks |
+| clip | content | Turns long-form video into short clips with captions and thumbnails |
+| collector | data | Intelligence collection, change detection, and knowledge graphs |
+| devops | development | CI/CD management, infrastructure monitoring, and incident response |
+| lead | data | Lead generation, enrichment, scoring, and scheduled delivery |
+| linkedin | communication | LinkedIn content creation, networking, and engagement |
+| predictor | data | Signal collection, calibrated predictions, and accuracy tracking |
+| reddit | communication | Subreddit monitoring, content posting, and engagement tracking |
+| researcher | productivity | Deep research, cross-referencing, fact-checking, and reports |
+| strategist | productivity | Market research, competitive analysis, and strategic planning |
+| trader | data | Market intelligence, multi-signal analysis, and risk management |
+| twitter | communication | Twitter/X content creation, scheduling, and performance tracking |
 
 ## Adding a New Hand
 
-1. Create `hands/<name>/HAND.toml` (and optionally `SKILL.md`)
+1. Create `hands/<name>/HAND.toml` and `SKILL.md` (expert knowledge for the agent)
 2. Ensure `id` matches the directory name
 3. Run `python scripts/validate.py`
 4. Submit a PR
diff --git a/hands/analytics/HAND.toml b/hands/analytics/HAND.toml
index 1d35f06..169c41e 100644
--- a/hands/analytics/HAND.toml
+++ b/hands/analytics/HAND.toml
@@ -484,6 +484,217 @@ token_consumption = "high"
 default_active = true
 # Note: High consumption when actively analyzing data, lower when idle
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "数据分析 Hand"
 description = "自主数据分析智能体——数据采集、分析、可视化、仪表盘和自动化报告"
+category = "数据"
+
+[i18n.zh.settings.data_source]
+label = "数据源"
+description = "主要数据源类型"
+
+[i18n.zh.settings.analysis_type]
+label = "分析类型"
+description = "默认分析方法"
+
+[i18n.zh.settings.output_format]
+label = "输出格式"
+description = "分析结果的呈现方式"
+
+[i18n.zh.settings.visualization]
+label = "可视化"
+description = "生成图表和可视化内容"
+
+[i18n.zh.settings.auto_schedule]
+label = "定时报告"
+description = "按计划自动生成报告"
+
+[i18n.zh.settings.report_frequency]
+label = "报告频率"
+description = "定时报告的生成频率"
+
+[i18n.zh.settings.confidence_threshold]
+label = "置信度阈值"
+description = "报告中纳入分析结论的最低置信度要求"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "データ分析 Hand"
+description = "自律型データ分析エージェント——データ収集、分析、可視化、ダッシュボード、自動レポート生成"
+category = "データ"
+
+[i18n.ja.settings.data_source]
+label = "データソース"
+description = "主要なデータソースの種類"
+
+[i18n.ja.settings.analysis_type]
+label = "分析タイプ"
+description = "デフォルトの分析アプローチ"
+
+[i18n.ja.settings.output_format]
+label = "出力形式"
+description = "分析結果の表示方法"
+
+[i18n.ja.settings.visualization]
+label = "可視化"
+description = "チャートやビジュアライゼーションを生成する"
+
+[i18n.ja.settings.auto_schedule]
+label = "定期レポート"
+description = "スケジュールに基づいてレポートを自動生成する"
+
+[i18n.ja.settings.report_frequency]
+label = "レポート頻度"
+description = "定期レポートの生成頻度"
+
+[i18n.ja.settings.confidence_threshold]
+label = "信頼度しきい値"
+description = "レポートに分析結果を含めるための最低信頼度"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Analítica"
+description = "Agente autónomo de analítica de datos — recopilación, análisis, visualización, paneles de control e informes automatizados"
+category = "Datos"
+
+[i18n.es.settings.data_source]
+label = "Fuente de datos"
+description = "Tipo de fuente de datos principal"
+
+[i18n.es.settings.analysis_type]
+label = "Tipo de análisis"
+description = "Enfoque de análisis predeterminado"
+
+[i18n.es.settings.output_format]
+label = "Formato de salida"
+description = "Cómo presentar los resultados del análisis"
+
+[i18n.es.settings.visualization]
+label = "Visualización"
+description = "Generar gráficos y visualizaciones"
+
+[i18n.es.settings.auto_schedule]
+label = "Informes programados"
+description = "Generar informes automáticamente según un calendario"
+
+[i18n.es.settings.report_frequency]
+label = "Frecuencia de informes"
+description = "Con qué frecuencia generar los informes programados"
+
+[i18n.es.settings.confidence_threshold]
+label = "Umbral de confianza"
+description = "Nivel mínimo de confianza para incluir hallazgos en los informes"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Analytique"
+description = "Agent autonome d'analyse de données — collecte, analyse, visualisation, tableaux de bord et rapports automatisés"
+category = "Données"
+
+[i18n.fr.settings.data_source]
+label = "Source de données"
+description = "Type de source de données principal"
+
+[i18n.fr.settings.analysis_type]
+label = "Type d'analyse"
+description = "Approche d'analyse par défaut"
+
+[i18n.fr.settings.output_format]
+label = "Format de sortie"
+description = "Mode de présentation des résultats d'analyse"
+
+[i18n.fr.settings.visualization]
+label = "Visualisation"
+description = "Générer des graphiques et des visualisations"
+
+[i18n.fr.settings.auto_schedule]
+label = "Rapports programmés"
+description = "Générer automatiquement des rapports selon un calendrier"
+
+[i18n.fr.settings.report_frequency]
+label = "Fréquence des rapports"
+description = "Fréquence de génération des rapports programmés"
+
+[i18n.fr.settings.confidence_threshold]
+label = "Seuil de confiance"
+description = "Niveau de confiance minimum pour inclure les résultats dans les rapports"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Analytik-Hand"
+description = "Autonomer Datenanalyse-Agent — Datenerfassung, Analyse, Visualisierung, Dashboards und automatisierte Berichte"
+category = "Daten"
+
+[i18n.de.settings.data_source]
+label = "Datenquelle"
+description = "Primärer Datenquellentyp"
+
+[i18n.de.settings.analysis_type]
+label = "Analysetyp"
+description = "Standard-Analyseansatz"
+
+[i18n.de.settings.output_format]
+label = "Ausgabeformat"
+description = "Darstellung der Analyseergebnisse"
+
+[i18n.de.settings.visualization]
+label = "Visualisierung"
+description = "Diagramme und Visualisierungen generieren"
+
+[i18n.de.settings.auto_schedule]
+label = "Geplante Berichte"
+description = "Berichte automatisch nach Zeitplan generieren"
+
+[i18n.de.settings.report_frequency]
+label = "Berichtshäufigkeit"
+description = "Häufigkeit der geplanten Berichtserstellung"
+
+[i18n.de.settings.confidence_threshold]
+label = "Konfidenzschwelle"
+description = "Mindest-Konfidenzniveau für die Aufnahme von Ergebnissen in Berichte"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "데이터 분석 Hand"
+description = "자율 데이터 분석 에이전트 — 데이터 수집, 분석, 시각화, 대시보드 및 자동화 보고서"
+category = "데이터"
+
+[i18n.ko.settings.data_source]
+label = "데이터 소스"
+description = "주요 데이터 소스 유형"
+
+[i18n.ko.settings.analysis_type]
+label = "분석 유형"
+description = "기본 분석 방법"
+
+[i18n.ko.settings.output_format]
+label = "출력 형식"
+description = "분석 결과 표시 방식"
+
+[i18n.ko.settings.visualization]
+label = "시각화"
+description = "차트 및 시각화 콘텐츠 생성"
+
+[i18n.ko.settings.auto_schedule]
+label = "정기 보고서"
+description = "일정에 따라 자동으로 보고서 생성"
+
+[i18n.ko.settings.report_frequency]
+label = "보고서 빈도"
+description = "정기 보고서 생성 주기"
+
+[i18n.ko.settings.confidence_threshold]
+label = "신뢰도 임계값"
+description = "보고서에 분석 결과를 포함하기 위한 최소 신뢰도 수준"
diff --git a/hands/analytics/SKILL.md b/hands/analytics/SKILL.md
index 16a404d..264b35f 100644
--- a/hands/analytics/SKILL.md
+++ b/hands/analytics/SKILL.md
@@ -337,3 +337,702 @@ Level 4: What to do (prescriptive)
 | Timeliness | Current | Data refreshed daily |
 | Uniqueness | 99% | 1% duplicate records found |
 ```
+
+---
+
+## Worked Examples
+
+### Example 1: E-commerce Sales Analysis
+
+**Goal**: Analyze 12 months of order data to identify revenue drivers, customer segments, and growth trends.
+
+#### Step 1 — Load and clean
+```python
+import pandas as pd
+import numpy as np
+
+df = pd.read_csv('orders.csv', parse_dates=['order_date'])
+
+# Quick audit
+print(f"Rows: {len(df):,}  Columns: {df.shape[1]}")
+print(df.isnull().sum()[df.isnull().sum() > 0])
+
+# Clean
+df = df.dropna(subset=['customer_id', 'order_total'])
+df['order_total'] = df['order_total'].clip(lower=0)  # Remove negative values
+df['order_month'] = df['order_date'].dt.to_period('M')
+```
+
+#### Step 2 — Revenue trend analysis
+```python
+monthly = (
+    df.groupby('order_month')
+    .agg(revenue=('order_total', 'sum'),
+         orders=('order_id', 'nunique'),
+         customers=('customer_id', 'nunique'))
+    .reset_index()
+)
+monthly['aov'] = monthly['revenue'] / monthly['orders']  # Average order value
+monthly['revenue_mom'] = monthly['revenue'].pct_change()  # Month-over-month growth
+
+fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)
+axes[0].bar(monthly['order_month'].astype(str), monthly['revenue'], color='steelblue')
+axes[0].set_title('Monthly Revenue', fontsize=14, fontweight='bold')
+axes[0].set_ylabel('Revenue ($)')
+
+axes[1].plot(monthly['order_month'].astype(str), monthly['aov'], marker='o', color='coral')
+axes[1].set_title('Average Order Value', fontsize=14, fontweight='bold')
+axes[1].set_ylabel('AOV ($)')
+plt.xticks(rotation=45, ha='right')
+plt.tight_layout()
+plt.savefig('revenue_trend.png', dpi=150, bbox_inches='tight')
+plt.close()
+```
+
+#### Step 3 — Customer segmentation (RFM)
+```python
+snapshot_date = df['order_date'].max() + pd.Timedelta(days=1)
+
+rfm = df.groupby('customer_id').agg(
+    recency=('order_date', lambda x: (snapshot_date - x.max()).days),
+    frequency=('order_id', 'nunique'),
+    monetary=('order_total', 'sum')
+)
+
+# Score each dimension 1-4 using quartiles
+for col in ['recency', 'frequency', 'monetary']:
+    labels = [4, 3, 2, 1] if col == 'recency' else [1, 2, 3, 4]
+    rfm[f'{col}_score'] = pd.qcut(rfm[col], q=4, labels=labels, duplicates='drop')
+
+rfm['rfm_score'] = (rfm['recency_score'].astype(int)
+                     + rfm['frequency_score'].astype(int)
+                     + rfm['monetary_score'].astype(int))
+
+# Segment mapping
+def segment(row):
+    r, f, m = int(row['recency_score']), int(row['frequency_score']), int(row['monetary_score'])
+    if r >= 3 and f >= 3:
+        return 'Champions'
+    elif r >= 3 and f < 3:
+        return 'New / Promising'
+    elif r < 3 and f >= 3:
+        return 'At Risk'
+    else:
+        return 'Needs Attention'
+
+rfm['segment'] = rfm.apply(segment, axis=1)
+print(rfm.groupby('segment').agg(
+    count=('monetary', 'size'),
+    avg_revenue=('monetary', 'mean'),
+    avg_frequency=('frequency', 'mean')
+).sort_values('avg_revenue', ascending=False))
+```
+
+#### Step 4 — Cohort retention analysis
+```python
+df['cohort'] = df.groupby('customer_id')['order_date'].transform('min').dt.to_period('M')
+df['order_period'] = df['order_date'].dt.to_period('M')
+df['cohort_index'] = (df['order_period'] - df['cohort']).apply(lambda x: x.n)
+
+cohort_table = (
+    df.groupby(['cohort', 'cohort_index'])['customer_id']
+    .nunique()
+    .reset_index()
+    .pivot(index='cohort', columns='cohort_index', values='customer_id')
+)
+
+# Convert to retention percentages
+retention = cohort_table.div(cohort_table[0], axis=0) * 100
+
+fig, ax = plt.subplots(figsize=(14, 8))
+sns.heatmap(retention, annot=True, fmt='.0f', cmap='YlOrRd_r', ax=ax)
+ax.set_title('Cohort Retention (% of original customers)', fontsize=14, fontweight='bold')
+ax.set_xlabel('Months Since First Purchase')
+ax.set_ylabel('Cohort')
+plt.tight_layout()
+plt.savefig('cohort_retention.png', dpi=150, bbox_inches='tight')
+plt.close()
+```
+
+---
+
+### Example 2: A/B Test Analysis
+
+**Goal**: Evaluate whether a new checkout flow (variant B) improves conversion rate over the existing flow (variant A).
+
+#### Step 1 — Sample size calculation (pre-test)
+```python
+from scipy import stats
+import numpy as np
+
+baseline_rate = 0.12      # Current conversion rate: 12%
+mde = 0.02                # Minimum detectable effect: 2 percentage points
+alpha = 0.05              # Significance level
+power = 0.80              # Statistical power
+
+# Using the normal approximation formula
+p1 = baseline_rate
+p2 = baseline_rate + mde
+p_avg = (p1 + p2) / 2
+
+z_alpha = stats.norm.ppf(1 - alpha / 2)  # Two-tailed
+z_beta = stats.norm.ppf(power)
+
+n_per_group = ((z_alpha * np.sqrt(2 * p_avg * (1 - p_avg))
+                + z_beta * np.sqrt(p1 * (1 - p1) + p2 * (1 - p2))) ** 2
+               / (p2 - p1) ** 2)
+
+print(f"Required sample size per group: {int(np.ceil(n_per_group)):,}")
+print(f"Total required: {int(np.ceil(n_per_group)) * 2:,}")
+```
+
+#### Step 2 — Run the test and collect results
+```python
+ab = pd.read_csv('ab_test_results.csv')
+
+summary = ab.groupby('variant').agg(
+    visitors=('user_id', 'nunique'),
+    conversions=('converted', 'sum')
+)
+summary['conversion_rate'] = summary['conversions'] / summary['visitors']
+print(summary)
+```
+
+#### Step 3 — Statistical significance
+```python
+a = ab[ab['variant'] == 'A']
+b = ab[ab['variant'] == 'B']
+
+# Chi-squared test for proportions
+contingency = pd.crosstab(ab['variant'], ab['converted'])
+chi2, p_value, dof, expected = stats.chi2_contingency(contingency)
+
+# Proportions z-test (more direct)
+from statsmodels.stats.proportion import proportions_ztest
+successes = [summary.loc['B', 'conversions'], summary.loc['A', 'conversions']]
+trials = [summary.loc['B', 'visitors'], summary.loc['A', 'visitors']]
+z_stat, p_val = proportions_ztest(successes, trials, alternative='larger')
+
+print(f"Z-statistic: {z_stat:.4f}")
+print(f"P-value:     {p_val:.4f}")
+print(f"Significant: {'Yes' if p_val < 0.05 else 'No'} (at alpha=0.05)")
+```
+
+#### Step 4 — Effect size and confidence interval
+```python
+p_a = summary.loc['A', 'conversion_rate']
+p_b = summary.loc['B', 'conversion_rate']
+n_a = summary.loc['A', 'visitors']
+n_b = summary.loc['B', 'visitors']
+
+lift = (p_b - p_a) / p_a
+se_diff = np.sqrt(p_a * (1 - p_a) / n_a + p_b * (1 - p_b) / n_b)
+ci_lower = (p_b - p_a) - 1.96 * se_diff
+ci_upper = (p_b - p_a) + 1.96 * se_diff
+
+print(f"Control rate:        {p_a:.4f}")
+print(f"Variant rate:        {p_b:.4f}")
+print(f"Absolute lift:       {p_b - p_a:+.4f}")
+print(f"Relative lift:       {lift:+.2%}")
+print(f"95% CI for diff:     [{ci_lower:+.4f}, {ci_upper:+.4f}]")
+```
+
+#### Step 5 — Recommendation template
+```
+## A/B Test Report: New Checkout Flow
+
+| Metric              | Control (A) | Variant (B) |
+|---------------------|-------------|-------------|
+| Visitors            | 15,204      | 15,198      |
+| Conversions         | 1,824       | 2,127       |
+| Conversion Rate     | 12.00%      | 13.99%      |
+
+**Result**: Statistically significant (p = 0.0003, alpha = 0.05)
+**Lift**: +1.99pp absolute / +16.6% relative
+**95% CI**: [+0.90pp, +3.08pp]
+**Recommendation**: Deploy variant B. The effect is both statistically
+and practically significant with a lower bound above the +1pp threshold.
+```
+
+---
+
+### Example 3: Customer Churn Analysis
+
+**Goal**: Identify which factors most strongly predict customer churn and quantify their relative importance.
+
+#### Step 1 — Feature engineering
+```python
+df = pd.read_csv('customers.csv')
+
+# Create behavioral features from raw data
+features = df.copy()
+features['tenure_months'] = (pd.Timestamp.now() - pd.to_datetime(df['signup_date'])).dt.days / 30
+features['support_tickets_per_month'] = df['total_tickets'] / features['tenure_months'].clip(lower=1)
+features['avg_session_minutes'] = df['total_session_minutes'] / df['total_sessions'].clip(lower=1)
+features['days_since_last_login'] = (pd.Timestamp.now() - pd.to_datetime(df['last_login'])).dt.days
+features['has_premium'] = (df['plan'] == 'premium').astype(int)
+
+# Drop raw columns, keep engineered features
+feature_cols = [
+    'tenure_months', 'support_tickets_per_month', 'avg_session_minutes',
+    'days_since_last_login', 'has_premium', 'monthly_spend', 'num_features_used'
+]
+```
+
+#### Step 2 — Correlation analysis
+```python
+churn_corr = features[feature_cols + ['churned']].corr()['churned'].drop('churned').sort_values()
+
+fig, ax = plt.subplots(figsize=(8, 5))
+churn_corr.plot(kind='barh', ax=ax, color=['coral' if x > 0 else 'steelblue' for x in churn_corr])
+ax.set_title('Feature Correlation with Churn', fontsize=14, fontweight='bold')
+ax.set_xlabel('Pearson Correlation')
+ax.axvline(x=0, color='black', linewidth=0.5)
+plt.tight_layout()
+plt.savefig('churn_correlations.png', dpi=150, bbox_inches='tight')
+plt.close()
+```
+
+#### Step 3 — Key driver identification via group comparison
+```python
+churned = features[features['churned'] == 1]
+retained = features[features['churned'] == 0]
+
+comparison = []
+for col in feature_cols:
+    t_stat, p_val = stats.ttest_ind(churned[col].dropna(), retained[col].dropna())
+    d = cohens_d(churned[col].dropna(), retained[col].dropna())  # From earlier definition
+    comparison.append({
+        'feature': col,
+        'churned_mean': churned[col].mean(),
+        'retained_mean': retained[col].mean(),
+        'diff_pct': (churned[col].mean() - retained[col].mean()) / retained[col].mean() * 100,
+        'cohens_d': abs(d),
+        'p_value': p_val,
+        'significant': p_val < 0.05
+    })
+
+result = pd.DataFrame(comparison).sort_values('cohens_d', ascending=False)
+print(result.to_string(index=False))
+```
+
+#### Step 4 — Interpret and report
+```
+## Churn Driver Analysis
+
+**Top 3 factors distinguishing churned vs. retained customers:**
+
+| Factor                     | Churned (avg) | Retained (avg) | Diff     | Effect Size |
+|----------------------------|---------------|----------------|----------|-------------|
+| Days since last login      | 34.2          | 8.7            | +293%    | Large       |
+| Support tickets per month  | 2.8           | 0.9            | +211%    | Large       |
+| Number of features used    | 3.1           | 7.4            | -58%     | Medium      |
+
+**Actionable insights:**
+1. Customers inactive >14 days are 4x more likely to churn -- trigger re-engagement email at day 10
+2. High support ticket rate signals frustration -- escalate accounts with >2 tickets/month to success team
+3. Low feature adoption correlates with churn -- implement onboarding flow targeting unused features
+```
+
+---
+
+## Advanced pandas Patterns
+
+### Window Functions
+
+```python
+# Expanding window (cumulative statistics)
+df['cumulative_avg'] = df['value'].expanding().mean()
+df['cumulative_max'] = df['value'].expanding().max()
+
+# Exponentially weighted moving average (EWMA) -- emphasizes recent values
+df['ewma_7'] = df['value'].ewm(span=7).mean()     # Span-based decay
+df['ewma_a'] = df['value'].ewm(alpha=0.3).mean()   # Explicit decay factor
+
+# Comparison: rolling vs. EWMA
+# - rolling(7).mean() weights all 7 values equally
+# - ewm(span=7).mean() weights recent values exponentially more
+# Use EWMA when recent data matters more (stock prices, real-time metrics)
+
+# Rolling with min_periods (handles early rows with insufficient data)
+df['rolling_avg'] = df['value'].rolling(window=30, min_periods=5).mean()
+
+# Rolling rank (percentile within window)
+df['rolling_pctile'] = df['value'].rolling(90).rank(pct=True)
+```
+
+### Multi-Index Operations
+
+```python
+# Create multi-index from groupby
+multi = df.groupby(['region', 'product']).agg(
+    revenue=('amount', 'sum'),
+    units=('quantity', 'sum')
+)
+
+# Access levels
+multi.loc['North']                        # All products in North region
+multi.loc[('North', 'Widget')]            # Specific region + product
+multi.xs('Widget', level='product')       # All regions for Widget
+
+# Swap and sort levels
+multi = multi.swaplevel().sort_index()
+
+# Reset to flat columns
+flat = multi.reset_index()
+
+# Stack / unstack (reshape between long and wide)
+wide = multi['revenue'].unstack(level='product')    # Products become columns
+long = wide.stack()                                  # Back to multi-index
+```
+
+### Merge and Join Patterns
+
+```python
+# Inner join (only matching rows)
+merged = orders.merge(customers, on='customer_id', how='inner')
+
+# Left join with indicator (see which rows matched)
+merged = orders.merge(customers, on='customer_id', how='left', indicator=True)
+unmatched = merged[merged['_merge'] == 'left_only']
+
+# Join on multiple keys
+merged = df1.merge(df2, on=['date', 'region'], how='left')
+
+# Join with different column names
+merged = orders.merge(products, left_on='prod_id', right_on='product_id')
+
+# Anti-join (rows in A that have no match in B)
+anti = df_a.merge(df_b, on='key', how='left', indicator=True)
+anti = anti[anti['_merge'] == 'left_only'].drop(columns='_merge')
+
+# Self-join (compare rows within same table)
+df_prev = df[['customer_id', 'order_date', 'amount']].rename(
+    columns={'order_date': 'prev_date', 'amount': 'prev_amount'}
+)
+df_with_prev = df.merge(df_prev, on='customer_id', how='left')
+df_with_prev = df_with_prev[df_with_prev['prev_date'] < df_with_prev['order_date']]
+```
+
+### Apply and Transform
+
+```python
+# transform() returns same-shaped output -- useful for group-level stats on each row
+df['group_mean'] = df.groupby('category')['value'].transform('mean')
+df['pct_of_group'] = df['value'] / df.groupby('category')['value'].transform('sum')
+df['z_within_group'] = df.groupby('category')['value'].transform(
+    lambda x: (x - x.mean()) / x.std()
+)
+
+# apply() for multi-column group operations
+def top_n(group, n=3):
+    return group.nlargest(n, 'value')
+
+top3_per_category = df.groupby('category', group_keys=False).apply(top_n, n=3)
+
+# Vectorized operations (prefer these over apply when possible)
+# Slow:
+df['result'] = df.apply(lambda row: row['a'] * row['b'] + row['c'], axis=1)
+# Fast:
+df['result'] = df['a'] * df['b'] + df['c']
+
+# np.where for conditional columns (vectorized if/else)
+df['tier'] = np.where(df['revenue'] > 10000, 'high', 'low')
+
+# np.select for multiple conditions
+conditions = [
+    df['revenue'] > 10000,
+    df['revenue'] > 5000,
+    df['revenue'] > 0,
+]
+choices = ['high', 'medium', 'low']
+df['tier'] = np.select(conditions, choices, default='none')
+```
+
+### Memory Optimization for Large Datasets
+
+```python
+# Check current memory usage
+print(df.memory_usage(deep=True).sum() / 1024**2, "MB")
+
+# Downcast numeric types
+df['int_col'] = pd.to_numeric(df['int_col'], downcast='integer')    # int64 -> int8/16/32
+df['float_col'] = pd.to_numeric(df['float_col'], downcast='float')  # float64 -> float32
+
+# Use category type for low-cardinality strings
+for col in df.select_dtypes(include='object'):
+    if df[col].nunique() / len(df) < 0.5:  # Less than 50% unique values
+        df[col] = df[col].astype('category')
+
+# Read in chunks for files that exceed memory
+chunks = pd.read_csv('huge_file.csv', chunksize=100_000)
+results = []
+for chunk in chunks:
+    processed = chunk.groupby('category')['value'].sum()
+    results.append(processed)
+final = pd.concat(results).groupby(level=0).sum()
+
+# Specify dtypes at load time (avoids loading as float64/object first)
+dtypes = {
+    'id': 'int32',
+    'category': 'category',
+    'value': 'float32',
+    'flag': 'bool'
+}
+df = pd.read_csv('data.csv', dtype=dtypes)
+
+# Use pyarrow backend for better memory efficiency (pandas 2.0+)
+df = pd.read_csv('data.csv', engine='pyarrow', dtype_backend='pyarrow')
+```
+
+---
+
+## Dashboard and Reporting Patterns
+
+### Executive Dashboard Template
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from matplotlib.patches import FancyBboxPatch
+
+def executive_dashboard(kpis, trend_df, comparison_df, output='dashboard.png'):
+    """
+    kpis: dict with keys like {'Revenue': '$1.2M', 'Growth': '+15%', ...}
+    trend_df: DataFrame with 'date' and 'value' columns
+    comparison_df: DataFrame with 'category' and 'current'/'previous' columns
+    """
+    fig = plt.figure(figsize=(16, 10))
+    gs = gridspec.GridSpec(3, len(kpis), hspace=0.4, wspace=0.3)
+
+    # Row 1: KPI cards
+    for i, (label, value) in enumerate(kpis.items()):
+        ax = fig.add_subplot(gs[0, i])
+        ax.text(0.5, 0.6, value, ha='center', va='center',
+                fontsize=28, fontweight='bold', color='#2c3e50')
+        ax.text(0.5, 0.2, label, ha='center', va='center',
+                fontsize=12, color='#7f8c8d')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.axis('off')
+        # Card background
+        rect = FancyBboxPatch((0.05, 0.05), 0.9, 0.9, boxstyle="round,pad=0.05",
+                              facecolor='#f8f9fa', edgecolor='#dee2e6')
+        ax.add_patch(rect)
+
+    # Row 2: Trend line
+    ax_trend = fig.add_subplot(gs[1, :])
+    ax_trend.plot(trend_df['date'], trend_df['value'], linewidth=2, color='steelblue')
+    ax_trend.fill_between(trend_df['date'], trend_df['value'], alpha=0.1, color='steelblue')
+    ax_trend.set_title('Trend Over Time', fontsize=13, fontweight='bold')
+    ax_trend.set_ylabel('Value')
+
+    # Row 3: Period comparison (grouped bar)
+    ax_comp = fig.add_subplot(gs[2, :])
+    x = range(len(comparison_df))
+    width = 0.35
+    ax_comp.bar([i - width/2 for i in x], comparison_df['previous'], width,
+                label='Previous', color='#bdc3c7')
+    ax_comp.bar([i + width/2 for i in x], comparison_df['current'], width,
+                label='Current', color='steelblue')
+    ax_comp.set_xticks(list(x))
+    ax_comp.set_xticklabels(comparison_df['category'], rotation=45, ha='right')
+    ax_comp.set_title('Current vs. Previous Period', fontsize=13, fontweight='bold')
+    ax_comp.legend()
+
+    plt.savefig(output, dpi=150, bbox_inches='tight', facecolor='white')
+    plt.close()
+```
+
+### Weekly Metrics Report Template
+
+```python
+def weekly_report(df, date_col='date', metric_col='value', group_col=None):
+    """Generate a standard weekly metrics summary."""
+    df[date_col] = pd.to_datetime(df[date_col])
+    df['week'] = df[date_col].dt.isocalendar().week.astype(int)
+    df['year'] = df[date_col].dt.year
+
+    current_week = df['week'].max()
+    prev_week = current_week - 1
+
+    curr = df[df['week'] == current_week]
+    prev = df[df['week'] == prev_week]
+
+    report = {
+        'period': f"Week {current_week}",
+        'total': curr[metric_col].sum(),
+        'mean': curr[metric_col].mean(),
+        'median': curr[metric_col].median(),
+        'wow_change': (curr[metric_col].sum() - prev[metric_col].sum())
+                      / prev[metric_col].sum() * 100
+                      if prev[metric_col].sum() != 0 else None,
+    }
+
+    if group_col:
+        report['by_group'] = curr.groupby(group_col)[metric_col].agg(['sum', 'mean', 'count'])
+
+    # Sparkline trend (last 8 weeks)
+    weekly_totals = (
+        df.groupby('week')[metric_col].sum()
+        .tail(8)
+        .reset_index()
+    )
+
+    fig, ax = plt.subplots(figsize=(6, 2))
+    ax.plot(weekly_totals['week'], weekly_totals[metric_col], marker='o',
+            linewidth=2, color='steelblue', markersize=4)
+    ax.fill_between(weekly_totals['week'], weekly_totals[metric_col],
+                    alpha=0.1, color='steelblue')
+    ax.set_title(f'{metric_col.title()} — Last 8 Weeks', fontsize=10)
+    ax.tick_params(labelsize=8)
+    plt.tight_layout()
+    plt.savefig('weekly_sparkline.png', dpi=150, bbox_inches='tight')
+    plt.close()
+
+    return report
+```
+
+### Anomaly Detection Patterns
+
+```python
+def detect_anomalies(series, method='zscore', threshold=3.0, window=30):
+    """
+    Detect anomalies in a numeric series.
+
+    Methods:
+    - 'zscore':  Flag values beyond `threshold` standard deviations from mean
+    - 'iqr':     Flag values beyond 1.5x IQR from quartiles
+    - 'rolling': Flag values beyond `threshold` std devs from rolling mean
+    """
+    anomalies = pd.Series(False, index=series.index)
+
+    if method == 'zscore':
+        z = (series - series.mean()) / series.std()
+        anomalies = z.abs() > threshold
+
+    elif method == 'iqr':
+        q1 = series.quantile(0.25)
+        q3 = series.quantile(0.75)
+        iqr = q3 - q1
+        anomalies = (series < q1 - 1.5 * iqr) | (series > q3 + 1.5 * iqr)
+
+    elif method == 'rolling':
+        rolling_mean = series.rolling(window, min_periods=5).mean()
+        rolling_std = series.rolling(window, min_periods=5).std()
+        anomalies = (series - rolling_mean).abs() > threshold * rolling_std
+
+    return anomalies
+
+
+# Usage: detect and visualize
+anomalies = detect_anomalies(df['metric'], method='rolling', threshold=2.5, window=30)
+
+fig, ax = plt.subplots(figsize=(14, 5))
+ax.plot(df.index, df['metric'], linewidth=1, color='steelblue', label='Metric')
+ax.scatter(df.index[anomalies], df['metric'][anomalies],
+           color='red', s=40, zorder=5, label='Anomaly')
+ax.legend()
+ax.set_title('Anomaly Detection (Rolling Z-Score)', fontsize=14, fontweight='bold')
+plt.tight_layout()
+plt.savefig('anomalies.png', dpi=150, bbox_inches='tight')
+plt.close()
+
+print(f"Detected {anomalies.sum()} anomalies out of {len(series):,} data points")
+```
+
+**Method selection guide:**
+
+| Method | Best For | Assumptions | Sensitivity |
+|--------|----------|-------------|-------------|
+| Z-score | Stationary data with normal distribution | Constant mean and variance | Low (misses local anomalies) |
+| IQR | Skewed distributions, outlier screening | None (non-parametric) | Medium |
+| Rolling z-score | Time series with trends or seasonality | Local stationarity within window | High (adapts to drift) |
+
+---
+
+## Common Analytics Pitfalls
+
+### Simpson's Paradox
+
+A trend that appears in grouped data reverses when the groups are combined.
+
+```
+Department A: Drug works better     (80% vs 70%)
+Department B: Drug works better     (50% vs 40%)
+Combined:     Drug appears WORSE    (55% vs 60%)  <-- paradox
+```
+
+**Why it happens**: Unequal group sizes create a confounding effect. Department B (with lower overall rates) sent most patients to the drug group.
+
+**Prevention**: Always segment data by relevant confounders before drawing conclusions. If aggregate and segmented results disagree, trust the segmented analysis and report the confounding variable.
+
+### Survivorship Bias
+
+Analyzing only entities that "survived" a selection process, ignoring those that dropped out.
+
+**Classic examples:**
+- Studying only successful companies to find success patterns (ignoring failed companies with the same patterns)
+- Analyzing only current customers to understand satisfaction (ignoring those who already left)
+- Looking at fund performance by examining only funds that still exist (dead funds were closed)
+
+**Prevention**: Always ask "what is missing from this dataset?" before drawing conclusions. If possible, include data from non-survivors. Explicitly note the selection criteria and what it excludes.
+
+### Correlation vs. Causation
+
+A statistically significant correlation between X and Y does not mean X causes Y. Possible explanations:
+
+| Explanation | Example |
+|-------------|---------|
+| X causes Y | Exercise reduces blood pressure |
+| Y causes X | Depression reduces exercise (not exercise causes depression) |
+| Z causes both | Income drives both education spending AND health outcomes |
+| Coincidence | Ice cream sales correlate with drowning deaths (both driven by summer) |
+
+**Prevention**: Establish causation only with randomized controlled experiments (A/B tests). For observational data, state findings as "associated with" not "causes." Look for confounders and test whether the relationship holds when controlling for them.
+
+### Cherry-Picking Time Windows
+
+Selecting a start/end date that makes a metric look better or worse than the true trend.
+
+```python
+# Example: same data, different conclusions
+# "Revenue up 40%!"    -- comparing Jan (seasonal low) to Dec (seasonal high)
+# "Revenue flat."       -- comparing Dec 2024 to Dec 2025 (year-over-year)
+
+# Prevention: always use year-over-year comparison for seasonal data
+df['yoy_change'] = df.groupby(df['date'].dt.month)['revenue'].pct_change(periods=12)
+```
+
+**Prevention checklist:**
+- Compare like-for-like periods (YoY for seasonal businesses)
+- Show the full time range, not a selected subset
+- Use multiple time windows (WoW, MoM, QoQ, YoY) and note if they disagree
+- Include a moving average to show the underlying trend separate from noise
+
+### Small Sample Size Issues
+
+Small samples produce unstable statistics that can flip with just a few more observations.
+
+```python
+# Illustrate instability: conversion rates with small vs. large samples
+from scipy.stats import beta
+
+# Scenario: 3 conversions out of 10 visitors (30%)
+a_small, b_small = 3 + 1, 10 - 3 + 1   # Beta posterior
+ci_small = beta.interval(0.95, a_small, b_small)
+print(f"n=10:   30% conversion, 95% CI: [{ci_small[0]:.1%}, {ci_small[1]:.1%}]")
+# Output: 95% CI: [9.9%, 56.8%]  -- extremely wide, almost useless
+
+# Scenario: 300 conversions out of 1000 visitors (30%)
+a_large, b_large = 300 + 1, 1000 - 300 + 1
+ci_large = beta.interval(0.95, a_large, b_large)
+print(f"n=1000: 30% conversion, 95% CI: [{ci_large[0]:.1%}, {ci_large[1]:.1%}]")
+# Output: 95% CI: [27.2%, 32.9%]  -- narrow and actionable
+```
+
+**Rules of thumb:**
+- n < 30: Do not draw firm conclusions. Report as directional only.
+- Conversion rates need hundreds (not dozens) of conversions to stabilize.
+- Always report confidence intervals alongside point estimates.
+- If sample size is fixed and small, use exact tests (Fisher's exact) rather than approximations (chi-squared).
diff --git a/hands/apitester/HAND.toml b/hands/apitester/HAND.toml
index f295e5d..f5b99b1 100644
--- a/hands/apitester/HAND.toml
+++ b/hands/apitester/HAND.toml
@@ -460,6 +460,265 @@ token_consumption = "medium"
 default_active = false
 activation_warning = "API Tester hand runs continuously, consuming tokens. Use on-demand for specific tests."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "API 测试 Hand"
 description = "自主 API 测试智能体——端点发现、请求验证、负载测试和回归检测"
+category = "开发"
+
+[i18n.zh.settings.base_url]
+label = "基础 URL"
+description = "待测试 API 的基础 URL（例如 https://api.example.com/v1）"
+
+[i18n.zh.settings.auth_type]
+label = "认证方式"
+description = "API 请求的认证方式"
+
+[i18n.zh.settings.auth_token]
+label = "认证令牌 / API 密钥"
+description = "Bearer 令牌、API 密钥或 Base64 编码的凭据，取决于认证方式"
+
+[i18n.zh.settings.test_mode]
+label = "测试模式"
+description = "执行的 API 测试类型"
+
+[i18n.zh.settings.openapi_spec_url]
+label = "OpenAPI 规范 URL"
+description = "OpenAPI/Swagger 规范的 URL（例如 /openapi.json）。留空则自动发现。"
+
+[i18n.zh.settings.auto_schedule]
+label = "自动定时"
+description = "按计划自动运行测试"
+
+[i18n.zh.settings.test_frequency]
+label = "测试频率"
+description = "定时测试的执行频率"
+
+[i18n.zh.settings.fail_on_error]
+label = "严格模式"
+description = "将任何非 2xx 响应视为失败（而非允许预期的错误码）"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "将测试计划和破坏性请求写入队列文件供审核，而非直接执行"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "APIテスト Hand"
+description = "自律型APIテストエージェント——エンドポイント検出、リクエスト検証、負荷テスト、リグレッション検出"
+category = "開発"
+
+[i18n.ja.settings.base_url]
+label = "ベースURL"
+description = "テスト対象APIのベースURL（例: https://api.example.com/v1）"
+
+[i18n.ja.settings.auth_type]
+label = "認証方式"
+description = "APIリクエストの認証方法"
+
+[i18n.ja.settings.auth_token]
+label = "認証トークン / APIキー"
+description = "認証方式に応じたBearerトークン、APIキー、またはBase64エンコードされた資格情報"
+
+[i18n.ja.settings.test_mode]
+label = "テストモード"
+description = "実行するAPIテストの種類"
+
+[i18n.ja.settings.openapi_spec_url]
+label = "OpenAPI仕様URL"
+description = "OpenAPI/Swagger仕様のURL（例: /openapi.json）。空欄にすると自動検出します。"
+
+[i18n.ja.settings.auto_schedule]
+label = "自動スケジュール"
+description = "スケジュールに基づいてテストを自動実行する"
+
+[i18n.ja.settings.test_frequency]
+label = "テスト頻度"
+description = "定期テストの実行頻度"
+
+[i18n.ja.settings.fail_on_error]
+label = "厳格モード"
+description = "2xx以外のレスポンスをすべて失敗として扱う（期待されるエラーコードを許容しない）"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "テスト計画や破壊的リクエストを直接実行せず、レビュー用のキューファイルに書き出す"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Pruebas API"
+description = "Agente autónomo de pruebas de API — descubrimiento de endpoints, validación de peticiones, pruebas de carga y detección de regresiones"
+category = "Desarrollo"
+
+[i18n.es.settings.base_url]
+label = "URL base"
+description = "URL base de la API a probar (ej. https://api.example.com/v1)"
+
+[i18n.es.settings.auth_type]
+label = "Tipo de autenticación"
+description = "Cómo autenticar las peticiones a la API"
+
+[i18n.es.settings.auth_token]
+label = "Token de autenticación / Clave API"
+description = "Token Bearer, clave API o credenciales codificadas en Base64 según el tipo de autenticación"
+
+[i18n.es.settings.test_mode]
+label = "Modo de prueba"
+description = "Qué tipo de pruebas de API realizar"
+
+[i18n.es.settings.openapi_spec_url]
+label = "URL de especificación OpenAPI"
+description = "URL de la especificación OpenAPI/Swagger (ej. /openapi.json). Dejar vacío para descubrimiento automático."
+
+[i18n.es.settings.auto_schedule]
+label = "Programación automática"
+description = "Ejecutar pruebas automáticamente según un calendario"
+
+[i18n.es.settings.test_frequency]
+label = "Frecuencia de pruebas"
+description = "Con qué frecuencia ejecutar las pruebas programadas"
+
+[i18n.es.settings.fail_on_error]
+label = "Modo estricto"
+description = "Tratar cualquier respuesta no 2xx como un fallo (en lugar de permitir códigos de error esperados)"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Escribir planes de prueba y peticiones destructivas en un archivo de cola para revisión en lugar de ejecutarlos directamente"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand de Test API"
+description = "Agent autonome de test d'API — découverte de points de terminaison, validation de requêtes, tests de charge et détection de régression"
+category = "Développement"
+
+[i18n.fr.settings.base_url]
+label = "URL de base"
+description = "URL de base de l'API à tester (ex. https://api.example.com/v1)"
+
+[i18n.fr.settings.auth_type]
+label = "Type d'authentification"
+description = "Méthode d'authentification des requêtes API"
+
+[i18n.fr.settings.auth_token]
+label = "Jeton d'authentification / Clé API"
+description = "Jeton Bearer, clé API ou identifiants encodés en Base64 selon le type d'authentification"
+
+[i18n.fr.settings.test_mode]
+label = "Mode de test"
+description = "Type de tests API à exécuter"
+
+[i18n.fr.settings.openapi_spec_url]
+label = "URL de spécification OpenAPI"
+description = "URL de la spécification OpenAPI/Swagger (ex. /openapi.json). Laisser vide pour la découverte automatique."
+
+[i18n.fr.settings.auto_schedule]
+label = "Planification automatique"
+description = "Exécuter automatiquement les tests selon un calendrier"
+
+[i18n.fr.settings.test_frequency]
+label = "Fréquence des tests"
+description = "Fréquence d'exécution des tests planifiés"
+
+[i18n.fr.settings.fail_on_error]
+label = "Mode strict"
+description = "Traiter toute réponse non 2xx comme un échec (au lieu d'autoriser les codes d'erreur attendus)"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Écrire les plans de test et requêtes destructives dans un fichier d'attente pour révision au lieu de les exécuter directement"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "API-Test-Hand"
+description = "Autonomer API-Test-Agent — Endpunkt-Erkennung, Anfrage-Validierung, Lasttests und Regressionserkennung"
+category = "Entwicklung"
+
+[i18n.de.settings.base_url]
+label = "Basis-URL"
+description = "Basis-URL der zu testenden API (z.B. https://api.example.com/v1)"
+
+[i18n.de.settings.auth_type]
+label = "Authentifizierungstyp"
+description = "Authentifizierungsmethode für API-Anfragen"
+
+[i18n.de.settings.auth_token]
+label = "Authentifizierungstoken / API-Schlüssel"
+description = "Bearer-Token, API-Schlüssel oder Base64-kodierte Anmeldedaten je nach Authentifizierungstyp"
+
+[i18n.de.settings.test_mode]
+label = "Testmodus"
+description = "Art der durchzuführenden API-Tests"
+
+[i18n.de.settings.openapi_spec_url]
+label = "OpenAPI-Spezifikations-URL"
+description = "URL der OpenAPI/Swagger-Spezifikation (z.B. /openapi.json). Leer lassen für automatische Erkennung."
+
+[i18n.de.settings.auto_schedule]
+label = "Automatische Planung"
+description = "Tests automatisch nach Zeitplan ausführen"
+
+[i18n.de.settings.test_frequency]
+label = "Testhäufigkeit"
+description = "Ausführungshäufigkeit der geplanten Tests"
+
+[i18n.de.settings.fail_on_error]
+label = "Strikter Modus"
+description = "Jede Nicht-2xx-Antwort als Fehler behandeln (anstatt erwartete Fehlercodes zuzulassen)"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Testpläne und destruktive Anfragen in eine Warteschlange zur Überprüfung schreiben, anstatt sie direkt auszuführen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "API 테스트 Hand"
+description = "자율 API 테스트 에이전트 — 엔드포인트 탐색, 요청 검증, 부하 테스트 및 회귀 감지"
+category = "개발"
+
+[i18n.ko.settings.base_url]
+label = "기본 URL"
+description = "테스트할 API의 기본 URL (예: https://api.example.com/v1)"
+
+[i18n.ko.settings.auth_type]
+label = "인증 방식"
+description = "API 요청의 인증 방식"
+
+[i18n.ko.settings.auth_token]
+label = "인증 토큰 / API 키"
+description = "인증 방식에 따른 Bearer 토큰, API 키 또는 Base64 인코딩 자격 증명"
+
+[i18n.ko.settings.test_mode]
+label = "테스트 모드"
+description = "수행할 API 테스트 유형"
+
+[i18n.ko.settings.openapi_spec_url]
+label = "OpenAPI 스펙 URL"
+description = "OpenAPI/Swagger 스펙의 URL (예: /openapi.json). 비워두면 자동 탐색합니다."
+
+[i18n.ko.settings.auto_schedule]
+label = "자동 일정"
+description = "일정에 따라 자동으로 테스트 실행"
+
+[i18n.ko.settings.test_frequency]
+label = "테스트 빈도"
+description = "정기 테스트 실행 주기"
+
+[i18n.ko.settings.fail_on_error]
+label = "엄격 모드"
+description = "모든 비-2xx 응답을 실패로 처리 (예상된 오류 코드 허용 안 함)"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "테스트 계획 및 파괴적 요청을 직접 실행하지 않고 큐 파일에 기록하여 검토"
diff --git a/hands/apitester/SKILL.md b/hands/apitester/SKILL.md
index 58fc634..ca1c16f 100644
--- a/hands/apitester/SKILL.md
+++ b/hands/apitester/SKILL.md
@@ -237,3 +237,656 @@ First Seen:     2025-01-15 run
 Previous Value: string (email format)
 Current Value:  field absent
 ```
+
+---
+
+## Worked Examples
+
+### Example 1: Testing a REST API CRUD Endpoint
+
+Full test suite for a `/api/users` resource covering create, read, update, delete, and edge cases.
+
+**Setup — Create a test user**:
+```bash
+# POST /api/users — create
+RESPONSE=$(curl -s -w "\n%{http_code}" -X POST \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $TOKEN" \
+  -d '{"name": "Ada Lovelace", "email": "ada@example.com", "role": "engineer"}' \
+  "https://api.example.com/api/users")
+
+BODY=$(echo "$RESPONSE" | sed '$d')
+STATUS=$(echo "$RESPONSE" | tail -1)
+
+# Expect 201 Created
+[ "$STATUS" = "201" ] && echo "PASS: Create user" || echo "FAIL: Expected 201, got $STATUS"
+
+# Extract ID for subsequent tests
+USER_ID=$(echo "$BODY" | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])")
+```
+
+**Read operations**:
+```bash
+# GET /api/users — list all
+curl -s -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users" | python3 -m json.tool
+
+# GET /api/users/:id — single user
+curl -s -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users/$USER_ID" | python3 -m json.tool
+
+# GET /api/users/nonexistent-id — expect 404
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users/00000000-0000-0000-0000-000000000000")
+[ "$STATUS" = "404" ] && echo "PASS: 404 for missing user" || echo "FAIL: Expected 404, got $STATUS"
+```
+
+**Update operations**:
+```bash
+# PUT /api/users/:id — full update
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X PUT \
+  -H "Content-Type: application/json" -H "Authorization: Bearer $TOKEN" \
+  -d '{"name": "Ada Lovelace", "email": "ada.updated@example.com", "role": "lead"}' \
+  "https://api.example.com/api/users/$USER_ID")
+[ "$STATUS" = "200" ] && echo "PASS: Full update" || echo "FAIL: Expected 200, got $STATUS"
+
+# PATCH — partial update (expect 200); also test invalid data (expect 400/422)
+```
+
+**Delete and verify**:
+```bash
+# DELETE /api/users/:id
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X DELETE \
+  -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users/$USER_ID")
+[ "$STATUS" = "204" ] || [ "$STATUS" = "200" ] && echo "PASS: Delete user" || echo "FAIL: Expected 2xx, got $STATUS"
+
+# GET deleted user — expect 404 or 410
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users/$USER_ID")
+[ "$STATUS" = "404" ] || [ "$STATUS" = "410" ] && echo "PASS: Deleted user gone" || echo "FAIL: Expected 404/410, got $STATUS"
+
+# DELETE again — idempotency check
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X DELETE \
+  -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users/$USER_ID")
+[ "$STATUS" = "404" ] || [ "$STATUS" = "204" ] && echo "PASS: Idempotent delete" || echo "FAIL: Got $STATUS"
+```
+
+**Edge cases to test**: duplicate create (expect 409), empty body (expect 400/422), extra unknown fields (verify ignored or rejected, not persisted).
+
+### Example 2: Testing an Authenticated API with Rate Limiting
+
+Scenario: API uses Bearer tokens, tokens expire after 1 hour, rate limit is 100 requests/minute.
+
+**Token lifecycle testing**:
+```bash
+# Step 1: Obtain token
+AUTH_RESPONSE=$(curl -s -X POST \
+  -H "Content-Type: application/json" \
+  -d '{"client_id": "myapp", "client_secret": "secret", "grant_type": "client_credentials"}' \
+  "https://api.example.com/oauth/token")
+
+ACCESS_TOKEN=$(echo "$AUTH_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")
+EXPIRES_IN=$(echo "$AUTH_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['expires_in'])")
+echo "Token obtained, expires in ${EXPIRES_IN}s"
+
+# Step 2: Use token — expect 200
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "Authorization: Bearer $ACCESS_TOKEN" \
+  "https://api.example.com/api/protected")
+[ "$STATUS" = "200" ] && echo "PASS: Valid token accepted" || echo "FAIL: Got $STATUS"
+
+# Step 3: Use expired/invalid token — expect 401
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "Authorization: Bearer expired.token.here" \
+  "https://api.example.com/api/protected")
+[ "$STATUS" = "401" ] && echo "PASS: Expired token rejected" || echo "FAIL: Got $STATUS"
+
+# Step 4: Missing Authorization header — expect 401
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  "https://api.example.com/api/protected")
+[ "$STATUS" = "401" ] && echo "PASS: No auth rejected" || echo "FAIL: Got $STATUS"
+
+# Step 5: Malformed header — expect 401
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "Authorization: NotBearer $ACCESS_TOKEN" \
+  "https://api.example.com/api/protected")
+[ "$STATUS" = "401" ] && echo "PASS: Bad scheme rejected" || echo "FAIL: Got $STATUS"
+```
+
+**Rate limit testing**:
+```bash
+# Hit the endpoint rapidly and watch for 429
+RESULTS_FILE=$(mktemp)
+for i in $(seq 1 120); do
+  curl -s -o /dev/null -w "%{http_code}\n" \
+    -H "Authorization: Bearer $ACCESS_TOKEN" \
+    "https://api.example.com/api/data" >> "$RESULTS_FILE" &
+done
+wait
+
+# Count status codes
+echo "=== Rate Limit Results ==="
+sort "$RESULTS_FILE" | uniq -c | sort -rn
+# Expected: ~100 x 200, ~20 x 429
+
+# Check rate limit headers on a single request
+curl -s -D- -o /dev/null \
+  -H "Authorization: Bearer $ACCESS_TOKEN" \
+  "https://api.example.com/api/data" | grep -i "x-ratelimit"
+# Expected headers:
+#   X-RateLimit-Limit: 100
+#   X-RateLimit-Remaining: 99
+#   X-RateLimit-Reset: 1700000060
+
+rm "$RESULTS_FILE"
+```
+
+**Backoff strategy**: On 429, respect `Retry-After` header. Use exponential backoff (1s, 2s, 4s...) as fallback. Verify the API returns `X-RateLimit-Reset` for client scheduling.
+
+### Example 3: Testing a Webhook Endpoint
+
+Scenario: Your API accepts webhook callbacks at `POST /webhooks/payment` with HMAC-SHA256 signature verification.
+
+**Payload and signature generation**:
+```bash
+WEBHOOK_SECRET="whsec_test_secret_key_12345"
+PAYLOAD='{"event":"payment.completed","data":{"id":"pay_123","amount":4999,"currency":"usd"}}'
+TIMESTAMP=$(date +%s)
+SIGNATURE=$(printf "%s.%s" "$TIMESTAMP" "$PAYLOAD" | openssl dgst -sha256 -hmac "$WEBHOOK_SECRET" | awk '{print $2}')
+
+# Valid webhook delivery
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Content-Type: application/json" \
+  -H "X-Webhook-Signature: t=$TIMESTAMP,v1=$SIGNATURE" \
+  -H "X-Webhook-Id: wh_evt_001" \
+  -d "$PAYLOAD" \
+  "https://api.example.com/webhooks/payment")
+[ "$STATUS" = "200" ] || [ "$STATUS" = "204" ] && echo "PASS: Valid webhook accepted" || echo "FAIL: Got $STATUS"
+```
+
+**Signature verification tests**:
+```bash
+# Wrong signature — expect 401 or 403
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Content-Type: application/json" \
+  -H "X-Webhook-Signature: t=$TIMESTAMP,v1=badsignaturevalue" \
+  -d "$PAYLOAD" \
+  "https://api.example.com/webhooks/payment")
+[ "$STATUS" = "401" ] || [ "$STATUS" = "403" ] && echo "PASS: Bad signature rejected" || echo "FAIL: Got $STATUS"
+
+# Missing signature header — expect 401
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Content-Type: application/json" \
+  -d "$PAYLOAD" \
+  "https://api.example.com/webhooks/payment")
+[ "$STATUS" = "401" ] && echo "PASS: Missing signature rejected" || echo "FAIL: Got $STATUS"
+
+# Stale timestamp (replay attack) — expect 403
+OLD_TIMESTAMP=$((TIMESTAMP - 600))
+OLD_SIGNATURE=$(printf "%s.%s" "$OLD_TIMESTAMP" "$PAYLOAD" | openssl dgst -sha256 -hmac "$WEBHOOK_SECRET" | awk '{print $2}')
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Content-Type: application/json" \
+  -H "X-Webhook-Signature: t=$OLD_TIMESTAMP,v1=$OLD_SIGNATURE" \
+  -d "$PAYLOAD" \
+  "https://api.example.com/webhooks/payment")
+[ "$STATUS" = "403" ] && echo "PASS: Stale timestamp rejected" || echo "FAIL: Got $STATUS"
+```
+
+**Also test**: idempotency (same `X-Webhook-Id` sent twice — should be processed once), invalid/empty payloads (expect 400).
+
+---
+
+## Authentication Testing Patterns
+
+### OAuth 2.0 Flow Testing
+
+**Authorization Code flow**:
+```bash
+# Step 1: Initiate authorization — verify redirect
+AUTHORIZE_URL="https://api.example.com/oauth/authorize?response_type=code&client_id=myapp&redirect_uri=https://myapp.example.com/callback&scope=read+write&state=random_state_123"
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" "$AUTHORIZE_URL")
+[ "$STATUS" = "302" ] || [ "$STATUS" = "200" ] && echo "PASS: Auth endpoint reachable" || echo "FAIL: Got $STATUS"
+
+# Step 2: Exchange authorization code for token
+TOKEN_RESPONSE=$(curl -s -X POST \
+  -H "Content-Type: application/x-www-form-urlencoded" \
+  -d "grant_type=authorization_code&code=AUTH_CODE_HERE&redirect_uri=https://myapp.example.com/callback&client_id=myapp&client_secret=secret" \
+  "https://api.example.com/oauth/token")
+echo "$TOKEN_RESPONSE" | python3 -m json.tool
+# Verify: access_token, refresh_token, expires_in, token_type present
+
+# Step 3: Use invalid authorization code — expect 400
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Content-Type: application/x-www-form-urlencoded" \
+  -d "grant_type=authorization_code&code=INVALID_CODE&redirect_uri=https://myapp.example.com/callback&client_id=myapp&client_secret=secret" \
+  "https://api.example.com/oauth/token")
+[ "$STATUS" = "400" ] && echo "PASS: Invalid code rejected" || echo "FAIL: Got $STATUS"
+
+# Step 4: Reuse authorization code — must fail (codes are single-use)
+# Use the same AUTH_CODE_HERE again
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Content-Type: application/x-www-form-urlencoded" \
+  -d "grant_type=authorization_code&code=AUTH_CODE_HERE&redirect_uri=https://myapp.example.com/callback&client_id=myapp&client_secret=secret" \
+  "https://api.example.com/oauth/token")
+[ "$STATUS" = "400" ] && echo "PASS: Code reuse rejected" || echo "FAIL: Got $STATUS"
+```
+
+**Client Credentials flow**: Same pattern as above with `grant_type=client_credentials`. Test: valid credentials (expect `access_token`), invalid secret (expect 401), invalid `grant_type` (expect 400).
+
+**Refresh Token flow**: Exchange `grant_type=refresh_token` with `refresh_token=$REFRESH_TOKEN`. Verify: new `access_token` returned, old refresh token invalidated if rotation is enabled (reuse should return 400/401).
+
+### JWT Validation Testing
+
+Test each type of JWT failure independently:
+
+| Test Case | Token Modification | Expected Status | Expected Error |
+|-----------|-------------------|-----------------|----------------|
+| Expired token | Set `exp` to past timestamp | 401 | `token_expired` |
+| Not-yet-valid | Set `nbf` to future timestamp | 401 | `token_not_yet_valid` |
+| Wrong signature | Sign with different key | 401 | `invalid_signature` |
+| Malformed token | Remove a segment | 401 | `malformed_token` |
+| Missing `sub` claim | Remove `sub` from payload | 401 | `missing_claims` |
+| Wrong audience | Set `aud` to different app | 401 | `invalid_audience` |
+| Wrong issuer | Set `iss` to unknown issuer | 401 | `invalid_issuer` |
+| Algorithm none attack | Set `alg: none`, remove signature | 401 | `invalid_algorithm` |
+
+```bash
+# Generate a test JWT with wrong signature (using python3 as a helper)
+HEADER=$(echo -n '{"alg":"HS256","typ":"JWT"}' | base64 | tr -d '=' | tr '+/' '-_')
+PAYLOAD=$(echo -n '{"sub":"user123","exp":9999999999}' | base64 | tr -d '=' | tr '+/' '-_')
+BAD_SIG=$(echo -n "fakesignature" | base64 | tr -d '=' | tr '+/' '-_')
+BAD_JWT="${HEADER}.${PAYLOAD}.${BAD_SIG}"
+
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "Authorization: Bearer $BAD_JWT" \
+  "https://api.example.com/api/protected")
+[ "$STATUS" = "401" ] && echo "PASS: Bad JWT signature rejected" || echo "FAIL: Got $STATUS"
+
+# Algorithm "none" attack
+NONE_HEADER=$(echo -n '{"alg":"none","typ":"JWT"}' | base64 | tr -d '=' | tr '+/' '-_')
+NONE_JWT="${NONE_HEADER}.${PAYLOAD}."
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "Authorization: Bearer $NONE_JWT" \
+  "https://api.example.com/api/protected")
+[ "$STATUS" = "401" ] && echo "PASS: alg:none attack blocked" || echo "FAIL: Got $STATUS — SECURITY RISK"
+```
+
+### API Key Testing Patterns
+
+```bash
+# Valid API key in header
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
+  -H "X-API-Key: valid_key_abc123" \
+  "https://api.example.com/api/data")
+[ "$STATUS" = "200" ] && echo "PASS: Valid API key" || echo "FAIL: Got $STATUS"
+```
+
+**Also test**: key in query param (if supported), revoked key (expect 401/403), empty key (expect 401), read-only key attempting write (expect 403).
+
+### Session-Based Auth Testing
+
+Test pattern: login (capture `Set-Cookie`), use cookie for authenticated request (expect 200), logout, reuse cookie (expect 401). Also verify session fixation prevention — session ID should rotate on login.
+
+---
+
+## Contract Testing
+
+### Schema Validation Techniques
+
+Validate API responses against a JSON Schema using `python3 -c "from jsonschema import validate; ..."`:
+
+```bash
+# Fetch response and validate against schema file
+curl -s -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/users/user_001" | python3 -c "
+import sys, json
+from jsonschema import validate, ValidationError
+schema = json.load(open('/tmp/user_schema.json'))
+try:
+    validate(instance=json.load(sys.stdin), schema=schema)
+    print('PASS: Schema valid')
+except ValidationError as e:
+    print(f'FAIL: {e.message}')
+"
+```
+
+Schema should define `required` fields, property `type`/`format`/`enum` constraints, and `additionalProperties: false` for strict mode.
+
+### Breaking Change Detection
+
+Compare current response structure against a recorded baseline:
+
+```bash
+# Helper: extract JSON shape as "path: type" lines
+extract_shape() {
+  curl -s -H "Authorization: Bearer $TOKEN" "$1" | python3 -c "
+import sys, json
+def shape(obj, prefix=''):
+    s = {}
+    if isinstance(obj, dict):
+        for k, v in obj.items():
+            p = f'{prefix}.{k}' if prefix else k
+            s[p] = type(v).__name__; s.update(shape(v, p))
+    elif isinstance(obj, list) and obj:
+        s[f'{prefix}[]'] = type(obj[0]).__name__; s.update(shape(obj[0], f'{prefix}[]'))
+    return s
+for p, t in sorted(shape(json.load(sys.stdin)).items()): print(f'{p}: {t}')
+"
+}
+
+# Record baseline once, then diff against current
+extract_shape "https://api.example.com/api/users/user_001" > /tmp/api_baseline.txt
+# ... later ...
+extract_shape "https://api.example.com/api/users/user_001" > /tmp/api_current.txt
+diff /tmp/api_baseline.txt /tmp/api_current.txt && echo "PASS: No schema changes" || echo "WARN: Schema changed"
+```
+
+### Backward Compatibility Checklist
+
+When a new API version is deployed, verify that existing consumers are not broken:
+
+| Check | How to Test | Severity |
+|-------|------------|----------|
+| Removed fields | Diff response shape against baseline | **HIGH** — breaks consumers |
+| Renamed fields | Diff response keys | **HIGH** — breaks consumers |
+| Changed field type | Compare type of each field | **HIGH** — breaks deserialization |
+| New required request field | Send old-format request | **HIGH** — breaks callers |
+| Changed enum values | Check if old values still accepted | **MEDIUM** — breaks validation |
+| Changed error format | Compare error response structure | **MEDIUM** — breaks error handlers |
+| Changed status codes | Compare response codes for same input | **MEDIUM** — breaks status checks |
+| New optional fields | Verify response still parses | **LOW** — usually safe |
+| Pagination format change | Test with existing page params | **MEDIUM** — breaks pagination loops |
+
+### Consumer-Driven Contract Testing
+
+Concept: Each API consumer defines the minimum contract they need (required fields, forbidden fields, expected status codes). The provider runs all consumer contracts in CI.
+
+```json
+{
+  "consumer": "mobile-app-v2",
+  "provider": "user-service",
+  "interactions": [
+    {
+      "description": "get user profile",
+      "request": {"method": "GET", "path": "/api/users/me", "headers": {"Authorization": "Bearer valid_token"}},
+      "response": {"status": 200, "body_contains": ["id", "name", "email"], "body_must_not_contain": ["password", "internal_id"]}
+    }
+  ]
+}
+```
+
+Runner approach: iterate interactions, execute each request with curl, verify status code matches and required/forbidden fields are present/absent in the response body.
+
+---
+
+## Performance Testing Deep Dive
+
+### Load Test Types
+
+| Type | Purpose | Pattern |
+|------|---------|---------|
+| **Soak** | Detect memory leaks, connection pool exhaustion | Steady traffic (e.g., 5 req/s) for hours; compare first-quarter vs last-quarter response times |
+| **Spike** | Verify graceful handling of sudden bursts | Baseline → 10x-20x burst → recovery; check error rate and recovery time |
+| **Stress** | Find the breaking point | Incrementally increase concurrency until errors begin |
+
+### Stress Testing (Representative Example)
+
+Incrementally increase load until errors begin — adapt the same pattern for soak (fixed concurrency, long duration) or spike (sudden burst) testing:
+
+```bash
+echo "concurrency,success_rate,avg_time,p95_time" > /tmp/stress_results.csv
+for CONCURRENCY in 10 25 50 100 200 500; do
+  RESULTS=$(mktemp)
+  for i in $(seq 1 $CONCURRENCY); do
+    curl -s -o /dev/null -w "%{http_code} %{time_total}\n" \
+      -H "Authorization: Bearer $TOKEN" \
+      "https://api.example.com/api/data" >> "$RESULTS" &
+  done
+  wait
+
+  TOTAL=$(wc -l < "$RESULTS")
+  SUCCESS=$(grep -c "^200" "$RESULTS")
+  AVG_TIME=$(awk '{sum+=$2; n++} END {printf "%.3f", sum/n}' "$RESULTS")
+  P95_TIME=$(awk '{print $2}' "$RESULTS" | sort -n | awk -v p=0.95 'NR==1{n=0} {a[n++]=$1} END {print a[int(n*p)]}')
+
+  echo "$CONCURRENCY,$((SUCCESS*100/TOTAL))%,$AVG_TIME,$P95_TIME" >> /tmp/stress_results.csv
+  echo "Concurrency $CONCURRENCY: ${SUCCESS}/${TOTAL} success, avg=${AVG_TIME}s, p95=${P95_TIME}s"
+
+  rm "$RESULTS"
+  sleep 3  # Let the server recover between steps
+done
+
+echo "=== Stress Test Summary ==="
+column -t -s',' /tmp/stress_results.csv
+```
+
+### Latency Percentile Analysis
+
+Collect many response times (e.g., 1000 with concurrency capped at 20), then compute p50/p75/p90/p95/p99 percentiles. Compare first-quarter vs last-quarter averages to detect degradation over time.
+
+```bash
+# Collect response times
+TIMES_FILE=$(mktemp)
+for i in $(seq 1 1000); do
+  curl -s -o /dev/null -w "%{time_total}\n" \
+    -H "Authorization: Bearer $TOKEN" \
+    "https://api.example.com/api/data" >> "$TIMES_FILE" &
+  [ $((i % 20)) -eq 0 ] && wait
+done
+wait
+# Sort and compute percentiles with: sort -n "$TIMES_FILE" | python3 ...
+rm "$TIMES_FILE"
+```
+
+### Connection Pool Testing
+
+- **Keep-alive reuse**: Send multiple URLs in one curl call with `Connection: keep-alive`; second/third requests should show near-zero `time_connect`.
+- **Connection exhaustion**: Open 500 concurrent keep-alive connections; watch for 503 or connection refused errors.
+
+---
+
+## Common API Bugs & How to Find Them
+
+### N+1 Query Detection
+
+Response time should not scale linearly with data size. If fetching 10 items takes 100ms but 100 items takes 1000ms, the API likely has an N+1 query problem.
+
+```bash
+# Compare response times for different page sizes
+for SIZE in 1 10 50 100; do
+  TIME=$(curl -s -o /dev/null -w "%{time_total}" \
+    -H "Authorization: Bearer $TOKEN" \
+    "https://api.example.com/api/orders?per_page=$SIZE")
+  echo "page_size=$SIZE  time=${TIME}s"
+done
+# Expected (healthy): Times should NOT scale linearly
+#   page_size=1    time=0.045s
+#   page_size=10   time=0.052s
+#   page_size=50   time=0.078s
+#   page_size=100  time=0.110s
+# Red flag (N+1): Times scale roughly linearly
+#   page_size=1    time=0.045s
+#   page_size=10   time=0.350s
+#   page_size=50   time=1.600s
+#   page_size=100  time=3.200s
+```
+
+### Race Condition Testing
+
+```bash
+# Concurrent counter increment — final value should equal attempt count
+curl -s -X PUT -H "Content-Type: application/json" -H "Authorization: Bearer $TOKEN" \
+  -d '{"value": 0}' "https://api.example.com/api/counters/counter_001"
+
+for i in $(seq 1 50); do
+  curl -s -X POST -H "Content-Type: application/json" -H "Authorization: Bearer $TOKEN" \
+    -d '{"increment": 1}' "https://api.example.com/api/counters/counter_001/increment" &
+done
+wait
+
+FINAL=$(curl -s -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/counters/counter_001" | python3 -c "import sys,json; print(json.load(sys.stdin)['value'])")
+[ "$FINAL" = "50" ] && echo "PASS: No race condition" || echo "FAIL: Lost $((50 - FINAL)) increments"
+```
+
+**Optimistic locking test**: Two concurrent PUTs with same `If-Match` ETag — one should get 200, the other 409 Conflict.
+
+### Pagination Edge Cases
+
+| Input | Expected Behavior |
+|-------|------------------|
+| `page=0` | 400, or treat as page 1 |
+| `page=-1` | 400 |
+| `page=99999` (beyond data) | 200 with empty array, not error |
+| `per_page=0` | 400 or use default |
+| `per_page=100000` | Capped to server max (e.g., 100) |
+| Delete item mid-pagination | No items skipped or duplicated on next page |
+
+### Timezone Handling Bugs
+
+Test that equivalent timestamps in different offset formats are stored identically:
+
+```bash
+# All four represent the same moment — stored values should be equivalent
+for TZ in "2025-06-15T10:00:00Z" "2025-06-15T10:00:00+00:00" "2025-06-15T18:00:00+08:00" "2025-06-15T05:00:00-05:00"; do
+  STORED=$(curl -s -X POST -H "Content-Type: application/json" -H "Authorization: Bearer $TOKEN" \
+    -d "{\"title\": \"tz_test\", \"scheduled_at\": \"$TZ\"}" \
+    "https://api.example.com/api/events" | python3 -c "import sys,json; print(json.load(sys.stdin).get('scheduled_at','ERROR'))")
+  echo "Input: $TZ -> Stored: $STORED"
+done
+```
+
+**Also test**: date range filters across timezone boundaries, midnight boundary inclusion/exclusion behavior.
+
+### Character Encoding Issues
+
+Test that the API correctly round-trips various Unicode inputs. Key test values:
+
+| Category | Example | What Breaks |
+|----------|---------|-------------|
+| Emoji | `Hello 🌍🚀` | UTF-8 4-byte sequences, database column width |
+| CJK | `你好世界` | Multi-byte encoding, string length vs byte length |
+| Diacritics | `café` (composed vs decomposed) | Unicode normalization (NFC vs NFD) |
+| Zero-width | `test\u200Bword` | Invisible characters in search/comparison |
+| Null byte | `test\u0000value` | String termination in C-based systems |
+
+```bash
+# Round-trip test pattern: POST a value, verify GET returns the same
+for VALUE in "Hello 🌍🚀" "你好世界" "café"; do
+  RESPONSE=$(curl -s -X POST -H "Content-Type: application/json; charset=utf-8" \
+    -H "Authorization: Bearer $TOKEN" \
+    -d "{\"name\": \"$VALUE\"}" \
+    "https://api.example.com/api/items")
+  RETURNED=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin).get('name','ERROR'))")
+  [ "$VALUE" = "$RETURNED" ] && echo "PASS: $VALUE" || echo "FAIL: sent='$VALUE' got='$RETURNED'"
+done
+```
+
+---
+
+## Advanced curl Patterns
+
+### File Upload Testing
+
+```bash
+# Single file upload
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Authorization: Bearer $TOKEN" \
+  -F "file=@/path/to/document.pdf" \
+  -F "description=Test upload" \
+  "https://api.example.com/api/uploads")
+echo "Single file upload: $STATUS"
+
+# Multiple file upload
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
+  -H "Authorization: Bearer $TOKEN" \
+  -F "files[]=@/path/to/file1.png" \
+  -F "files[]=@/path/to/file2.png" \
+  -F "category=images" \
+  "https://api.example.com/api/uploads/batch")
+echo "Multi-file upload: $STATUS"
+```
+
+**Edge cases to also test**: oversized files (expect 413), wrong content type (e.g., `script.sh` declared as `image/png`), zero-byte files (expect 400).
+
+### Multipart Form Data
+
+```bash
+# Mixed multipart: file + JSON metadata
+curl -s -X POST \
+  -H "Authorization: Bearer $TOKEN" \
+  -F "metadata={\"title\":\"Report Q4\",\"tags\":[\"finance\",\"quarterly\"]};type=application/json" \
+  -F "file=@/path/to/report.pdf" \
+  "https://api.example.com/api/documents"
+
+# Form-encoded data (not JSON)
+curl -s -X POST \
+  -H "Content-Type: application/x-www-form-urlencoded" \
+  -d "username=testuser&password=testpass&remember=true" \
+  "https://api.example.com/auth/login"
+```
+
+### Cookie-Based Session Testing
+
+```bash
+# Full session lifecycle with cookie jar
+COOKIE_JAR=$(mktemp)
+
+# Login — store cookies
+curl -s -c "$COOKIE_JAR" -X POST \
+  -H "Content-Type: application/json" \
+  -d '{"username": "testuser", "password": "testpass"}' \
+  "https://api.example.com/auth/login"
+
+# Authenticated request — send cookies
+curl -s -b "$COOKIE_JAR" -c "$COOKIE_JAR" \
+  "https://api.example.com/api/profile"
+
+# Logout and verify session invalidated
+curl -s -b "$COOKIE_JAR" -c "$COOKIE_JAR" -X POST \
+  "https://api.example.com/auth/logout"
+STATUS=$(curl -s -b "$COOKIE_JAR" -o /dev/null -w "%{http_code}" \
+  "https://api.example.com/api/profile")
+[ "$STATUS" = "401" ] && echo "PASS: Session invalidated" || echo "FAIL: Got $STATUS"
+rm "$COOKIE_JAR"
+```
+
+**Also verify**: HttpOnly/Secure/SameSite cookie attributes, session ID rotation on login (session fixation prevention).
+
+### Following Redirects
+
+```bash
+# Follow redirects automatically
+curl -s -L -o /dev/null -w "final_url:%{url_effective} status:%{http_code} redirects:%{num_redirects}\n" \
+  "https://api.example.com/old-endpoint"
+
+# Don't follow — inspect redirect target
+curl -s -D- -o /dev/null \
+  "https://api.example.com/old-endpoint" | grep -i "location:"
+
+# Open redirect vulnerability test
+LOCATION=$(curl -s -D- -o /dev/null \
+  "https://api.example.com/redirect?url=https://evil.example.com" | grep -i "location:" | tr -d '\r')
+echo "$LOCATION" | grep -q "evil.example.com" && echo "FAIL: Open redirect vulnerability" || echo "PASS: Redirect restricted"
+
+# HTTP to HTTPS redirect check
+STATUS=$(curl -s -o /dev/null -w "%{http_code}" "http://api.example.com/api/data")
+[ "$STATUS" = "301" ] || [ "$STATUS" = "308" ] && echo "PASS: HTTP redirects to HTTPS" || echo "WARN: No HTTPS redirect (got $STATUS)"
+```
+
+### HEAD, OPTIONS, and CORS
+
+```bash
+# HEAD request — verify no body returned
+curl -s -I -w "status:%{http_code} size:%{size_download}\n" \
+  -H "Authorization: Bearer $TOKEN" \
+  "https://api.example.com/api/data"
+
+# OPTIONS request — check CORS and allowed methods
+curl -s -X OPTIONS -D- -o /dev/null \
+  -H "Origin: https://myapp.example.com" \
+  -H "Access-Control-Request-Method: POST" \
+  "https://api.example.com/api/data" | grep -iE "(allow|access-control)"
+```
diff --git a/hands/browser/HAND.toml b/hands/browser/HAND.toml
index 3ff4f86..cad8f0d 100644
--- a/hands/browser/HAND.toml
+++ b/hands/browser/HAND.toml
@@ -296,6 +296,169 @@ token_consumption = "low"
 default_active = true
 activation_warning = "Browser hand runs continuously but mainly consumes tokens when actively performing web tasks."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "浏览器 Hand"
 description = "自主网页浏览器——导航网站、填写表单、点击按钮，经用户批准后完成多步骤网页任务"
+category = "生产力"
+
+[i18n.zh.settings.headless]
+label = "无头模式"
+description = "在不显示浏览器窗口的情况下运行（推荐用于服务器环境）"
+
+[i18n.zh.settings.approval_mode]
+label = "购买审批"
+description = "在完成任何购买或支付操作前，需要用户明确确认"
+
+[i18n.zh.settings.max_pages_per_task]
+label = "每任务最大页面数"
+description = "每个任务允许的最大页面导航次数，防止无限浏览"
+
+[i18n.zh.settings.default_wait]
+label = "操作后默认等待"
+description = "点击或导航后等待页面稳定的时长"
+
+[i18n.zh.settings.screenshot_on_action]
+label = "操作后截图"
+description = "每次点击/导航后自动截图，用于视觉验证"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "ブラウザ Hand"
+description = "自律型ウェブブラウザ——サイトのナビゲーション、フォーム入力、ボタンクリック、ユーザー承認付きの複数ステップWebタスクの実行"
+category = "生産性"
+
+[i18n.ja.settings.headless]
+label = "ヘッドレスモード"
+description = "ブラウザウィンドウを表示せずに実行する（サーバー環境に推奨）"
+
+[i18n.ja.settings.approval_mode]
+label = "購入承認"
+description = "購入や支払いを完了する前にユーザーの明示的な確認を求める"
+
+[i18n.ja.settings.max_pages_per_task]
+label = "タスクあたりの最大ページ数"
+description = "暴走的なブラウジングを防ぐため、タスクごとに許可されるページ遷移の最大数"
+
+[i18n.ja.settings.default_wait]
+label = "操作後のデフォルト待機時間"
+description = "クリックやナビゲーション後、ページが安定するまでの待機時間"
+
+[i18n.ja.settings.screenshot_on_action]
+label = "操作後のスクリーンショット"
+description = "クリック/ナビゲーションのたびに自動的にスクリーンショットを撮影し、視覚的に確認する"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Navegador"
+description = "Navegador web autónomo — navega sitios, completa formularios, hace clic en botones y realiza tareas web de múltiples pasos con aprobación del usuario para compras"
+category = "Productividad"
+
+[i18n.es.settings.headless]
+label = "Modo sin interfaz"
+description = "Ejecutar el navegador sin ventana visible (recomendado para servidores)"
+
+[i18n.es.settings.approval_mode]
+label = "Aprobación de compras"
+description = "Requerir confirmación explícita del usuario antes de completar cualquier compra o pago"
+
+[i18n.es.settings.max_pages_per_task]
+label = "Máximo de páginas por tarea"
+description = "Número máximo de navegaciones de página permitidas por tarea para evitar navegación descontrolada"
+
+[i18n.es.settings.default_wait]
+label = "Espera predeterminada tras acción"
+description = "Cuánto tiempo esperar después de hacer clic o navegar para que la página se estabilice"
+
+[i18n.es.settings.screenshot_on_action]
+label = "Captura de pantalla tras acciones"
+description = "Tomar automáticamente una captura de pantalla después de cada clic/navegación para verificación visual"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Navigateur"
+description = "Navigateur web autonome — navigue sur les sites, remplit les formulaires, clique sur les boutons et exécute des tâches web multi-étapes avec approbation utilisateur pour les achats"
+category = "Productivité"
+
+[i18n.fr.settings.headless]
+label = "Mode sans interface"
+description = "Exécuter le navigateur sans fenêtre visible (recommandé pour les serveurs)"
+
+[i18n.fr.settings.approval_mode]
+label = "Approbation des achats"
+description = "Exiger la confirmation explicite de l'utilisateur avant de finaliser tout achat ou paiement"
+
+[i18n.fr.settings.max_pages_per_task]
+label = "Pages maximum par tâche"
+description = "Nombre maximum de navigations de page autorisées par tâche pour éviter une navigation incontrôlée"
+
+[i18n.fr.settings.default_wait]
+label = "Attente par défaut après action"
+description = "Durée d'attente après un clic ou une navigation pour que la page se stabilise"
+
+[i18n.fr.settings.screenshot_on_action]
+label = "Capture d'écran après action"
+description = "Prendre automatiquement une capture d'écran après chaque clic/navigation pour vérification visuelle"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Browser-Hand"
+description = "Autonomer Webbrowser — navigiert Websites, füllt Formulare aus, klickt Schaltflächen und führt mehrstufige Webaufgaben mit Benutzerfreigabe für Käufe aus"
+category = "Produktivität"
+
+[i18n.de.settings.headless]
+label = "Headless-Modus"
+description = "Browser ohne sichtbares Fenster ausführen (empfohlen für Server)"
+
+[i18n.de.settings.approval_mode]
+label = "Kaufgenehmigung"
+description = "Ausdrückliche Benutzerbestätigung vor dem Abschluss eines Kaufs oder einer Zahlung erforderlich"
+
+[i18n.de.settings.max_pages_per_task]
+label = "Maximale Seiten pro Aufgabe"
+description = "Maximale Anzahl erlaubter Seitennavigationen pro Aufgabe, um unkontrolliertes Surfen zu verhindern"
+
+[i18n.de.settings.default_wait]
+label = "Standard-Wartezeit nach Aktion"
+description = "Wartezeit nach einem Klick oder einer Navigation, bis sich die Seite stabilisiert hat"
+
+[i18n.de.settings.screenshot_on_action]
+label = "Screenshot nach Aktion"
+description = "Nach jedem Klick/jeder Navigation automatisch einen Screenshot für visuelle Überprüfung erstellen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "브라우저 Hand"
+description = "자율 웹 브라우저 — 사이트 탐색, 양식 작성, 버튼 클릭, 구매 시 사용자 승인을 받아 다단계 웹 작업 수행"
+category = "생산성"
+
+[i18n.ko.settings.headless]
+label = "헤드리스 모드"
+description = "브라우저 창을 표시하지 않고 실행 (서버 환경에 권장)"
+
+[i18n.ko.settings.approval_mode]
+label = "구매 승인"
+description = "구매 또는 결제 완료 전 사용자의 명시적 확인 필요"
+
+[i18n.ko.settings.max_pages_per_task]
+label = "작업당 최대 페이지 수"
+description = "작업당 허용되는 최대 페이지 탐색 횟수 (무한 브라우징 방지)"
+
+[i18n.ko.settings.default_wait]
+label = "동작 후 기본 대기"
+description = "클릭 또는 탐색 후 페이지가 안정될 때까지 대기하는 시간"
+
+[i18n.ko.settings.screenshot_on_action]
+label = "동작 후 스크린샷"
+description = "클릭/탐색 후 자동으로 스크린샷을 캡처하여 시각적으로 검증"
diff --git a/hands/clip/HAND.toml b/hands/clip/HAND.toml
index ec0a22a..e31a520 100644
--- a/hands/clip/HAND.toml
+++ b/hands/clip/HAND.toml
@@ -645,6 +645,289 @@ frequency = "on-demand"
 token_consumption = "medium"
 default_active = false
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "视频剪辑 Hand"
 description = "将长视频自动剪辑为病毒式短视频，配有字幕和缩略图"
+category = "内容"
+
+[i18n.zh.settings.stt_provider]
+label = "语音转文字服务"
+description = "用于生成字幕和片段选择的音频转录方式"
+
+[i18n.zh.settings.tts_provider]
+label = "文字转语音服务"
+description = "可选的配音或旁白生成服务"
+
+[i18n.zh.settings.elevenlabs_api_key]
+label = "ElevenLabs API 密钥"
+description = "来自 elevenlabs.io 的高质量文字转语音 API 密钥。选择 ElevenLabs TTS 时必填。"
+
+[i18n.zh.settings.publish_target]
+label = "发布目标"
+description = "处理完成后将短视频发送到哪里。选择"仅本地"则跳过发布。"
+
+[i18n.zh.settings.telegram_bot_token]
+label = "Telegram 机器人令牌"
+description = "从 Telegram 的 @BotFather 获取（例如 123456:ABC-DEF...）。机器人需为目标频道管理员。"
+
+[i18n.zh.settings.telegram_chat_id]
+label = "Telegram 聊天 ID"
+description = "频道：-100XXXXXXXXXX 或 @频道名。群组：数字 ID。可通过 @userinfobot 获取。"
+
+[i18n.zh.settings.whatsapp_token]
+label = "WhatsApp 访问令牌"
+description = "从 Meta 商务管理平台 > 系统用户获取的永久令牌。临时令牌 24 小时后过期。"
+
+[i18n.zh.settings.whatsapp_phone_id]
+label = "WhatsApp 电话号码 ID"
+description = "从 Meta 开发者门户 > WhatsApp > API 设置获取（例如 1234567890）"
+
+[i18n.zh.settings.whatsapp_recipient]
+label = "WhatsApp 接收方"
+description = "国际格式的电话号码，不含 + 号或空格（例如 14155551234）"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "发布到频道前将短视频加入队列供审核"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "動画クリップ Hand"
+description = "長尺動画をキャプション付きサムネイル付きのバイラルショートクリップに変換"
+category = "コンテンツ"
+
+[i18n.ja.settings.stt_provider]
+label = "音声テキスト変換プロバイダー"
+description = "字幕生成とクリップ選択に使用する音声の文字起こし方法"
+
+[i18n.ja.settings.tts_provider]
+label = "テキスト音声変換プロバイダー"
+description = "クリップへのオプションのボイスオーバーまたはナレーション生成"
+
+[i18n.ja.settings.elevenlabs_api_key]
+label = "ElevenLabs APIキー"
+description = "elevenlabs.ioの高品質テキスト音声変換用APIキー。ElevenLabs TTSを選択した場合に必須。"
+
+[i18n.ja.settings.publish_target]
+label = "公開先"
+description = "処理完了後にクリップを送信する先。「ローカルのみ」を選択すると公開をスキップします。"
+
+[i18n.ja.settings.telegram_bot_token]
+label = "Telegramボットトークン"
+description = "Telegramの@BotFatherから取得（例: 123456:ABC-DEF...）。ボットは対象チャンネルの管理者である必要があります。"
+
+[i18n.ja.settings.telegram_chat_id]
+label = "TelegramチャットID"
+description = "チャンネル: -100XXXXXXXXXX または @チャンネル名。グループ: 数値ID。@userinfobot で取得可能。"
+
+[i18n.ja.settings.whatsapp_token]
+label = "WhatsAppアクセストークン"
+description = "Metaビジネス設定 > システムユーザーから取得した永続トークン。一時トークンは24時間で期限切れになります。"
+
+[i18n.ja.settings.whatsapp_phone_id]
+label = "WhatsApp電話番号ID"
+description = "Meta開発者ポータル > WhatsApp > APIセットアップから取得（例: 1234567890）"
+
+[i18n.ja.settings.whatsapp_recipient]
+label = "WhatsApp送信先"
+description = "国際形式の電話番号（+やスペースなし、例: 14155551234）"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "チャンネルに公開する前にクリップをレビュー用キューに追加する"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Clips de Video"
+description = "Convierte videos largos en clips cortos virales con subtítulos y miniaturas"
+category = "Contenido"
+
+[i18n.es.settings.stt_provider]
+label = "Proveedor de voz a texto"
+description = "Cómo se transcribe el audio a texto para subtítulos y selección de clips"
+
+[i18n.es.settings.tts_provider]
+label = "Proveedor de texto a voz"
+description = "Generación opcional de locución o narración para los clips"
+
+[i18n.es.settings.elevenlabs_api_key]
+label = "Clave API de ElevenLabs"
+description = "Clave API de elevenlabs.io para texto a voz de alta calidad. Requerida cuando se selecciona ElevenLabs TTS."
+
+[i18n.es.settings.publish_target]
+label = "Destino de publicación"
+description = "Dónde enviar los clips terminados después del procesamiento. Seleccionar 'Solo local' para omitir la publicación."
+
+[i18n.es.settings.telegram_bot_token]
+label = "Token del bot de Telegram"
+description = "De @BotFather en Telegram (ej. 123456:ABC-DEF...). El bot debe ser administrador del canal de destino."
+
+[i18n.es.settings.telegram_chat_id]
+label = "ID de chat de Telegram"
+description = "Canal: -100XXXXXXXXXX o @nombrechannel. Grupo: ID numérico. Obtener mediante @userinfobot."
+
+[i18n.es.settings.whatsapp_token]
+label = "Token de acceso de WhatsApp"
+description = "Token permanente de Meta Business Settings > Usuarios del sistema. Los tokens temporales expiran en 24h."
+
+[i18n.es.settings.whatsapp_phone_id]
+label = "ID de número de teléfono de WhatsApp"
+description = "Desde el Portal de Desarrolladores de Meta > WhatsApp > Configuración de API (ej. 1234567890)"
+
+[i18n.es.settings.whatsapp_recipient]
+label = "Destinatario de WhatsApp"
+description = "Número de teléfono en formato internacional, sin + ni espacios (ej. 14155551234)"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Poner clips en cola para revisión antes de publicarlos en los canales"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Clips Vidéo"
+description = "Transforme les longues vidéos en clips courts viraux avec sous-titres et miniatures"
+category = "Contenu"
+
+[i18n.fr.settings.stt_provider]
+label = "Fournisseur de reconnaissance vocale"
+description = "Méthode de transcription audio pour les sous-titres et la sélection de clips"
+
+[i18n.fr.settings.tts_provider]
+label = "Fournisseur de synthèse vocale"
+description = "Génération optionnelle de voix off ou de narration pour les clips"
+
+[i18n.fr.settings.elevenlabs_api_key]
+label = "Clé API ElevenLabs"
+description = "Clé API de elevenlabs.io pour la synthèse vocale haute qualité. Requise lorsque ElevenLabs TTS est sélectionné."
+
+[i18n.fr.settings.publish_target]
+label = "Destination de publication"
+description = "Où envoyer les clips terminés après traitement. Sélectionner 'Local uniquement' pour ignorer la publication."
+
+[i18n.fr.settings.telegram_bot_token]
+label = "Jeton du bot Telegram"
+description = "De @BotFather sur Telegram (ex. 123456:ABC-DEF...). Le bot doit être administrateur du canal cible."
+
+[i18n.fr.settings.telegram_chat_id]
+label = "ID de chat Telegram"
+description = "Canal : -100XXXXXXXXXX ou @nomducanal. Groupe : ID numérique. Obtenir via @userinfobot."
+
+[i18n.fr.settings.whatsapp_token]
+label = "Jeton d'accès WhatsApp"
+description = "Jeton permanent depuis Meta Business Settings > Utilisateurs système. Les jetons temporaires expirent en 24h."
+
+[i18n.fr.settings.whatsapp_phone_id]
+label = "ID de numéro de téléphone WhatsApp"
+description = "Depuis le Portail Développeurs Meta > WhatsApp > Configuration API (ex. 1234567890)"
+
+[i18n.fr.settings.whatsapp_recipient]
+label = "Destinataire WhatsApp"
+description = "Numéro de téléphone au format international, sans + ni espaces (ex. 14155551234)"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Mettre les clips en file d'attente pour révision avant publication sur les canaux"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Videoclip-Hand"
+description = "Verwandelt lange Videos in virale Kurzclips mit Untertiteln und Vorschaubildern"
+category = "Inhalt"
+
+[i18n.de.settings.stt_provider]
+label = "Sprache-zu-Text-Anbieter"
+description = "Methode der Audiotranskription für Untertitel und Clipauswahl"
+
+[i18n.de.settings.tts_provider]
+label = "Text-zu-Sprache-Anbieter"
+description = "Optionale Voiceover- oder Erzählungsgenerierung für Clips"
+
+[i18n.de.settings.elevenlabs_api_key]
+label = "ElevenLabs API-Schlüssel"
+description = "API-Schlüssel von elevenlabs.io für hochwertige Text-zu-Sprache. Erforderlich bei Auswahl von ElevenLabs TTS."
+
+[i18n.de.settings.publish_target]
+label = "Veröffentlichungsziel"
+description = "Wohin fertige Clips nach der Verarbeitung gesendet werden. 'Nur lokal' wählen, um die Veröffentlichung zu überspringen."
+
+[i18n.de.settings.telegram_bot_token]
+label = "Telegram-Bot-Token"
+description = "Von @BotFather auf Telegram (z.B. 123456:ABC-DEF...). Der Bot muss Administrator des Zielkanals sein."
+
+[i18n.de.settings.telegram_chat_id]
+label = "Telegram-Chat-ID"
+description = "Kanal: -100XXXXXXXXXX oder @Kanalname. Gruppe: Numerische ID. Über @userinfobot abrufbar."
+
+[i18n.de.settings.whatsapp_token]
+label = "WhatsApp-Zugriffstoken"
+description = "Permanentes Token aus Meta Business Settings > Systembenutzer. Temporäre Token laufen nach 24h ab."
+
+[i18n.de.settings.whatsapp_phone_id]
+label = "WhatsApp-Telefonnummer-ID"
+description = "Aus dem Meta-Entwicklerportal > WhatsApp > API-Einrichtung (z.B. 1234567890)"
+
+[i18n.de.settings.whatsapp_recipient]
+label = "WhatsApp-Empfänger"
+description = "Telefonnummer im internationalen Format, ohne + oder Leerzeichen (z.B. 14155551234)"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Clips zur Überprüfung in die Warteschlange stellen, bevor sie auf Kanälen veröffentlicht werden"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "비디오 클립 Hand"
+description = "장편 영상을 자막과 썸네일이 포함된 바이럴 숏폼 클립으로 변환"
+category = "콘텐츠"
+
+[i18n.ko.settings.stt_provider]
+label = "음성-텍스트 변환 서비스"
+description = "자막 생성 및 클립 선택을 위한 오디오 전사 방식"
+
+[i18n.ko.settings.tts_provider]
+label = "텍스트-음성 변환 서비스"
+description = "선택적 더빙 또는 나레이션 생성 서비스"
+
+[i18n.ko.settings.elevenlabs_api_key]
+label = "ElevenLabs API 키"
+description = "elevenlabs.io의 고품질 텍스트-음성 변환 API 키. ElevenLabs TTS 선택 시 필수."
+
+[i18n.ko.settings.publish_target]
+label = "게시 대상"
+description = "처리 완료 후 숏폼 클립을 전송할 위치. '로컬 전용'을 선택하면 게시를 건너뜁니다."
+
+[i18n.ko.settings.telegram_bot_token]
+label = "Telegram 봇 토큰"
+description = "Telegram의 @BotFather에서 발급 (예: 123456:ABC-DEF...). 봇이 대상 채널의 관리자여야 합니다."
+
+[i18n.ko.settings.telegram_chat_id]
+label = "Telegram 채팅 ID"
+description = "채널: -100XXXXXXXXXX 또는 @채널명. 그룹: 숫자 ID. @userinfobot으로 확인 가능."
+
+[i18n.ko.settings.whatsapp_token]
+label = "WhatsApp 액세스 토큰"
+description = "Meta 비즈니스 설정 > 시스템 사용자에서 발급한 영구 토큰. 임시 토큰은 24시간 후 만료."
+
+[i18n.ko.settings.whatsapp_phone_id]
+label = "WhatsApp 전화번호 ID"
+description = "Meta 개발자 포털 > WhatsApp > API 설정에서 확인 (예: 1234567890)"
+
+[i18n.ko.settings.whatsapp_recipient]
+label = "WhatsApp 수신자"
+description = "+ 기호나 공백 없이 국제 형식의 전화번호 (예: 14155551234)"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "채널에 게시하기 전 클립을 대기열에 추가하여 검토"
diff --git a/hands/collector/HAND.toml b/hands/collector/HAND.toml
index 930559e..ac9e419 100644
--- a/hands/collector/HAND.toml
+++ b/hands/collector/HAND.toml
@@ -391,6 +391,241 @@ token_consumption = "high"
 default_active = false
 activation_warning = "Collector hand runs continuously and monitors targets, consuming tokens."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "情报采集 Hand"
 description = "自主情报采集智能体——持续监控目标，支持变更检测和知识图谱"
+category = "数据"
+
+[i18n.zh.settings.target_subject]
+label = "监控目标"
+description = "要监控的对象（公司名称、人物、技术、市场、话题）"
+
+[i18n.zh.settings.collection_depth]
+label = "采集深度"
+description = "每个采集周期的挖掘深度"
+
+[i18n.zh.settings.update_frequency]
+label = "更新频率"
+description = "执行采集扫描的频率"
+
+[i18n.zh.settings.focus_area]
+label = "关注领域"
+description = "分析采集情报时的侧重角度"
+
+[i18n.zh.settings.alert_on_changes]
+label = "变更告警"
+description = "检测到重大变更时发布事件通知"
+
+[i18n.zh.settings.report_format]
+label = "报告格式"
+description = "情报报告的输出格式"
+
+[i18n.zh.settings.max_sources_per_cycle]
+label = "每周期最大来源数"
+description = "每次采集扫描处理的最大来源数量"
+
+[i18n.zh.settings.track_sentiment]
+label = "情感追踪"
+description = "分析并追踪随时间变化的情感趋势"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "インテリジェンス収集 Hand"
+description = "自律型インテリジェンス収集エージェント——変更検出とナレッジグラフによる対象の継続的監視"
+category = "データ"
+
+[i18n.ja.settings.target_subject]
+label = "監視対象"
+description = "監視する対象（企業名、人物、技術、市場、トピック）"
+
+[i18n.ja.settings.collection_depth]
+label = "収集深度"
+description = "各収集サイクルでの調査の深さ"
+
+[i18n.ja.settings.update_frequency]
+label = "更新頻度"
+description = "収集スキャンの実行頻度"
+
+[i18n.ja.settings.focus_area]
+label = "フォーカスエリア"
+description = "収集したインテリジェンスを分析する際の視点"
+
+[i18n.ja.settings.alert_on_changes]
+label = "変更アラート"
+description = "重大な変更が検出された場合にイベント通知を発行する"
+
+[i18n.ja.settings.report_format]
+label = "レポート形式"
+description = "インテリジェンスレポートの出力形式"
+
+[i18n.ja.settings.max_sources_per_cycle]
+label = "サイクルあたりの最大ソース数"
+description = "各収集スキャンで処理するソースの最大数"
+
+[i18n.ja.settings.track_sentiment]
+label = "センチメント追跡"
+description = "時間の経過に伴うセンチメントの傾向を分析・追跡する"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Recopilación de Inteligencia"
+description = "Recopilador autónomo de inteligencia — monitorea cualquier objetivo de forma continua con detección de cambios y grafos de conocimiento"
+category = "Datos"
+
+[i18n.es.settings.target_subject]
+label = "Objetivo de monitoreo"
+description = "Qué monitorear (nombre de empresa, persona, tecnología, mercado, tema)"
+
+[i18n.es.settings.collection_depth]
+label = "Profundidad de recopilación"
+description = "Qué tan profundo investigar en cada ciclo"
+
+[i18n.es.settings.update_frequency]
+label = "Frecuencia de actualización"
+description = "Con qué frecuencia ejecutar los barridos de recopilación"
+
+[i18n.es.settings.focus_area]
+label = "Área de enfoque"
+description = "Perspectiva desde la cual analizar la inteligencia recopilada"
+
+[i18n.es.settings.alert_on_changes]
+label = "Alertar ante cambios"
+description = "Publicar un evento cuando se detecten cambios significativos"
+
+[i18n.es.settings.report_format]
+label = "Formato de informe"
+description = "Formato de salida para los informes de inteligencia"
+
+[i18n.es.settings.max_sources_per_cycle]
+label = "Máximo de fuentes por ciclo"
+description = "Número máximo de fuentes a procesar por barrido de recopilación"
+
+[i18n.es.settings.track_sentiment]
+label = "Seguimiento de sentimiento"
+description = "Analizar y rastrear las tendencias de sentimiento a lo largo del tiempo"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Collecteur de Renseignements"
+description = "Collecteur autonome de renseignements — surveille toute cible en continu avec détection de changements et graphes de connaissances"
+category = "Données"
+
+[i18n.fr.settings.target_subject]
+label = "Sujet cible"
+description = "Objet de la surveillance (nom d'entreprise, personne, technologie, marché, sujet)"
+
+[i18n.fr.settings.collection_depth]
+label = "Profondeur de collecte"
+description = "Niveau d'approfondissement à chaque cycle de collecte"
+
+[i18n.fr.settings.update_frequency]
+label = "Fréquence de mise à jour"
+description = "Fréquence d'exécution des cycles de collecte"
+
+[i18n.fr.settings.focus_area]
+label = "Domaine d'intérêt"
+description = "Angle d'analyse des renseignements collectés"
+
+[i18n.fr.settings.alert_on_changes]
+label = "Alerte sur changements"
+description = "Publier un événement lorsque des changements significatifs sont détectés"
+
+[i18n.fr.settings.report_format]
+label = "Format de rapport"
+description = "Format de sortie pour les rapports de renseignements"
+
+[i18n.fr.settings.max_sources_per_cycle]
+label = "Sources maximum par cycle"
+description = "Nombre maximum de sources à traiter par cycle de collecte"
+
+[i18n.fr.settings.track_sentiment]
+label = "Suivi du sentiment"
+description = "Analyser et suivre les tendances de sentiment au fil du temps"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Informationssammlungs-Hand"
+description = "Autonomer Informationssammler — überwacht jedes Ziel kontinuierlich mit Änderungserkennung und Wissensgraphen"
+category = "Daten"
+
+[i18n.de.settings.target_subject]
+label = "Zielobjekt"
+description = "Was überwacht werden soll (Firmenname, Person, Technologie, Markt, Thema)"
+
+[i18n.de.settings.collection_depth]
+label = "Sammlungstiefe"
+description = "Wie tief in jedem Sammlungszyklus recherchiert wird"
+
+[i18n.de.settings.update_frequency]
+label = "Aktualisierungshäufigkeit"
+description = "Wie oft Sammlungszyklen ausgeführt werden"
+
+[i18n.de.settings.focus_area]
+label = "Fokusbereich"
+description = "Perspektive für die Analyse der gesammelten Informationen"
+
+[i18n.de.settings.alert_on_changes]
+label = "Warnung bei Änderungen"
+description = "Ein Ereignis veröffentlichen, wenn bedeutende Änderungen erkannt werden"
+
+[i18n.de.settings.report_format]
+label = "Berichtsformat"
+description = "Ausgabeformat für Informationsberichte"
+
+[i18n.de.settings.max_sources_per_cycle]
+label = "Maximale Quellen pro Zyklus"
+description = "Maximale Anzahl der pro Sammlungszyklus zu verarbeitenden Quellen"
+
+[i18n.de.settings.track_sentiment]
+label = "Stimmungsverfolgung"
+description = "Stimmungstrends im Zeitverlauf analysieren und verfolgen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "정보 수집 Hand"
+description = "자율 정보 수집 에이전트 — 대상을 지속적으로 모니터링하며 변경 감지 및 지식 그래프 지원"
+category = "데이터"
+
+[i18n.ko.settings.target_subject]
+label = "모니터링 대상"
+description = "모니터링할 대상 (회사명, 인물, 기술, 시장, 주제)"
+
+[i18n.ko.settings.collection_depth]
+label = "수집 깊이"
+description = "각 수집 주기의 조사 깊이"
+
+[i18n.ko.settings.update_frequency]
+label = "업데이트 빈도"
+description = "수집 스캔 실행 주기"
+
+[i18n.ko.settings.focus_area]
+label = "관심 분야"
+description = "수집된 정보를 분석하는 관점"
+
+[i18n.ko.settings.alert_on_changes]
+label = "변경 알림"
+description = "중요한 변경 사항 감지 시 이벤트 알림 발행"
+
+[i18n.ko.settings.report_format]
+label = "보고서 형식"
+description = "정보 보고서의 출력 형식"
+
+[i18n.ko.settings.max_sources_per_cycle]
+label = "주기당 최대 소스 수"
+description = "수집 스캔당 처리할 최대 소스 수"
+
+[i18n.ko.settings.track_sentiment]
+label = "감성 추적"
+description = "시간에 따른 감성 추세 분석 및 추적"
diff --git a/hands/collector/SKILL.md b/hands/collector/SKILL.md
index 41c03fa..48037fd 100644
--- a/hands/collector/SKILL.md
+++ b/hands/collector/SKILL.md
@@ -269,3 +269,744 @@ Before including data in the knowledge graph, evaluate:
 6. **Track record**: Has this source been reliable in the past?
 
 If a claim fails 3+ checks, downgrade its confidence to "low".
+
+---
+
+## Worked Examples
+
+### Example 1: Competitor Monitoring Campaign
+
+**Scenario**: A B2B SaaS company wants continuous intelligence on three direct competitors: AlphaCloud, BetaStack, and GammaSuite.
+
+**Step 1 — Define targets and collection requirements**
+
+Configure the hand with:
+```
+target_subject: "AlphaCloud, BetaStack, GammaSuite"
+focus_area: competitor
+collection_depth: deep
+update_frequency: daily
+alert_on_changes: true
+track_sentiment: true
+max_sources_per_cycle: 50
+```
+
+Build the initial query set:
+```
+"AlphaCloud" pricing OR plans OR tiers
+"AlphaCloud" product launch OR release OR update
+"AlphaCloud" review site:g2.com OR site:capterra.com
+"AlphaCloud" customer case study
+"AlphaCloud" hiring site:linkedin.com OR site:greenhouse.io
+"switch from AlphaCloud to"
+(repeat for BetaStack and GammaSuite)
+```
+
+**Step 2 — Run first collection cycle**
+
+Execute queries, fetch top results, extract entities:
+```json
+[
+  {"type": "product", "name": "AlphaCloud v4.2", "company": "AlphaCloud", "launch_date": "2025-11-15", "source": "alphacloud.com/blog"},
+  {"type": "person", "name": "Sarah Chen", "role": "New VP Engineering", "company": "BetaStack", "source": "linkedin.com/in/sarachen"},
+  {"type": "event", "name": "GammaSuite Series C", "amount": "$85M", "date": "2025-11-10", "source": "techcrunch.com/2025/11/10/gammasuite-series-c"}
+]
+```
+
+**Step 3 — Build knowledge graph entries**
+
+```
+knowledge_add_entity  type=company  name="AlphaCloud"   industry="SaaS"  funding_stage="Series B"
+knowledge_add_entity  type=product  name="AlphaCloud v4.2"  category="cloud platform"
+knowledge_add_entity  type=person   name="Sarah Chen"    role="VP Engineering"  company="BetaStack"
+knowledge_add_relation source="AlphaCloud" relation="launched" target="AlphaCloud v4.2"
+knowledge_add_relation source="Sarah Chen" relation="works_at" target="BetaStack"
+```
+
+**Step 4 — Process findings into change detection**
+
+| Change | Type | Significance | Action |
+|--------|------|-------------|--------|
+| AlphaCloud released v4.2 with AI features | Product launch | IMPORTANT | Include in report, compare against own roadmap |
+| BetaStack hired VP Engineering from FAANG | Leadership change | IMPORTANT | Track subsequent hiring patterns |
+| GammaSuite raised $85M Series C | Major funding | CRITICAL | Immediate alert, expect aggressive expansion |
+
+**Step 5 — Generate intelligence brief**
+
+```markdown
+# Competitor Intelligence Brief
+**Date**: 2025-11-16 | **Cycle**: 1 | **Sources**: 47
+
+## Priority Changes
+1. [CRITICAL] GammaSuite closed $85M Series C led by Sequoia (TechCrunch, confirmed via Crunchbase)
+2. [IMPORTANT] AlphaCloud shipped v4.2 with AI-assisted workflow builder
+3. [IMPORTANT] BetaStack hired Sarah Chen (ex-Google) as VP Engineering
+
+## Executive Summary
+GammaSuite's large funding round signals intent to accelerate growth — expect increased
+marketing spend and possible M&A activity in the next 6 months. AlphaCloud's v4.2
+introduces direct feature overlap with our AI pipeline. BetaStack's engineering
+leadership hire suggests a product quality push.
+
+## Recommended Actions
+- Review AlphaCloud v4.2 feature parity against our roadmap
+- Monitor GammaSuite job postings for expansion signals
+- Track BetaStack engineering team growth over next 3 cycles
+```
+
+---
+
+### Example 2: Technology Landscape Mapping
+
+**Scenario**: Map the emerging real-time AI inference landscape — track frameworks, adoption signals, key players, and performance benchmarks.
+
+**Step 1 — Define scope and seed entities**
+
+```
+target_subject: "real-time AI inference (vLLM, TensorRT-LLM, Triton, Ollama, llama.cpp)"
+focus_area: technology
+collection_depth: exhaustive
+update_frequency: weekly
+```
+
+Initial seed queries:
+```
+"real-time AI inference" benchmark 2025
+"vLLM" vs "TensorRT-LLM" performance
+"llama.cpp" release changelog
+"AI inference" startup funding 2025
+"edge AI inference" adoption enterprise
+"AI inference" tokens per second benchmark
+site:github.com "vLLM" stars OR contributors
+site:arxiv.org "inference optimization" 2025
+```
+
+**Step 2 — Build entity graph from first sweep**
+
+Entities collected:
+```json
+[
+  {"type": "technology", "name": "vLLM", "version": "0.6.3", "vendor": "UC Berkeley / community", "category": "inference engine"},
+  {"type": "technology", "name": "TensorRT-LLM", "version": "0.15", "vendor": "NVIDIA", "category": "inference engine"},
+  {"type": "company", "name": "Groq", "industry": "AI hardware", "product": "LPU Inference Engine"},
+  {"type": "number", "metric": "tokens_per_second", "value": 523, "context": "Groq Llama 3 70B", "date": "2025-10"},
+  {"type": "number", "metric": "github_stars", "value": 32400, "context": "vLLM", "date": "2025-11"}
+]
+```
+
+Relationships:
+```
+vLLM        --competes_with-->  TensorRT-LLM
+vLLM        --competes_with-->  Ollama
+Groq        --launched-->       "LPU Inference Engine"
+NVIDIA      --launched-->       TensorRT-LLM
+llama.cpp   --uses-->           GGUF format
+```
+
+**Step 3 — Track adoption signals across cycles**
+
+| Signal Type | What to Watch | Detection Method |
+|-------------|--------------|-----------------|
+| GitHub velocity | Stars, forks, contributor count week-over-week | Snapshot comparison |
+| Enterprise adoption | Case studies, "we migrated to X" blog posts | Keyword search |
+| Benchmark results | Tokens/sec, latency, cost-per-token comparisons | Structured extraction |
+| Job postings | "Experience with vLLM" in job descriptions | Job board queries |
+| Conference talks | Accepted papers, keynote mentions | Conference program search |
+
+**Step 4 — Detect trends over 4 weekly cycles**
+
+```
+Cycle 1: vLLM 31,800 stars | TensorRT-LLM 9,200 stars | Ollama 98,000 stars
+Cycle 2: vLLM 32,400 stars | TensorRT-LLM 9,500 stars | Ollama 101,000 stars
+Cycle 3: vLLM 33,500 stars | TensorRT-LLM 9,600 stars | Ollama 103,500 stars
+Cycle 4: vLLM 35,200 stars | TensorRT-LLM 9,700 stars | Ollama 105,000 stars
+
+Trend: vLLM accelerating (+1,700/wk avg → +1,700 last week)
+       Ollama decelerating (+3,000/wk → +1,500/wk)
+       TensorRT-LLM flat (~200/wk)
+```
+
+**Step 5 — Produce technology landscape report**
+
+Include a positioning summary:
+
+| Framework | Strengths | Weaknesses | Momentum | Best For |
+|-----------|-----------|------------|----------|----------|
+| vLLM | High throughput, PagedAttention | GPU-only, complex setup | Accelerating | Production serving at scale |
+| TensorRT-LLM | NVIDIA optimization, low latency | Vendor lock-in, NVIDIA GPUs only | Flat | NVIDIA-stack deployments |
+| Ollama | Simple UX, local-first | Lower throughput, less tunable | Decelerating | Developer experimentation |
+| llama.cpp | CPU support, portable | Manual optimization needed | Steady | Edge/embedded inference |
+| Groq LPU | Extreme speed, low latency | Limited model support, cloud-only | Growing | Latency-critical applications |
+
+---
+
+### Example 3: M&A Signal Detection
+
+**Scenario**: Detect early acquisition indicators for companies in the enterprise observability space (Datadog, Grafana Labs, Chronosphere, Honeycomb).
+
+**Step 1 — Define M&A signal categories**
+
+| Signal Category | Indicators | Weight |
+|----------------|-----------|--------|
+| Executive changes | CEO/CFO departure, new "Chief Strategy Officer", board additions | High |
+| Hiring patterns | Sudden corporate development/M&A roles, legal team expansion | High |
+| Financial signals | Unusual funding, secondary sales, down round, runway concerns | High |
+| Strategic moves | Exclusive partnerships, technology licensing, IP transfers | Medium |
+| Market behavior | Quiet period (no product updates), website changes, domain changes | Medium |
+| Social signals | Founder tone shifts, "exciting news soon" posts, unusual silence | Low |
+
+**Step 2 — Build targeted queries**
+
+```
+"Chronosphere" AND ("acquisition" OR "acquire" OR "acqui-hire" OR "merger")
+"Honeycomb" AND ("strategic alternatives" OR "exploring options" OR "advisors")
+"Grafana Labs" AND ("corporate development" OR "M&A" OR "strategic partnership")
+site:linkedin.com "Chronosphere" "corporate development" OR "M&A"
+site:sec.gov "Honeycomb" OR "Hound Technology"
+"[company]" "quiet period" OR "exciting announcement"
+"[company]" hiring "corporate development" OR "business development director"
+"[company]" board of directors new appointment
+```
+
+**Step 3 — Entity and event extraction**
+
+From collected sources, extract and classify:
+
+```json
+[
+  {
+    "type": "event",
+    "name": "Chronosphere CFO departure",
+    "date": "2025-10-28",
+    "entities": ["Chronosphere", "Lisa Park"],
+    "signal_category": "executive_change",
+    "m_and_a_weight": "high",
+    "source": "linkedin.com/posts/lisapark-farewell"
+  },
+  {
+    "type": "event",
+    "name": "Honeycomb hires Goldman Sachs advisor",
+    "date": "2025-11-02",
+    "entities": ["Honeycomb", "Goldman Sachs"],
+    "signal_category": "financial",
+    "m_and_a_weight": "high",
+    "source": "theinformation.com/articles/honeycomb-advisors"
+  },
+  {
+    "type": "event",
+    "name": "Datadog acquires incident.io",
+    "date": "2025-11-08",
+    "entities": ["Datadog", "incident.io"],
+    "signal_category": "strategic",
+    "m_and_a_weight": "confirmed_event",
+    "source": "datadog.com/blog/incident-io-acquisition"
+  }
+]
+```
+
+**Step 4 — Score composite M&A probability**
+
+Aggregate signals per company over a rolling 90-day window:
+
+```
+Chronosphere:
+  - CFO departed (high)         +3
+  - 2 corp dev job postings     +2
+  - No product release in 90d   +1
+  - Composite score: 6/10 → ELEVATED
+
+Honeycomb:
+  - Hired investment bank       +4
+  - Board added PE partner      +2
+  - Founder "grateful" post     +1
+  - Composite score: 7/10 → HIGH
+
+Grafana Labs:
+  - New enterprise partnerships +1
+  - Active hiring across all    -1 (normal growth, reduces M&A signal)
+  - Composite score: 0/10 → LOW
+```
+
+**Step 5 — Generate M&A signal alert**
+
+```markdown
+# M&A Signal Alert: Enterprise Observability Sector
+**Date**: 2025-11-10 | **Window**: 90 days
+
+## HIGH probability
+- **Honeycomb**: Investment bank engagement + board changes suggest active process.
+  Key evidence: Goldman Sachs advisory (The Information), new PE board member.
+  Likely acquirers: Datadog, Cisco, ServiceNow.
+
+## ELEVATED probability
+- **Chronosphere**: Leadership turnover + hiring freeze + corp dev roles.
+  Key evidence: CFO departure, no product releases, corp dev postings on LinkedIn.
+  Could indicate: acquisition target OR internal restructuring.
+
+## LOW probability
+- **Grafana Labs**: Normal operating patterns, active hiring, regular releases.
+- **Datadog**: Active acquirer (incident.io deal closed), not a target.
+```
+
+---
+
+## Advanced Entity Extraction
+
+### Relationship Mapping from Unstructured Text
+
+Extract relationships by identifying sentence-level patterns that connect two named entities.
+
+**Pattern templates**:
+```
+[Person] joined [Company] as [Role]
+  → relation: works_at, attributes: {role: Role, event: "joined"}
+
+[Company] acquired [Company] for [Amount]
+  → relation: acquired, attributes: {amount: Amount}
+
+[Person] and [Person] co-founded [Company]
+  → relations: founded (x2), co_founded_with (between persons)
+
+[Company] partnered with [Company] to [Purpose]
+  → relation: partnered_with, attributes: {purpose: Purpose}
+
+[Person] left [Company] to join [Company]
+  → relation: left (old), works_at (new), attributes: {event: "departure"}
+```
+
+**Multi-hop relationships**: When A relates to B and B relates to C, infer indirect connections:
+```
+Sarah Chen works_at BetaStack
+BetaStack competes_with AlphaCloud
+→ Indirect: Sarah Chen is key_person_at competitor of AlphaCloud
+```
+
+**Negation detection**: Watch for negated relationships that should NOT be added:
+```
+"Company X denied it was in acquisition talks with Company Y"
+→ Do NOT add acquired relation. Add entity note: "denied acquisition rumor, [date]"
+
+"Former CEO of Company X" → Person left. Mark works_at as ended.
+```
+
+### Temporal Event Extraction (Timeline Construction)
+
+Extract dates and temporal markers to build event timelines.
+
+**Explicit dates**:
+```
+"On March 15, 2025, Acme launched ProductX"
+  → event: product_launch, date: 2025-03-15, entities: [Acme, ProductX]
+```
+
+**Relative dates** (resolve against article publication date):
+```
+"last week"     → pub_date - 7 days
+"earlier today" → pub_date
+"next quarter"  → pub_date + next fiscal quarter boundary
+"in Q3"         → July-September of article's year
+"recently"      → pub_date - 30 days (approximate, confidence: medium)
+```
+
+**Temporal ordering heuristics**:
+```
+"before the acquisition" → event precedes known acquisition date
+"following the launch"   → event follows known launch date
+"amid layoffs"          → event concurrent with layoff period
+```
+
+**Timeline output format**:
+```json
+{
+  "entity": "Acme Corp",
+  "timeline": [
+    {"date": "2025-01-15", "event": "Series B ($40M)", "type": "funding", "confidence": "high"},
+    {"date": "2025-03-20", "event": "Hired new CTO (Jane Lee)", "type": "leadership", "confidence": "high"},
+    {"date": "2025-06-01", "event": "Launched v3.0", "type": "product", "confidence": "high"},
+    {"date": "2025-08-10", "event": "Partnership with CloudCo", "type": "partnership", "confidence": "medium"},
+    {"date": "2025-11-05", "event": "Acquired by BigCorp", "type": "acquisition", "confidence": "high"}
+  ]
+}
+```
+
+### Quantitative Data Extraction
+
+Extract numerical data points with units, context, and time reference.
+
+**Financial figures**:
+```
+Pattern: "[Company] raised $[amount][M/B] in [round]"
+Example: "Acme raised $40M in Series B"
+  → {metric: "funding", value: 40000000, currency: "USD", context: "Series B", entity: "Acme"}
+
+Pattern: "[Company] revenue of $[amount][M/B]"
+Example: "reported annual revenue of $120M"
+  → {metric: "revenue", value: 120000000, currency: "USD", period: "annual", entity: subject}
+```
+
+**Growth rates**:
+```
+Pattern: "[metric] grew [X]% [period]"
+Example: "ARR grew 45% year-over-year"
+  → {metric: "ARR_growth", value: 0.45, period: "YoY", entity: subject}
+
+Pattern: "from [X] to [Y]"
+Example: "headcount grew from 200 to 350"
+  → {metric: "headcount", previous: 200, current: 350, growth: 0.75, entity: subject}
+```
+
+**Headcounts and scale metrics**:
+```
+"[Company] now has [N] employees"
+"[Company] serves [N] customers"
+"[Product] has [N] monthly active users"
+"[Company] operates in [N] countries"
+```
+
+**Extraction validation rules**:
+- Currency amounts without a clear entity reference: discard or mark confidence "low"
+- Growth percentages without a base period: mark confidence "medium"
+- Round numbers (e.g., "about 1,000 employees"): flag as approximate
+- Conflicting numbers from different sources: record both, note discrepancy
+
+### Multi-Source Entity Resolution
+
+When the same entity appears across different sources with variations, deduplicate.
+
+**Company name normalization**:
+```
+"Acme Corp" = "Acme Corporation" = "Acme, Inc." = "ACME" (when context matches)
+"Google" = "Alphabet" (parent) — but keep as separate entities with parent_of relation
+```
+
+**Resolution rules**:
+| Signal | Match Confidence | Action |
+|--------|-----------------|--------|
+| Exact name match | High | Merge immediately |
+| Name + same industry + same location | High | Merge |
+| Abbreviated name + same context | Medium | Merge with note |
+| Similar name, different industry | Low | Keep separate, flag for review |
+| Person same name, different company | Low | Keep separate unless linked by career event |
+
+**Deduplication process**:
+1. Normalize: lowercase, strip legal suffixes, expand abbreviations
+2. Match: compare against existing entity list using normalized form
+3. Verify: check at least one corroborating attribute (industry, location, person association)
+4. Merge: combine attributes, keep all source references, use highest confidence level
+5. Log: record the merge decision for audit
+
+```json
+{
+  "canonical": "entity_acme_corp",
+  "aliases": ["Acme Corp", "Acme Corporation", "Acme, Inc.", "ACME"],
+  "merged_from": ["source_techcrunch_entity_12", "source_linkedin_entity_89"],
+  "merge_confidence": "high",
+  "merge_reason": "exact name + same industry (SaaS) + same HQ (San Francisco)"
+}
+```
+
+---
+
+## Collection Automation Patterns
+
+### Scheduled Collection Workflows
+
+Define collection cadences matched to intelligence needs.
+
+**Daily cycle** (for active competitive monitoring):
+```
+06:00 UTC — Run news queries for all targets (surface scan)
+06:15 UTC — Check social media and forums for overnight mentions
+06:30 UTC — Compare against yesterday's snapshot, flag changes
+06:45 UTC — Generate daily brief, send alerts for CRITICAL items
+```
+
+**Weekly cycle** (for technology landscape and market mapping):
+```
+Monday  — Full source sweep: news, blogs, official sites
+Tuesday — Job board scan: new postings, closed postings, pattern analysis
+Wednesday — Financial data: funding rounds, SEC filings, earnings
+Thursday — Community signals: GitHub activity, forum discussions, reviews
+Friday  — Synthesis: generate weekly report, update entity graph, adjust queries
+```
+
+**Event-triggered cycle** (supplement scheduled runs):
+```
+Trigger: CRITICAL change detected in any cycle
+  → Immediately run deep collection on the affected entity
+  → Expand query set to cover related entities
+  → Generate ad-hoc alert report
+  → Shorten next scheduled cycle interval (e.g., weekly → daily for 7 days)
+```
+
+### Source Prioritization Based on Hit Rate
+
+Track which sources consistently produce actionable intelligence and allocate collection effort accordingly.
+
+**Hit rate calculation**:
+```
+hit_rate = (data_points_extracted / fetches_from_source) over last 10 cycles
+```
+
+**Priority tiers**:
+| Hit Rate | Priority | Collection Behavior |
+|----------|----------|-------------------|
+| > 60% | Tier 1 | Always fetch, process first |
+| 30-60% | Tier 2 | Fetch on every cycle |
+| 10-30% | Tier 3 | Fetch every other cycle |
+| < 10% | Tier 4 | Fetch weekly regardless of cycle frequency |
+| 0% for 5+ cycles | Drop | Remove from active source list, log reason |
+
+**Source performance tracking**:
+```json
+{
+  "source": "techcrunch.com",
+  "total_fetches": 48,
+  "data_points_extracted": 31,
+  "hit_rate": 0.65,
+  "tier": 1,
+  "avg_confidence": "medium-high",
+  "last_hit": "2025-11-15",
+  "best_queries": ["[company] funding", "[company] acquisition"]
+}
+```
+
+### Incremental Collection (Only New/Changed Content)
+
+Avoid re-processing unchanged content across cycles.
+
+**Techniques**:
+1. **URL deduplication**: Maintain a set of already-processed URLs. Skip on subsequent cycles.
+2. **Content hashing**: Hash the extracted text body. If hash matches previous cycle, skip processing.
+3. **Date filtering**: Append date ranges to queries to limit results to new content.
+4. **Pagination cursors**: For APIs and structured sources, store the last-seen ID or timestamp.
+
+**Query date narrowing**:
+```
+Cycle runs daily at 06:00 UTC:
+  "AlphaCloud" after:2025-11-15 before:2025-11-16
+  "AlphaCloud" news past 24 hours
+
+Cycle runs weekly:
+  "AlphaCloud" after:2025-11-08 before:2025-11-15
+```
+
+**State tracking for incremental collection**:
+```json
+{
+  "processed_urls": ["https://example.com/article-1", "..."],
+  "content_hashes": {"url1": "sha256:abc123", "url2": "sha256:def456"},
+  "last_collection_time": "2025-11-15T06:00:00Z",
+  "query_cursors": {
+    "techcrunch_rss": "2025-11-15T05:30:00Z",
+    "github_api_events": "event_id_98765"
+  }
+}
+```
+
+### Alert Trigger Conditions and Escalation Rules
+
+Define when and how to escalate detected changes.
+
+**Trigger conditions**:
+```
+IMMEDIATE ALERT (publish event_publish within the cycle):
+  - Leadership change at target company (CEO, CTO, CFO)
+  - Acquisition or merger announcement
+  - Funding round > $10M
+  - Product discontinuation or major pivot
+  - Regulatory action or legal filing
+  - Data breach or security incident
+
+DAILY DIGEST (batch into next daily report):
+  - New product feature or version release
+  - New partnership announcement
+  - Hiring surge (> 5 new roles in a category)
+  - Pricing or packaging change
+  - Significant sentiment shift (score delta > 2 in one cycle)
+
+WEEKLY SUMMARY (include in weekly report only):
+  - Blog posts and thought leadership
+  - Conference appearances
+  - Minor version updates or patches
+  - Individual job postings
+  - Social media activity within normal range
+```
+
+**Escalation rules**:
+```
+Level 1 — Auto-include in next scheduled report (default for all changes)
+Level 2 — event_publish immediately (for CRITICAL significance changes)
+Level 3 — event_publish + re-run deep collection on affected entity (for M&A, major crises)
+```
+
+**False positive suppression**:
+- Require 2+ independent sources before triggering Level 2 alerts
+- Ignore "rumor" or "speculation" tagged content for immediate alerts
+- If the same alert fired in the previous cycle with no new corroboration, suppress repeat
+
+---
+
+## Analysis Techniques
+
+### Link Analysis (Connection Mapping)
+
+Map the network of relationships between entities to reveal hidden connections, influence patterns, and structural vulnerabilities.
+
+**Building the adjacency map**:
+```
+From the knowledge graph, extract all relations and build:
+
+Nodes: [Acme, BetaCo, GammaSuite, Jane Lee, CloudCo, InvestorX]
+Edges:
+  Acme       --competes_with-->    BetaCo
+  Acme       --partnered_with-->   CloudCo
+  Jane Lee   --works_at-->         Acme
+  Jane Lee   --formerly-->         BetaCo
+  InvestorX  --invested_in-->      Acme
+  InvestorX  --invested_in-->      GammaSuite
+```
+
+**Key metrics to compute**:
+| Metric | Meaning | Use |
+|--------|---------|-----|
+| Degree centrality | Number of direct connections | Identifies most-connected entities |
+| Shared connections | Entities with overlapping relationships | Reveals indirect competition or collaboration |
+| Bridge nodes | Entities connecting otherwise separate clusters | Identifies key influencers or gatekeepers |
+| Cluster density | Ratio of actual to possible connections in a group | Measures how tightly coupled a set of entities is |
+
+**Practical analysis patterns**:
+```
+Investor overlap:
+  InvestorX invested_in Acme AND GammaSuite
+  → Potential: board-level information sharing, future merger pressure
+
+Talent flow:
+  Jane Lee: BetaCo (2020-2024) → Acme (2024-present)
+  3 other engineers: BetaCo → Acme in same period
+  → Pattern: talent drain from BetaCo to Acme, possible IP risk
+
+Supply chain dependency:
+  Acme uses CloudCo infrastructure
+  BetaCo uses CloudCo infrastructure
+  → Shared dependency: CloudCo outage affects both competitors
+```
+
+### Timeline Analysis (Event Sequencing and Pattern Detection)
+
+Arrange extracted events chronologically to detect causal chains, recurring patterns, and anomalous timing.
+
+**Constructing the timeline**:
+```
+2025-01  Acme raises Series B ($40M)
+2025-02  Acme posts 15 engineering roles
+2025-03  Acme hires CTO from Google
+2025-05  Acme acquires small startup (data pipeline tool)
+2025-06  Acme launches v3.0 with data pipeline features
+2025-08  Acme announces enterprise pricing tier
+```
+
+**Pattern detection rules**:
+
+| Pattern | Sequence | Interpretation |
+|---------|----------|---------------|
+| Build-up to launch | Funding → Hiring surge → Leadership hire → Product release | Normal growth execution |
+| Acquisition integration | Acquire company → Quiet period (2-4 months) → Feature launch using acquired tech | Successful integration |
+| Pre-acquisition signals | Advisor hire → Leadership departures → Quiet period → Announcement | Target company being acquired |
+| Distress pattern | Layoffs → Pricing cuts → Leadership change → Pivot or shutdown | Company in trouble |
+| Expansion play | Funding → New market entry → Localized hiring → Regional partnerships | Geographic or vertical expansion |
+
+**Anomaly detection**:
+```
+Expected: Funding round → hiring surge within 60 days
+Observed: Funding round → no hiring after 90 days
+→ Flag: "Post-funding hiring anomaly — possible pivot, internal issues, or stealth project"
+
+Expected: Product launch → marketing push within 30 days
+Observed: Product launch → silence
+→ Flag: "Launch without marketing — possible soft launch, or product issues"
+```
+
+### Trend Detection (Acceleration, Deceleration, Inflection Points)
+
+Track metrics across collection cycles to identify directional shifts.
+
+**Metric tracking format**:
+```json
+{
+  "entity": "Acme Corp",
+  "metric": "job_postings",
+  "series": [
+    {"cycle": 1, "date": "2025-09-01", "value": 12},
+    {"cycle": 2, "date": "2025-09-08", "value": 18},
+    {"cycle": 3, "date": "2025-09-15", "value": 31},
+    {"cycle": 4, "date": "2025-09-22", "value": 45},
+    {"cycle": 5, "date": "2025-09-29", "value": 42}
+  ]
+}
+```
+
+**Trend classification**:
+| Pattern | Detection Rule | Meaning |
+|---------|---------------|---------|
+| Accelerating | Growth rate increasing cycle-over-cycle | Expanding investment in area |
+| Decelerating | Growth rate decreasing but still positive | Approaching saturation or shift in priorities |
+| Inflection point | Direction change (growth → decline or vice versa) | Strategic shift, market event, or external shock |
+| Plateau | Value stable within 10% for 3+ cycles | Steady state, maintenance mode |
+| Spike | Single-cycle jump > 2x previous value | One-time event (launch, announcement, crisis) |
+| Cliff | Single-cycle drop > 50% | Sudden change (layoff, shutdown, policy change) |
+
+**Multi-metric correlation**:
+```
+When two metrics move together, the correlation strengthens the signal:
+
+Acme job_postings: accelerating
+Acme github_commits: accelerating
+→ Corroborated signal: major development push underway
+
+BetaCo job_postings: cliff (-60%)
+BetaCo glassdoor_rating: declining
+→ Corroborated signal: organizational distress
+```
+
+### Competitive Positioning Maps
+
+Synthesize collected intelligence into comparative frameworks.
+
+**Feature parity matrix**:
+| Capability | Acme | BetaCo | GammaSuite | Your Product |
+|-----------|------|--------|------------|-------------|
+| Real-time dashboards | Yes (v2.0+) | Yes | Limited | Yes |
+| AI-powered alerts | Yes (new in v4.2) | No | Beta | Planned Q1 |
+| On-prem deployment | No | Yes | Yes | Yes |
+| SOC2 compliance | Yes | Yes | In progress | Yes |
+| Free tier | No | Yes (limited) | Yes | Yes |
+
+**Market position quadrant** (based on collected metrics):
+```
+                    High Market Share
+                         |
+           Leaders       |      Challengers
+          (Acme)         |      (GammaSuite)
+                         |
+  Low Growth ────────────┼──────────── High Growth
+                         |
+           Declining     |      Emerging
+          (Legacy Co)    |      (BetaCo)
+                         |
+                    Low Market Share
+```
+
+Inputs for positioning:
+- **Market share proxy**: mention frequency, customer count, job posting volume
+- **Growth proxy**: funding recency, hiring rate, product release velocity, GitHub star velocity
+
+**Pricing intelligence table**:
+| Tier | Acme | BetaCo | GammaSuite | Notes |
+|------|------|--------|------------|-------|
+| Free | -- | 5 users | 10 users | BetaCo most restrictive |
+| Team | $15/user/mo | $12/user/mo | $20/user/mo | BetaCo cheapest |
+| Enterprise | Custom | $35/user/mo | Custom | BetaCo only one with public enterprise pricing |
+| Notable changes | Raised Team tier 20% in Q3 | Unchanged 12 months | New tier added Q4 | Acme pricing pressure |
+
+Track pricing changes across cycles — pricing increases signal confidence, decreases signal competitive pressure or churn concerns.
diff --git a/hands/devops/HAND.toml b/hands/devops/HAND.toml
index 5527f79..bfd0e4b 100644
--- a/hands/devops/HAND.toml
+++ b/hands/devops/HAND.toml
@@ -490,6 +490,265 @@ token_consumption = "high"
 default_active = false
 activation_warning = "DevOps hand runs continuously and monitors infrastructure, consuming tokens."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "DevOps Hand"
 description = "自主 DevOps 工程师——CI/CD 管理、基础设施监控、部署自动化和事件响应"
+category = "开发"
+
+[i18n.zh.settings.infrastructure]
+label = "基础设施类型"
+description = "主要基础设施平台"
+
+[i18n.zh.settings.ci_platform]
+label = "CI/CD 平台"
+description = "主要 CI/CD 平台"
+
+[i18n.zh.settings.monitoring_focus]
+label = "监控重点"
+description = "主要监控和告警的关注方向"
+
+[i18n.zh.settings.auto_monitor]
+label = "自动监控"
+description = "自动监控基础设施并在出现问题时告警"
+
+[i18n.zh.settings.check_interval]
+label = "健康检查间隔"
+description = "自动健康检查的执行频率"
+
+[i18n.zh.settings.service_urls]
+label = "服务 URL"
+description = "要监控的 URL 列表，以逗号分隔（例如 https://api.example.com/health,https://app.example.com）"
+
+[i18n.zh.settings.alert_on_failure]
+label = "故障告警"
+description = "健康检查失败时发布事件通知"
+
+[i18n.zh.settings.rollback_strategy]
+label = "回滚策略"
+description = "部署失败时的默认回滚方式"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "将部署和基础设施操作加入队列供审核，而非直接执行"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "DevOps Hand"
+description = "自律型DevOpsエンジニア——CI/CD管理、インフラ監視、デプロイ自動化、インシデント対応"
+category = "開発"
+
+[i18n.ja.settings.infrastructure]
+label = "インフラタイプ"
+description = "主要なインフラプラットフォーム"
+
+[i18n.ja.settings.ci_platform]
+label = "CI/CDプラットフォーム"
+description = "主要なCI/CDプラットフォーム"
+
+[i18n.ja.settings.monitoring_focus]
+label = "監視の重点"
+description = "監視とアラートの主な対象分野"
+
+[i18n.ja.settings.auto_monitor]
+label = "自動監視"
+description = "インフラを自動監視し、問題発生時にアラートを出す"
+
+[i18n.ja.settings.check_interval]
+label = "ヘルスチェック間隔"
+description = "自動ヘルスチェックの実行間隔"
+
+[i18n.ja.settings.service_urls]
+label = "サービスURL"
+description = "監視対象のURL一覧（カンマ区切り、例: https://api.example.com/health,https://app.example.com）"
+
+[i18n.ja.settings.alert_on_failure]
+label = "障害アラート"
+description = "ヘルスチェック失敗時にイベント通知を発行する"
+
+[i18n.ja.settings.rollback_strategy]
+label = "ロールバック戦略"
+description = "デプロイ失敗時のデフォルトのロールバック方法"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "デプロイやインフラ操作を直接実行せず、レビュー用キューに追加する"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de DevOps"
+description = "Ingeniero DevOps autónomo — gestión de CI/CD, monitoreo de infraestructura, automatización de despliegues y respuesta a incidentes"
+category = "Desarrollo"
+
+[i18n.es.settings.infrastructure]
+label = "Tipo de infraestructura"
+description = "Plataforma de infraestructura principal"
+
+[i18n.es.settings.ci_platform]
+label = "Plataforma CI/CD"
+description = "Plataforma principal de CI/CD"
+
+[i18n.es.settings.monitoring_focus]
+label = "Enfoque de monitoreo"
+description = "Área principal de monitoreo y alertas"
+
+[i18n.es.settings.auto_monitor]
+label = "Monitoreo automático"
+description = "Monitorear automáticamente la infraestructura y alertar ante problemas"
+
+[i18n.es.settings.check_interval]
+label = "Intervalo de comprobación de salud"
+description = "Con qué frecuencia ejecutar las comprobaciones de salud automatizadas"
+
+[i18n.es.settings.service_urls]
+label = "URLs de servicios"
+description = "Lista de URLs a monitorear separadas por comas (ej. https://api.example.com/health,https://app.example.com)"
+
+[i18n.es.settings.alert_on_failure]
+label = "Alertar ante fallos"
+description = "Publicar eventos cuando las comprobaciones de salud fallen"
+
+[i18n.es.settings.rollback_strategy]
+label = "Estrategia de reversión"
+description = "Enfoque de reversión predeterminado para despliegues fallidos"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Poner acciones de despliegue e infraestructura en cola para revisión en lugar de ejecutarlas directamente"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand DevOps"
+description = "Ingénieur DevOps autonome — gestion CI/CD, surveillance d'infrastructure, automatisation des déploiements et réponse aux incidents"
+category = "Développement"
+
+[i18n.fr.settings.infrastructure]
+label = "Type d'infrastructure"
+description = "Plateforme d'infrastructure principale"
+
+[i18n.fr.settings.ci_platform]
+label = "Plateforme CI/CD"
+description = "Plateforme CI/CD principale"
+
+[i18n.fr.settings.monitoring_focus]
+label = "Axe de surveillance"
+description = "Domaine principal de surveillance et d'alerte"
+
+[i18n.fr.settings.auto_monitor]
+label = "Surveillance automatique"
+description = "Surveiller automatiquement l'infrastructure et alerter en cas de problèmes"
+
+[i18n.fr.settings.check_interval]
+label = "Intervalle de vérification de santé"
+description = "Fréquence d'exécution des vérifications de santé automatisées"
+
+[i18n.fr.settings.service_urls]
+label = "URLs des services"
+description = "Liste d'URLs à surveiller séparées par des virgules (ex. https://api.example.com/health,https://app.example.com)"
+
+[i18n.fr.settings.alert_on_failure]
+label = "Alerte en cas d'échec"
+description = "Publier des événements lorsque les vérifications de santé échouent"
+
+[i18n.fr.settings.rollback_strategy]
+label = "Stratégie de retour en arrière"
+description = "Approche de retour en arrière par défaut pour les déploiements échoués"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Mettre les actions de déploiement et d'infrastructure en file d'attente pour révision au lieu de les exécuter directement"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "DevOps-Hand"
+description = "Autonomer DevOps-Ingenieur — CI/CD-Verwaltung, Infrastrukturüberwachung, Deployment-Automatisierung und Incident-Response"
+category = "Entwicklung"
+
+[i18n.de.settings.infrastructure]
+label = "Infrastrukturtyp"
+description = "Primäre Infrastrukturplattform"
+
+[i18n.de.settings.ci_platform]
+label = "CI/CD-Plattform"
+description = "Primäre CI/CD-Plattform"
+
+[i18n.de.settings.monitoring_focus]
+label = "Überwachungsschwerpunkt"
+description = "Hauptbereich für Überwachung und Alarme"
+
+[i18n.de.settings.auto_monitor]
+label = "Automatische Überwachung"
+description = "Infrastruktur automatisch überwachen und bei Problemen alarmieren"
+
+[i18n.de.settings.check_interval]
+label = "Gesundheitscheck-Intervall"
+description = "Ausführungshäufigkeit der automatisierten Gesundheitschecks"
+
+[i18n.de.settings.service_urls]
+label = "Service-URLs"
+description = "Kommagetrennte Liste der zu überwachenden URLs (z.B. https://api.example.com/health,https://app.example.com)"
+
+[i18n.de.settings.alert_on_failure]
+label = "Warnung bei Ausfall"
+description = "Ereignisse veröffentlichen, wenn Gesundheitschecks fehlschlagen"
+
+[i18n.de.settings.rollback_strategy]
+label = "Rollback-Strategie"
+description = "Standard-Rollback-Ansatz für fehlgeschlagene Deployments"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Deployment- und Infrastrukturaktionen zur Überprüfung in die Warteschlange stellen, anstatt sie direkt auszuführen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "DevOps Hand"
+description = "자율 DevOps 엔지니어 — CI/CD 관리, 인프라 모니터링, 배포 자동화 및 인시던트 대응"
+category = "개발"
+
+[i18n.ko.settings.infrastructure]
+label = "인프라 유형"
+description = "주요 인프라 플랫폼"
+
+[i18n.ko.settings.ci_platform]
+label = "CI/CD 플랫폼"
+description = "주요 CI/CD 플랫폼"
+
+[i18n.ko.settings.monitoring_focus]
+label = "모니터링 중점"
+description = "주요 모니터링 및 알림 방향"
+
+[i18n.ko.settings.auto_monitor]
+label = "자동 모니터링"
+description = "인프라를 자동으로 모니터링하고 문제 발생 시 알림"
+
+[i18n.ko.settings.check_interval]
+label = "상태 점검 간격"
+description = "자동 상태 점검 실행 주기"
+
+[i18n.ko.settings.service_urls]
+label = "서비스 URL"
+description = "모니터링할 URL 목록 (쉼표로 구분, 예: https://api.example.com/health,https://app.example.com)"
+
+[i18n.ko.settings.alert_on_failure]
+label = "장애 알림"
+description = "상태 점검 실패 시 이벤트 알림 발행"
+
+[i18n.ko.settings.rollback_strategy]
+label = "롤백 전략"
+description = "배포 실패 시 기본 롤백 방식"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "배포 및 인프라 작업을 직접 실행하지 않고 대기열에 추가하여 검토"
diff --git a/hands/devops/SKILL.md b/hands/devops/SKILL.md
index b4403a3..47fd3ad 100644
--- a/hands/devops/SKILL.md
+++ b/hands/devops/SKILL.md
@@ -330,3 +330,541 @@ for domain in api.example.com app.example.com; do
   echo "$domain: $expiry"
 done
 ```
+
+---
+
+## Worked Examples
+
+### Example 1: Zero-Downtime Deployment Pipeline
+
+Full lifecycle from code merge to production traffic switch with rollback safety.
+
+**Phases**: CI build and push image -> deploy to Green -> health-gate -> traffic switch -> monitor -> done (or rollback).
+
+**Traffic switch script** (the critical step):
+```bash
+#!/bin/bash
+set -euo pipefail
+# Record current slot for rollback, then switch
+CURRENT=$(kubectl get svc myapp-active -n production -o jsonpath='{.spec.selector.slot}')
+echo "$CURRENT" > /tmp/rollback-slot
+kubectl patch svc myapp-active -n production -p '{"spec":{"selector":{"slot":"green"}}}'
+# Verify
+sleep 5 && curl -sf https://api.example.com/api/health | jq '.version'
+```
+
+**Rollback**: read `/tmp/rollback-slot`, patch the service selector back, verify health.
+
+**Decision flowchart**:
+```
+Code merged to main
+  |
+  v
+CI build + test ----[FAIL]----> Block merge, notify author
+  |
+  [PASS]
+  v
+Deploy to Green
+  |
+  v
+Health checks ------[FAIL]----> Alert on-call, keep Blue active
+  |
+  [PASS]
+  v
+Switch traffic to Green
+  |
+  v
+Monitor 15 min -----[ERROR SPIKE]----> Rollback to Blue, open incident
+  |
+  [STABLE]
+  v
+Mark Green as new Blue, done
+```
+
+---
+
+### Example 2: Production Incident Response
+
+Walkthrough of a real-world SEV1 incident: API latency spike caused by a database connection pool exhaustion.
+
+**Timeline**
+
+| Time (UTC) | Event | Actor |
+|------------|-------|-------|
+| 14:02 | PagerDuty alert: P95 latency > 2s on `/api/orders` | Monitoring |
+| 14:04 | On-call acknowledges, opens incident channel `#inc-2025-0312` | On-call engineer |
+| 14:06 | Check dashboard: request queue depth spiking, error rate at 12% | On-call engineer |
+| 14:10 | Identify DB connection pool at 100% utilization | On-call engineer |
+| 14:12 | Find long-running query from analytics job (started 13:55) | On-call engineer |
+| 14:14 | Kill the runaway query, pool starts draining | On-call engineer |
+| 14:18 | Latency returns to normal, error rate drops to 0.2% | Monitoring |
+| 14:20 | Incident mitigated, continue monitoring | On-call engineer |
+| 14:45 | Root cause confirmed: analytics cron job without query timeout | Investigation |
+| 15:00 | Incident resolved, post-mortem scheduled | Incident commander |
+
+**Detection -- Alerting rules that fired**
+
+```yaml
+# Prometheus alerting rule
+groups:
+  - name: api-latency
+    rules:
+      - alert: HighAPILatency
+        expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket{job="api"}[5m])) > 2
+        for: 2m
+        labels:
+          severity: critical
+        annotations:
+          summary: "P95 latency above 2s for 2+ minutes"
+          runbook: "https://wiki.internal/runbooks/high-latency"
+```
+
+**Triage -- Quick diagnosis commands**
+
+```bash
+# 1. Check if it's a specific endpoint or global
+curl -s "http://prometheus:9090/api/v1/query?query=topk(5,rate(http_request_duration_seconds_sum[5m])/rate(http_request_duration_seconds_count[5m]))" | jq '.data.result[] | {endpoint: .metric.handler, avg_latency: .value[1]}'
+
+# 2. Check database connection pool
+psql -c "SELECT count(*) as total, state FROM pg_stat_activity GROUP BY state;"
+
+# 3. Find the blocking query
+psql -c "SELECT pid, now() - query_start AS duration, query
+         FROM pg_stat_activity
+         WHERE state = 'active' AND now() - query_start > interval '1 minute'
+         ORDER BY duration DESC LIMIT 5;"
+```
+
+**Mitigation -- Kill the offending query**
+
+```bash
+# Kill the long-running query by PID
+psql -c "SELECT pg_terminate_backend(12345);"
+
+# Verify pool is recovering
+watch -n 2 'psql -t -c "SELECT count(*) FROM pg_stat_activity WHERE state = '\''active'\'';"'
+```
+
+**Prevention -- Fix applied after incident**
+
+```sql
+-- Set statement timeout for analytics role
+ALTER ROLE analytics_readonly SET statement_timeout = '300s';
+```
+
+```yaml
+# Add connection pool monitoring alert
+- alert: DBConnectionPoolNearCapacity
+  expr: pg_stat_activity_count / pg_settings_max_connections > 0.8
+  for: 1m
+  labels:
+    severity: warning
+  annotations:
+    summary: "DB connection pool above 80% capacity"
+```
+
+**Post-mortem action items**:
+- [ ] Add `statement_timeout` to all non-interactive database roles
+- [ ] Add connection pool utilization alerts (threshold: 80%)
+- [ ] Move analytics queries to read replica
+- [ ] Add circuit breaker to API when pool utilization exceeds 90%
+
+---
+
+### Example 3: Infrastructure Scaling Event
+
+Scaling a Kubernetes deployment in response to sustained load increase.
+
+**Phase 1 -- Alert triggers**
+
+```
+Alert: HighCPUUtilization
+  Condition: avg(cpu_usage) > 80% for 10 minutes
+  Current:   87% across 3 pods
+  Namespace: production
+  Deployment: order-service
+```
+
+**Phase 2 -- Capacity analysis**
+
+```bash
+kubectl top pods -l app=order-service -n production   # Per-pod CPU/memory
+kubectl describe nodes | grep -A 5 "Allocated resources"  # Node headroom
+kubectl get hpa order-service -n production            # Current HPA state
+# Result: 87% CPU across 3 pods, 842 req/s (2x baseline)
+```
+
+**Phase 3 -- Scaling decision matrix**
+
+| Metric | Current | Target | Action |
+|--------|---------|--------|--------|
+| CPU usage | 87% | < 70% | Scale out |
+| Request rate | 842/s | - | 2x normal, sustained |
+| Memory | 258Mi avg | 512Mi limit | Headroom OK |
+| Pod count | 3 | 6 (estimated) | Double replicas |
+| Node capacity | 72% | < 85% | Sufficient for 6 pods |
+
+**Phase 4 -- Implement scaling**
+
+```bash
+# Option A: Manual scale (immediate)
+kubectl scale deployment order-service -n production --replicas=6
+
+# Option B: Adjust HPA for sustained load (preferred)
+kubectl patch hpa order-service -n production \
+  -p '{"spec":{"minReplicas":5,"maxReplicas":15}}'
+
+# Monitor rollout
+kubectl rollout status deployment/order-service -n production
+
+# Watch pods come up
+kubectl get pods -l app=order-service -n production -w
+```
+
+**Phase 5 -- Verify scaling**
+
+Confirm via `kubectl top pods` (CPU should drop to ~45% per pod), check P95 latency is back below SLO, and verify error rate < 1%.
+
+**Post-scaling actions**:
+- [ ] Investigate root cause of traffic increase (marketing event? bot traffic? organic growth?)
+- [ ] Update capacity planning spreadsheet
+- [ ] If sustained, adjust resource requests/limits and HPA baselines
+- [ ] Set calendar reminder to review and potentially scale down in 48h
+
+---
+
+## Observability Deep Dive
+
+### Structured Logging
+
+Use consistent JSON log format across all services for machine-parseable aggregation.
+
+**Log format standard**: JSON with required fields: `timestamp`, `level`, `service`, `trace_id`, `span_id`, `request_id`, `message`. Add `error` and `context` (structured key-value) as needed.
+
+**Log levels -- when to use each**:
+
+| Level | Purpose | Example | Persisted |
+|-------|---------|---------|-----------|
+| `error` | Requires human attention | Payment processing failed | 90 days |
+| `warn` | Degraded but recoverable | Retry succeeded on 2nd attempt | 30 days |
+| `info` | Business-significant events | Order placed, user logged in | 14 days |
+| `debug` | Developer troubleshooting | Cache hit/miss, query timing | 3 days |
+| `trace` | Fine-grained flow tracking | Function entry/exit, variable state | 1 day (sampled) |
+
+**Correlation IDs**: generate `X-Request-ID` at API gateway, propagate through all downstream calls. Query across services by filtering on `request_id.keyword` in Elasticsearch/OpenSearch.
+
+### Distributed Tracing
+
+**Core concepts**: A Trace is the end-to-end request path. Each service call is a Span with timing. Spans nest to show the call tree (e.g., API Gateway -> Order Service -> DB Query + Payment Service -> Stripe API).
+
+**OpenTelemetry propagation**: `traceparent: 00-<trace-id>-<span-id>-<flags>`, `tracestate: vendor=value`.
+
+**Useful trace queries (Jaeger/Tempo)**:
+```bash
+curl -s "http://jaeger:16686/api/traces?service=order-service&minDuration=1s&limit=20"  # Slow traces
+curl -s "http://jaeger:16686/api/traces?service=order-service&tags=error%3Dtrue&limit=20"  # Error traces
+```
+
+### Alerting Best Practices
+
+**Avoid alert fatigue -- rules of thumb**:
+- Every alert must have a runbook link
+- Every alert must be actionable (if no one needs to act, it is a log, not an alert)
+- Group related alerts to avoid notification storms
+- Use inhibition rules: if the cluster is down, suppress per-pod alerts
+
+**SLO-based alerting (burn rate)**:
+```yaml
+# SLO: 99.9% availability = 43.2 min/month error budget
+# Fast burn (exhausts budget in 2h): error_ratio > 14.4 * 0.001 for 2m -> critical
+# Slow burn (exhausts budget in 3d): error_ratio > 3 * 0.001 for 15m -> warning
+groups:
+  - name: slo-burn-rate
+    rules:
+      - alert: SLOBurnRateCritical
+        expr: sum(rate(http_requests_total{code=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) > (14.4 * 0.001)
+        for: 2m
+        labels: { severity: critical }
+      - alert: SLOBurnRateWarning
+        expr: sum(rate(http_requests_total{code=~"5.."}[1h])) / sum(rate(http_requests_total[1h])) > (3 * 0.001)
+        for: 15m
+        labels: { severity: warning }
+```
+
+**Runbook template**: Each alert runbook should cover: what the alert means (one sentence), impact scope, diagnosis steps (dashboard + commands), mitigation (quick fix vs proper fix), and escalation path (who to contact after 15 min).
+
+### Metrics Collection Patterns
+
+**RED Method (request-scoped services)**:
+
+| Metric | What | PromQL Example |
+|--------|------|----------------|
+| **R**ate | Requests per second | `sum(rate(http_requests_total[5m]))` |
+| **E**rrors | Failed requests per second | `sum(rate(http_requests_total{code=~"5.."}[5m]))` |
+| **D**uration | Latency distribution | `histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))` |
+
+**USE Method (infrastructure resources)**:
+
+| Metric | What | Example Check |
+|--------|------|---------------|
+| **U**tilization | % time resource is busy | `avg(rate(node_cpu_seconds_total{mode!="idle"}[5m]))` |
+| **S**aturation | Queue depth / backlog | `node_load1 / count(node_cpu_seconds_total{mode="idle"})` |
+| **E**rrors | Error event count | `rate(node_disk_io_time_weighted_seconds_total[5m])` |
+
+**When to use which**:
+- RED for services that handle requests (APIs, web servers, message consumers)
+- USE for infrastructure (CPU, memory, disk, network interfaces, queues)
+- Combine both for a complete picture
+
+---
+
+## Security Operations
+
+### Secret Management
+
+**Principles**:
+- Never store secrets in source code, environment variables (in Dockerfiles), or container images
+- Use a secrets manager (Vault, AWS Secrets Manager, K8s Secrets with encryption at rest)
+- Rotate secrets on a schedule and immediately after any suspected compromise
+- Audit all secret access
+
+**Vault pattern -- inject secrets at runtime**:
+```bash
+# Store a secret
+vault kv put secret/myapp/db \
+  username="app_user" \
+  password="$(openssl rand -base64 32)"
+
+# Read a secret (application startup)
+vault kv get -format=json secret/myapp/db | jq -r '.data.data.password'
+
+# Enable audit logging
+vault audit enable file file_path=/var/log/vault-audit.log
+```
+
+**Kubernetes secrets -- from Vault using sidecar injector**:
+
+Annotate the pod template with `vault.hashicorp.com/agent-inject: "true"`, specify the role and secret path. The Vault agent sidecar renders secrets to `/vault/secrets/` and the app sources them at startup. Key annotations: `agent-inject-secret-<name>` for the path, `agent-inject-template-<name>` for the rendering template.
+
+**Secret rotation checklist**:
+- [ ] Generate new secret value
+- [ ] Update secret in secrets manager
+- [ ] Restart/reload affected services (rolling, not all-at-once)
+- [ ] Verify services authenticate with new secret
+- [ ] Revoke the old secret value
+- [ ] Confirm no services are still using the old secret
+
+### Container Security Scanning
+
+```bash
+# Scan image for vulnerabilities (Trivy) -- fail CI on critical
+trivy image --exit-code 1 --severity CRITICAL registry.example.com/myapp:$CI_COMMIT_SHA
+
+# Scan K8s cluster for misconfigurations
+trivy k8s --report summary cluster
+```
+
+**Dockerfile security essentials**: use pinned base image tags (not `:latest`), run as non-root (`USER app`), copy only needed files, never bake secrets into image layers.
+
+### Network Security Policies
+
+**Kubernetes NetworkPolicy -- default deny with explicit allow**:
+```yaml
+# Default deny all ingress, then allow specific paths
+apiVersion: networking.k8s.io/v1
+kind: NetworkPolicy
+metadata:
+  name: allow-gateway-to-orders
+  namespace: production
+spec:
+  podSelector:
+    matchLabels: { app: order-service }
+  ingress:
+    - from:
+        - podSelector:
+            matchLabels: { app: api-gateway }
+      ports:
+        - { protocol: TCP, port: 8080 }
+```
+
+Apply a `default-deny-ingress` policy (empty `podSelector`, `policyTypes: [Ingress]`) per namespace first, then layer allow rules on top.
+
+### Compliance as Code
+
+**Policy enforcement with OPA/Gatekeeper**: use `K8sRequiredResources` constraints to enforce `limits.cpu`, `limits.memory`, `requests.cpu`, `requests.memory` on all pods in production namespaces.
+
+**Quick compliance audit commands**:
+```bash
+# Find pods without resource limits
+kubectl get pods -A -o json | jq -r '.items[] | select(.spec.containers[].resources.limits == null) | .metadata.namespace + "/" + .metadata.name'
+# Find containers running as root
+kubectl get pods -A -o json | jq -r '.items[] | select(.spec.containers[].securityContext.runAsNonRoot != true) | .metadata.namespace + "/" + .metadata.name'
+# Find ingress without TLS
+kubectl get ingress -A -o json | jq -r '.items[] | select(.spec.tls == null) | .metadata.namespace + "/" + .metadata.name'
+```
+
+---
+
+## Automation Patterns
+
+### Auto-Remediation
+
+**Restart on OOM (Kubernetes)**:
+```yaml
+# Built-in: set resource limits and let K8s handle OOM restarts
+apiVersion: apps/v1
+kind: Deployment
+spec:
+  template:
+    spec:
+      containers:
+        - name: myapp
+          resources:
+            limits:
+              memory: "512Mi"
+            requests:
+              memory: "256Mi"
+          # Liveness probe: restart if unhealthy
+          livenessProbe:
+            httpGet:
+              path: /health
+              port: 8080
+            initialDelaySeconds: 10
+            periodSeconds: 10
+            failureThreshold: 3
+```
+
+**Scale on load (HPA with custom metrics)**:
+```yaml
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+metadata:
+  name: order-service
+spec:
+  scaleTargetRef: { apiVersion: apps/v1, kind: Deployment, name: order-service }
+  minReplicas: 3
+  maxReplicas: 20
+  behavior:
+    scaleUp:   { stabilizationWindowSeconds: 60,  policies: [{ type: Percent, value: 50, periodSeconds: 60 }] }
+    scaleDown: { stabilizationWindowSeconds: 300, policies: [{ type: Percent, value: 25, periodSeconds: 120 }] }
+  metrics:
+    - type: Resource
+      resource: { name: cpu, target: { type: Utilization, averageUtilization: 70 } }
+    - type: Pods
+      pods: { metric: { name: http_requests_per_second }, target: { type: AverageValue, averageValue: "1000" } }
+```
+
+**Rotate secrets on expiry (CronJob)**:
+
+Use a K8s CronJob (e.g., monthly `"0 2 1 * *"`) with a `secret-rotator` service account that: generates new password -> updates Vault -> alters DB role password -> triggers rolling restart via `kubectl rollout restart`.
+
+### GitOps Workflow
+
+**Repository as source of truth**:
+```
+infrastructure-repo/
+  |-- apps/
+  |   |-- order-service/
+  |   |   |-- deployment.yaml
+  |   |   |-- service.yaml
+  |   |   |-- hpa.yaml
+  |   |   `-- kustomization.yaml
+  |   `-- payment-service/
+  |       |-- deployment.yaml
+  |       `-- kustomization.yaml
+  |-- base/
+  |   |-- namespace.yaml
+  |   |-- network-policies.yaml
+  |   `-- resource-quotas.yaml
+  `-- overlays/
+      |-- staging/
+      |   `-- kustomization.yaml
+      `-- production/
+          `-- kustomization.yaml
+```
+
+**Reconciliation loop (ArgoCD application)**:
+```yaml
+apiVersion: argoproj.io/v1alpha1
+kind: Application
+metadata:
+  name: order-service
+  namespace: argocd
+spec:
+  project: default
+  source:
+    repoURL: https://github.com/org/infrastructure-repo.git
+    targetRevision: main
+    path: apps/order-service
+  destination: { server: "https://kubernetes.default.svc", namespace: production }
+  syncPolicy:
+    automated: { prune: true, selfHeal: true }
+    syncOptions: [CreateNamespace=true]
+    retry: { limit: 3, backoff: { duration: 5s, factor: 2, maxDuration: 3m } }
+```
+
+**GitOps deployment flow**:
+```
+Developer pushes image tag update to infrastructure-repo
+  |
+  v
+ArgoCD detects drift between git state and cluster state
+  |
+  v
+ArgoCD syncs: applies manifests from git to cluster
+  |
+  v
+Kubernetes rolls out new pods
+  |
+  v
+ArgoCD verifies health (readiness probes pass)
+  |
+  [HEALTHY] --> Done
+  [DEGRADED] --> ArgoCD marks sync as failed, alerts on-call
+```
+
+### Database Backup and Restore
+
+**Automated backup (PostgreSQL)** -- run via cron `0 */6 * * *`:
+```bash
+#!/bin/bash
+set -euo pipefail
+TIMESTAMP=$(date +%Y%m%d_%H%M%S)
+DB_NAME="production"
+BACKUP_DIR="/backups/postgres"
+
+pg_dump -Fc -Z 9 "$DB_NAME" > "${BACKUP_DIR}/${DB_NAME}_${TIMESTAMP}.dump"
+aws s3 cp "${BACKUP_DIR}/${DB_NAME}_${TIMESTAMP}.dump" \
+  "s3://backups-bucket/postgres/" --storage-class STANDARD_IA
+find "$BACKUP_DIR" -name "*.dump" -mtime +30 -delete
+```
+
+**Restore procedure**:
+```bash
+#!/bin/bash
+set -euo pipefail
+BACKUP_FILE=$1  # e.g., "production_20250315_060000.dump"
+RESTORE_DB="production_restore"
+
+aws s3 cp "s3://backups-bucket/postgres/$BACKUP_FILE" /tmp/restore.dump
+psql -c "DROP DATABASE IF EXISTS $RESTORE_DB;" && psql -c "CREATE DATABASE $RESTORE_DB;"
+pg_restore -d "$RESTORE_DB" -j 4 --no-owner /tmp/restore.dump
+# Verify: check row counts on key tables, then clean up
+rm /tmp/restore.dump
+```
+
+### Disaster Recovery Runbook Template
+
+**Recovery Objectives**: Define RTO (e.g., 1 hour) and RPO (e.g., 6 hours) per service.
+
+**Prerequisites**: backup storage access, Terraform state access, DNS management access, stakeholder comms channel.
+
+| Scenario | Key Steps |
+|----------|-----------|
+| **Single service failure** | Check pod status -> restart deployment -> if fails, `kubectl rollout undo` -> verify health |
+| **Database failure** | `pg_isready` -> promote replica (or restore from backup) -> update connection strings -> verify data integrity |
+| **Full region outage** | Confirm via provider status page -> notify stakeholders -> switch DNS to DR region -> verify traffic -> failback when primary recovers |
+
+**Communication template**: Subject `[INCIDENT] Service -- Status`. Body: what happened, impact, current status, ETA, next update time.
+
+**Post-recovery checklist**: health checks passing, data integrity verified, monitoring restored, backups resumed, incident report filed, post-mortem scheduled within 48h.
diff --git a/hands/lead/HAND.toml b/hands/lead/HAND.toml
index b25f6b4..ea855e0 100644
--- a/hands/lead/HAND.toml
+++ b/hands/lead/HAND.toml
@@ -380,6 +380,265 @@ token_consumption = "medium"
 default_active = false
 activation_warning = "Lead hand runs continuously and generates leads on schedule, consuming tokens."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "线索生成 Hand"
 description = "自主线索生成——按计划发现、充实并交付合格的潜在客户"
+category = "数据"
+
+[i18n.zh.settings.target_industry]
+label = "目标行业"
+description = "重点关注的行业垂直领域（例如 SaaS、金融科技、医疗健康、电子商务）"
+
+[i18n.zh.settings.target_role]
+label = "目标职位"
+description = "要触达的决策者头衔（例如 CTO、工程副总裁、产品负责人）"
+
+[i18n.zh.settings.company_size]
+label = "公司规模"
+description = "按公司规模筛选线索"
+
+[i18n.zh.settings.lead_source]
+label = "线索来源"
+description = "发现线索的主要方式"
+
+[i18n.zh.settings.output_format]
+label = "输出格式"
+description = "报告交付格式"
+
+[i18n.zh.settings.leads_per_report]
+label = "每份报告线索数"
+description = "每份报告中包含的线索数量"
+
+[i18n.zh.settings.delivery_schedule]
+label = "交付计划"
+description = "生成和交付线索报告的时间安排"
+
+[i18n.zh.settings.geo_focus]
+label = "地域重点"
+description = "优先关注的地理区域（例如美国、欧洲、亚太、全球）"
+
+[i18n.zh.settings.enrichment_depth]
+label = "信息丰富度"
+description = "对每条线索收集多少上下文信息"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "리드 생성 Hand"
+description = "자율 리드 생성 — 일정에 따라 적격 리드를 탐색, 보강 및 전달"
+category = "데이터"
+
+[i18n.ko.settings.target_industry]
+label = "대상 산업"
+description = "집중할 산업 분야 (예: SaaS, 핀테크, 헬스케어, 이커머스)"
+
+[i18n.ko.settings.target_role]
+label = "대상 직책"
+description = "타겟할 의사결정자 직함 (예: CTO, 엔지니어링 VP, 프로덕트 총괄)"
+
+[i18n.ko.settings.company_size]
+label = "회사 규모"
+description = "회사 규모별 리드 필터링"
+
+[i18n.ko.settings.lead_source]
+label = "리드 소스"
+description = "리드를 발굴하는 주요 방법"
+
+[i18n.ko.settings.output_format]
+label = "출력 형식"
+description = "보고서 전달 형식"
+
+[i18n.ko.settings.leads_per_report]
+label = "보고서당 리드 수"
+description = "각 보고서에 포함할 리드 수"
+
+[i18n.ko.settings.delivery_schedule]
+label = "전달 일정"
+description = "리드 보고서 생성 및 전달 시간"
+
+[i18n.ko.settings.geo_focus]
+label = "지역 중점"
+description = "우선적으로 집중할 지역 (예: 미국, 유럽, 아시아 태평양, 글로벌)"
+
+[i18n.ko.settings.enrichment_depth]
+label = "보강 깊이"
+description = "리드당 수집할 컨텍스트 정보의 수준"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "リード生成 Hand"
+description = "自律型リード生成エージェント——スケジュールに基づき見込み客を発見・情報付加・配信"
+category = "データ"
+
+[i18n.ja.settings.target_industry]
+label = "ターゲット業界"
+description = "注力する業界バーティカル（例: SaaS、フィンテック、ヘルスケア、EC）"
+
+[i18n.ja.settings.target_role]
+label = "ターゲット職種"
+description = "アプローチする意思決定者の肩書き（例: CTO、VP Engineering、プロダクト責任者）"
+
+[i18n.ja.settings.company_size]
+label = "企業規模"
+description = "企業規模でリードをフィルタリング"
+
+[i18n.ja.settings.lead_source]
+label = "リードソース"
+description = "リードを発見する主な方法"
+
+[i18n.ja.settings.output_format]
+label = "出力形式"
+description = "レポートの配信形式"
+
+[i18n.ja.settings.leads_per_report]
+label = "レポートあたりのリード数"
+description = "各レポートに含めるリードの数"
+
+[i18n.ja.settings.delivery_schedule]
+label = "配信スケジュール"
+description = "リードレポートの生成・配信タイミング"
+
+[i18n.ja.settings.geo_focus]
+label = "地域フォーカス"
+description = "優先する地理的リージョン（例: 米国、欧州、APAC、グローバル）"
+
+[i18n.ja.settings.enrichment_depth]
+label = "情報付加の深さ"
+description = "リードごとに収集するコンテキスト情報の量"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Generación de Leads"
+description = "Generación autónoma de leads — descubre, enriquece y entrega leads cualificados según un calendario"
+category = "Datos"
+
+[i18n.es.settings.target_industry]
+label = "Industria objetivo"
+description = "Vertical de industria en la que enfocarse (ej. SaaS, fintech, salud, e-commerce)"
+
+[i18n.es.settings.target_role]
+label = "Rol objetivo"
+description = "Títulos de tomadores de decisiones a los que dirigirse (ej. CTO, VP de Ingeniería, Director de Producto)"
+
+[i18n.es.settings.company_size]
+label = "Tamaño de empresa"
+description = "Filtrar leads por tamaño de empresa"
+
+[i18n.es.settings.lead_source]
+label = "Fuente de leads"
+description = "Método principal para descubrir leads"
+
+[i18n.es.settings.output_format]
+label = "Formato de salida"
+description = "Formato de entrega del informe"
+
+[i18n.es.settings.leads_per_report]
+label = "Leads por informe"
+description = "Número de leads a incluir en cada informe"
+
+[i18n.es.settings.delivery_schedule]
+label = "Calendario de entrega"
+description = "Cuándo generar y entregar los informes de leads"
+
+[i18n.es.settings.geo_focus]
+label = "Enfoque geográfico"
+description = "Región geográfica a priorizar (ej. EE.UU., Europa, Asia-Pacífico, global)"
+
+[i18n.es.settings.enrichment_depth]
+label = "Profundidad de enriquecimiento"
+description = "Cuánto contexto recopilar por cada lead"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Génération de Prospects"
+description = "Génération autonome de prospects — découvre, enrichit et livre des prospects qualifiés selon un calendrier"
+category = "Données"
+
+[i18n.fr.settings.target_industry]
+label = "Secteur cible"
+description = "Secteur d'activité cible (ex. SaaS, fintech, santé, e-commerce)"
+
+[i18n.fr.settings.target_role]
+label = "Poste cible"
+description = "Titres de décideurs à cibler (ex. CTO, VP Engineering, Directeur Produit)"
+
+[i18n.fr.settings.company_size]
+label = "Taille d'entreprise"
+description = "Filtrer les prospects par taille d'entreprise"
+
+[i18n.fr.settings.lead_source]
+label = "Source de prospects"
+description = "Méthode principale de découverte des prospects"
+
+[i18n.fr.settings.output_format]
+label = "Format de sortie"
+description = "Format de livraison des rapports"
+
+[i18n.fr.settings.leads_per_report]
+label = "Prospects par rapport"
+description = "Nombre de prospects à inclure dans chaque rapport"
+
+[i18n.fr.settings.delivery_schedule]
+label = "Calendrier de livraison"
+description = "Quand générer et livrer les rapports de prospects"
+
+[i18n.fr.settings.geo_focus]
+label = "Focus géographique"
+description = "Région géographique prioritaire (ex. USA, Europe, Asie-Pacifique, Mondial)"
+
+[i18n.fr.settings.enrichment_depth]
+label = "Profondeur d'enrichissement"
+description = "Niveau d'informations contextuelles à collecter par prospect"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Lead-Generierungs-Hand"
+description = "Autonome Lead-Generierung — entdeckt, bereichert und liefert qualifizierte Leads nach Zeitplan"
+category = "Daten"
+
+[i18n.de.settings.target_industry]
+label = "Zielbranche"
+description = "Branchenvertikale für den Fokus (z.B. SaaS, Fintech, Gesundheitswesen, E-Commerce)"
+
+[i18n.de.settings.target_role]
+label = "Zielposition"
+description = "Titel der Entscheidungsträger (z.B. CTO, VP Engineering, Produktleiter)"
+
+[i18n.de.settings.company_size]
+label = "Unternehmensgröße"
+description = "Leads nach Unternehmensgröße filtern"
+
+[i18n.de.settings.lead_source]
+label = "Lead-Quelle"
+description = "Primäre Methode zur Lead-Entdeckung"
+
+[i18n.de.settings.output_format]
+label = "Ausgabeformat"
+description = "Berichtslieferformat"
+
+[i18n.de.settings.leads_per_report]
+label = "Leads pro Bericht"
+description = "Anzahl der Leads pro Bericht"
+
+[i18n.de.settings.delivery_schedule]
+label = "Lieferzeitplan"
+description = "Wann Lead-Berichte generiert und geliefert werden"
+
+[i18n.de.settings.geo_focus]
+label = "Geografischer Fokus"
+description = "Priorisierte geografische Region (z.B. USA, Europa, Asien-Pazifik, Global)"
+
+[i18n.de.settings.enrichment_depth]
+label = "Anreicherungstiefe"
+description = "Umfang der pro Lead gesammelten Kontextinformationen"
diff --git a/hands/lead/SKILL.md b/hands/lead/SKILL.md
index e12adf1..6fb4b26 100644
--- a/hands/lead/SKILL.md
+++ b/hands/lead/SKILL.md
@@ -62,6 +62,88 @@ site:builtwith.com "[company]"
 7. **News articles** — recent activity, reputation
 8. **Social media** — engagement, company culture
 
+### Industry-Specific Search Patterns
+
+#### SaaS / Technology
+```
+# Company directories
+site:g2.com/products "[category]"
+site:capterra.com "[category] software"
+site:producthunt.com "[product type]" "[year]"
+"[category] software" site:crunchbase.com/organization
+
+# Tech stack signals
+site:stackshare.io "[technology]" decisions
+site:builtwith.com/websites/[technology]
+
+# Growth signals
+"[company] SOC 2" OR "[company] ISO 27001"        — enterprise readiness
+"[company] API" OR "[company] integration"          — platform maturity
+"[company] case study" OR "[company] customer story" — traction evidence
+```
+
+#### Healthcare
+```
+# Directories & registries
+site:healthcareittoday.com "[company]"
+"digital health companies" site:crunchbase.com
+"health tech" "[city/state]" site:angellist.co
+"HIPAA compliant" "[category] software"
+
+# Regulatory signals
+"[company] FDA clearance" OR "[company] 510(k)"
+"[company] HIPAA" OR "[company] HITRUST"
+"[company] clinical trial" site:clinicaltrials.gov
+```
+
+#### Financial Services
+```
+# Directories & databases
+site:fintechmagazine.com "top" "[category]"
+"fintech companies" "[region]" site:crunchbase.com
+"banking technology" OR "insurtech" site:cbinsights.com
+
+# Compliance signals
+"[company] SOX compliance" OR "[company] PCI DSS"
+"[company] banking license" OR "[company] money transmitter"
+"[company] Series [A/B/C]" "fintech"
+```
+
+#### E-commerce
+```
+# Directories & tools
+site:apps.shopify.com "[category]"
+site:store.bigcommerce.com "[category]"
+"ecommerce brands" "[niche]" site:2pm.com OR site:modernretail.co
+
+# Revenue signals
+"[company] GMV" OR "[company] ARR"
+"[company] warehouse" OR "[company] fulfillment center"
+"[brand] DTC" OR "[brand] direct to consumer"
+```
+
+#### Manufacturing
+```
+# Directories
+site:thomasnet.com "[product category]"
+"manufacturing companies" "[city/state]" site:mfg.com
+"industrial [category]" site:dnb.com
+
+# Modernization signals
+"[company] Industry 4.0" OR "[company] smart factory"
+"[company] ERP" OR "[company] digital transformation"
+"[company] ISO 9001" OR "[company] ISO 14001"
+```
+
+#### Industry Source Quick Reference
+| Vertical | Primary Directories | Key Signal Keywords |
+|----------|-------------------|---------------------|
+| SaaS/Tech | G2, Capterra, ProductHunt, Crunchbase | "API launch", "SOC 2", "Series X" |
+| Healthcare | HealthcareIT, ClinicalTrials.gov | "HIPAA", "FDA", "clinical trial" |
+| Financial Services | CBInsights, Crunchbase | "PCI DSS", "banking license", "Series X" |
+| E-commerce | Shopify App Store, ModernRetail | "GMV", "DTC", "fulfillment" |
+| Manufacturing | ThomasNet, MFG.com | "Industry 4.0", "ISO 9001", "ERP" |
+
 ---
 
 ## Lead Enrichment Patterns
@@ -145,6 +227,63 @@ Accessibility (15 points max):
 
 ---
 
+## Lead Qualification Frameworks
+
+### BANT Framework
+
+Use BANT to quickly qualify leads during or after enrichment. Each dimension maps to data you can discover through web research.
+
+| Dimension | Question | Research Signals |
+|-----------|----------|-----------------|
+| **Budget** | Can they afford the solution? | Funding rounds, revenue estimates, job postings for related roles, pricing tier of current tools |
+| **Authority** | Is this person a decision-maker? | Title seniority (VP+, C-level, Director), reports to CEO/CTO, listed on "Leadership" page |
+| **Need** | Do they have the problem you solve? | Job postings mentioning the pain point, tech stack gaps, competitor tool usage, complaints on forums |
+| **Timeline** | Is there urgency to buy? | Contract renewals, compliance deadlines, product launches, recent leadership changes |
+
+#### BANT Scoring Overlay
+Apply these modifiers on top of the base lead score:
+```
+Budget confirmed (funding, revenue signal):   +5
+Authority confirmed (VP+ or C-level):         +5
+Need confirmed (pain point evidence):         +5
+Timeline confirmed (urgency signal):          +5
+                                     Max bonus: +20
+```
+
+### MEDDIC Framework
+
+Use MEDDIC for complex / enterprise sales qualification where longer deal cycles demand deeper research.
+
+| Dimension | Definition | What to Look For |
+|-----------|-----------|-----------------|
+| **Metrics** | Quantifiable outcomes the buyer cares about | Case studies they publish, KPIs in job postings, analyst reports, earnings calls |
+| **Economic Buyer** | Person with budget authority to sign | CFO, CEO, VP Finance, or "Head of Procurement" listed on team pages |
+| **Decision Criteria** | Factors they use to evaluate vendors | RFP documents, vendor comparison blog posts, compliance requirements, review site feedback |
+| **Decision Process** | Steps from evaluation to purchase | Procurement team presence, legal/compliance review cycles, pilot program mentions |
+| **Identify Pain** | Specific problems driving the purchase | Support forums, Glassdoor reviews, social media complaints, analyst reports on industry challenges |
+| **Champion** | Internal advocate for your solution | Conference speakers, blog authors, open-source contributors, people who engage with your content |
+
+#### MEDDIC Research Checklist
+```
+For each enterprise lead, attempt to discover:
+[ ] At least one quantifiable metric they care about
+[ ] The economic buyer's name and title
+[ ] 2+ decision criteria (compliance, performance, price, integration)
+[ ] Whether they run formal procurement (RFP, committee)
+[ ] 1+ specific pain point with evidence
+[ ] A potential internal champion (engaged user, tech advocate)
+```
+
+### Choosing Between BANT and MEDDIC
+| Scenario | Recommended Framework |
+|----------|----------------------|
+| SMB / startup targets, short sales cycle | BANT |
+| Enterprise targets, $100K+ deal size | MEDDIC |
+| Mixed list with varied company sizes | BANT first pass, MEDDIC for A-grade enterprise leads |
+| Time-constrained research | BANT (faster to assess) |
+
+---
+
 ## Deduplication Strategies
 
 ### Matching Algorithm
@@ -212,6 +351,166 @@ Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
 
 ---
 
+## Worked Examples
+
+### Example 1: Fintech SaaS Series A/B Companies (50-200 Employees)
+
+**Objective**: Find 10 SaaS companies in the fintech space with 50-200 employees that recently raised Series A or B.
+
+#### Step 1 — Define ICP
+```
+Industry:       Fintech / Financial Technology
+Company size:   50-200 employees (SMB)
+Funding stage:  Series A or Series B (raised within last 18 months)
+Geography:      United States (primary), UK/EU (secondary)
+Decision-maker: VP Engineering, CTO, or Head of Product
+Pain points:    Scaling infrastructure, compliance automation, developer tooling
+```
+
+#### Step 2 — Execute Search Queries
+```
+# Primary discovery queries
+"fintech" "series A" OR "series B" site:crunchbase.com/organization
+"fintech startup" "raised" "$" "2025" OR "2024" site:techcrunch.com
+site:news.crunchbase.com "fintech" "series A" OR "series B"
+
+# Employee count validation
+"fintech" "50" OR "100" OR "150" "employees" site:linkedin.com/company
+site:builtin.com/companies/fintech "51-200 employees"
+
+# Growth signals
+"fintech" hiring "senior engineer" OR "staff engineer" site:linkedin.com/jobs
+"fintech startup" "SOC 2" OR "PCI DSS" — compliance-ready = selling to banks
+```
+
+#### Step 3 — Enrich and Score Each Lead
+```
+For each discovered company, gather:
+  1. Company website → About page → leadership team, employee count
+  2. Crunchbase profile → funding amount, date, investors, total raised
+  3. LinkedIn company page → exact employee count, recent hires
+  4. Job boards → open roles (signals growth and tech stack)
+  5. Press releases → product launches, partnerships, customer wins
+
+Scoring example for "PayFlow Inc":
+  ICP Match:         25/30 (fintech ✓, 130 employees ✓, US ✓, CTO found ✓, no geography bonus)
+  Growth Signals:    18/20 (Series B $18M ✓, hiring 8 engineers ✓, product launch ✓)
+  Enrichment:        15/20 (LinkedIn ✓, full company data ✓, tech stack ✓, no direct email)
+  Recency:           15/15 (funding announced 3 weeks ago)
+  Accessibility:     10/15 (company contact form, CTO LinkedIn)
+  TOTAL:             83/100 → Grade A
+```
+
+#### Step 4 — Final Output (top 3 of 10)
+| # | Name | Title | Company | Employees | Funding | Score | Key Signal |
+|---|------|-------|---------|-----------|---------|-------|------------|
+| 1 | Sarah Chen | CTO | PayFlow Inc | 130 | Series B, $18M | 83 | Funded 3 weeks ago, hiring 8 engineers |
+| 2 | Marcus Rivera | VP Engineering | LendStack | 85 | Series A, $12M | 78 | Launched API platform Q4, SOC 2 certified |
+| 3 | Priya Patel | Head of Product | ComplianceAI | 62 | Series A, $8M | 75 | Hiring product + eng, regulatory focus |
+
+---
+
+### Example 2: Enterprise AI/ML Decision-Makers
+
+**Objective**: Identify decision-makers at enterprise companies (500+ employees) that are actively adopting AI/ML tools.
+
+#### Step 1 — Define ICP
+```
+Industry:       Any (cross-industry AI adoption)
+Company size:   500+ employees (Enterprise)
+Signals:        Active AI/ML adoption (hiring, projects, tool procurement)
+Geography:      North America
+Decision-maker: VP/Director of Data Science, Head of AI/ML, CTO, Chief Data Officer
+Pain points:    ML model deployment, data pipeline scaling, AI governance
+```
+
+#### Step 2 — Execute Search Queries
+```
+# Identify companies investing in AI
+"head of AI" OR "VP data science" OR "chief data officer" hiring site:linkedin.com
+"[company] machine learning" "team" OR "department" site:linkedin.com/company
+"AI adoption" OR "ML platform" "enterprise" site:venturebeat.com OR site:techcrunch.com
+
+# Conference and community signals
+"speaker" "machine learning" OR "AI" site:neurips.cc OR site:icml.cc
+"[company] MLOps" OR "[company] AI infrastructure" site:github.com
+
+# Budget and procurement signals
+"AI budget" OR "ML tools" RFP site:gov OR site:rfpdb.com
+"[company] partnership" "AI" OR "machine learning" press release
+```
+
+#### Step 3 — Multi-Source Enrichment
+```
+For enterprise targets, cross-reference at least 3 sources per lead:
+
+  Source 1: LinkedIn
+    → Title confirmation, tenure, reporting structure
+    → Company employee count, growth rate
+    → Recent posts about AI/ML topics (champion signal)
+
+  Source 2: Company website + press
+    → AI/ML team page, published case studies
+    → Press releases about AI initiatives
+    → Open positions on careers page
+
+  Source 3: Community / conferences
+    → Conference talks (NeurIPS, ICML, KDD, MLOps World)
+    → GitHub contributions (open-source ML projects)
+    → Blog posts or whitepapers on AI strategy
+
+  MEDDIC qualification pass:
+    Metrics:          "Reduced model deployment time by 60%" (from case study)
+    Economic Buyer:   Chief Data Officer, reports to CEO
+    Decision Criteria: SOC 2 compliance, on-prem option, Python SDK
+    Decision Process:  Procurement committee, 90-day eval period
+    Pain:             "Manual ML pipeline taking 3 weeks per model" (job posting)
+    Champion:         Sr. ML Engineer who spoke at MLOps World about tooling gaps
+```
+
+#### Step 4 — Final Output (top 3)
+| # | Name | Title | Company | Employees | Score | Qualification |
+|---|------|-------|---------|-----------|-------|---------------|
+| 1 | David Kim | Chief Data Officer | GlobalRetail Corp | 3,200 | 91 | MEDDIC 5/6: metrics, buyer, criteria, pain, champion |
+| 2 | Lisa Zhang | VP Data Science | HealthFirst Systems | 1,800 | 86 | MEDDIC 4/6: buyer, criteria, pain, champion |
+| 3 | James O'Brien | Director of AI | MegaBank Financial | 12,000 | 80 | MEDDIC 4/6: metrics, buyer, decision process, pain |
+
+---
+
+### Example 3: Quick-Turn SMB List Build
+
+**Objective**: Build a 20-lead list of SMB e-commerce brands using Shopify that might need an email marketing tool. Time budget: 30 minutes.
+
+#### Abbreviated Flow
+```
+ICP (quick):
+  Industry: E-commerce / DTC brands
+  Size: 10-100 employees
+  Platform: Shopify
+  Signal: Active store, social media presence, no advanced email tool detected
+
+Search queries (5 minutes):
+  site:myshopify.com "[niche]"
+  "[niche] brand" "shopify" site:linkedin.com/company
+  site:apps.shopify.com/reviews "[competitor email tool]" — negative reviews = opportunity
+  "DTC brands" "[niche]" "founded 2022" OR "founded 2023"
+
+Enrichment (15 minutes, per lead):
+  1. Shopify store URL → active? recent products?
+  2. LinkedIn company page → employee count, founded year
+  3. BuiltWith → check for existing email/marketing tools
+  4. Instagram/TikTok → follower count (engagement proxy)
+
+Scoring (5 minutes):
+  Use simplified scoring: ICP match (40%) + Growth signals (30%) + Reachability (30%)
+  Skip MEDDIC for SMB — use BANT quick-check instead
+
+Output (5 minutes):
+  Deliver as CSV with columns: Brand, URL, Employees, Platform, Current Email Tool, Score, Contact
+```
+
+---
+
 ## Compliance & Ethics
 
 ### DO
@@ -233,3 +532,81 @@ Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
 - Keep lead data in local files only — never exfiltrate
 - Mark stale leads (>90 days without activity) for review
 - Provide clear data export in all supported formats
+
+---
+
+## Common Pitfalls
+
+### 1. Outdated Data
+**Problem**: Company details change fast — people change jobs, startups pivot, funding info ages.
+**Mitigation**:
+- Verify every lead against at least 2 sources, and prefer sources updated within the last 90 days
+- Flag any data point older than 6 months as "needs re-verification"
+- Check LinkedIn tenure: if a contact joined their current role <3 months ago, they may not have budget authority yet
+
+### 2. Over-Relying on a Single Source
+**Problem**: Crunchbase has gaps in non-US companies. LinkedIn employee counts lag. News articles are biased toward funded companies.
+**Mitigation**:
+- Always cross-reference: Crunchbase funding + LinkedIn headcount + company website team page
+- Use at least 2 sources for employee count (the numbers often diverge by 20-30%)
+- If a company has zero press coverage, check industry-specific directories rather than discarding it
+
+### 3. Ignoring Enrichment Quality
+**Problem**: A lead list with 50 names but only 10 have titles and 5 have company size data is not actionable.
+**Mitigation**:
+- Set a minimum enrichment threshold before including a lead (e.g., must have: name + title + company + at least one signal)
+- Track an "enrichment completeness" percentage per lead
+- Return to partially-enriched leads in a second pass rather than shipping incomplete data
+
+### 4. Vanity List Sizes
+**Problem**: Delivering 100 leads when only 15 are qualified wastes the user's time and erodes trust.
+**Mitigation**:
+- Better to deliver 10 A-grade leads than 50 C-grade leads
+- Always sort by score descending and include a clear recommendation on where to draw the cut-off line
+- If the target count cannot be met at acceptable quality, say so: "Found 7 leads meeting all criteria; 13 additional leads are partial matches"
+
+### 5. Confusing Company Name Variants
+**Problem**: "Stripe, Inc.", "Stripe", and "Stripe Payments Europe Ltd" can appear as three separate leads.
+**Mitigation**:
+- Always normalize company names before deduplication (see Normalization Rules above)
+- Match on website domain as the primary key — it is the most stable identifier
+- Be especially careful with common words as company names ("Bolt", "Block", "Square")
+
+### 6. Mistaking Hiring Activity for Purchase Intent
+**Problem**: A company hiring engineers does not necessarily mean they are buying your product.
+**Mitigation**:
+- Hiring is a **growth signal**, not a **purchase signal** — score it accordingly (contributor, not decisive)
+- Look for more direct signals: RFPs, vendor comparison blog posts, demo requests, event attendance
+- Combine hiring data with tech stack analysis: hiring a "Salesforce Admin" means Salesforce budget exists
+
+### 7. Neglecting Negative Signals
+**Problem**: Focusing only on positive signals and missing red flags.
+**Mitigation**:
+- Check for layoffs, lawsuits, or executive departures — these reduce lead quality
+- A company that just went through a 30% layoff is unlikely to approve new vendor spend
+- Apply negative score modifiers:
+```
+Recent layoffs (>10% headcount):     -10
+Lawsuit / regulatory action:         -5
+Executive turnover (CEO/CTO left):   -5
+Declining web traffic (per SimilarWeb): -3
+```
+
+### 8. Skipping the ICP Step
+**Problem**: Jumping straight into search without a clear ICP produces scattered, low-quality results.
+**Mitigation**:
+- Always define the ICP **before** the first search query, even if it takes 5 extra minutes
+- Write the ICP down explicitly (industry, size, geography, role, pain point, budget signal)
+- Revisit and tighten the ICP after the first 10 leads if results are too broad
+
+### Pitfall Severity Quick Reference
+| Pitfall | Severity | Frequency | Fix Effort |
+|---------|----------|-----------|------------|
+| Outdated data | High | Very common | Medium (multi-source verification) |
+| Single source reliance | High | Common | Low (add 1-2 extra sources) |
+| Poor enrichment quality | Medium | Common | Medium (set thresholds, second pass) |
+| Vanity list sizes | Medium | Common | Low (enforce scoring cut-off) |
+| Company name variants | Medium | Very common | Low (normalize + domain match) |
+| Hiring != purchase intent | Low | Occasional | Low (adjust scoring weight) |
+| Ignoring negative signals | High | Common | Medium (add negative modifiers) |
+| Skipping ICP | High | Occasional | Low (5-minute discipline) |
diff --git a/hands/linkedin/HAND.toml b/hands/linkedin/HAND.toml
index ab515b5..a9e8b9d 100644
--- a/hands/linkedin/HAND.toml
+++ b/hands/linkedin/HAND.toml
@@ -394,6 +394,241 @@ frequency = "daily"
 token_consumption = "medium"
 default_active = false
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "LinkedIn Hand"
 description = "自主 LinkedIn 管理——个人资料优化、内容创作、人脉拓展和职业互动"
+category = "通信"
+
+[i18n.zh.settings.content_style]
+label = "内容风格"
+description = "LinkedIn 帖子的语气和风格"
+
+[i18n.zh.settings.post_frequency]
+label = "发布频率"
+description = "创建和发布内容的频率"
+
+[i18n.zh.settings.content_topics]
+label = "内容主题"
+description = "要创作内容的主题（逗号分隔，例如 AI、领导力、创业）"
+
+[i18n.zh.settings.auto_engage]
+label = "自动互动"
+description = "自动对人脉网络中的相关帖子点赞和评论"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "将帖子加入队列等待审核，而非直接发布"
+
+[i18n.zh.settings.hashtag_count]
+label = "话题标签数量"
+description = "每篇帖子包含的话题标签数量"
+
+[i18n.zh.settings.target_audience]
+label = "目标受众"
+description = "内容的主要目标受众"
+
+[i18n.zh.settings.language]
+label = "语言"
+description = "帖子和互动使用的语言"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "LinkedIn Hand"
+description = "自律型LinkedInマネージャー——プロフィール最適化、コンテンツ作成、ネットワーキング、プロフェッショナルエンゲージメント"
+category = "コミュニケーション"
+
+[i18n.ja.settings.content_style]
+label = "コンテンツスタイル"
+description = "LinkedIn投稿の語調とスタイル"
+
+[i18n.ja.settings.post_frequency]
+label = "投稿頻度"
+description = "コンテンツの作成・投稿の頻度"
+
+[i18n.ja.settings.content_topics]
+label = "コンテンツトピック"
+description = "作成するコンテンツのトピック（カンマ区切り、例: AI、リーダーシップ、スタートアップ）"
+
+[i18n.ja.settings.auto_engage]
+label = "自動エンゲージメント"
+description = "ネットワーク内の関連投稿に自動でいいねやコメントをする"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "投稿を直接公開せず、レビュー用キューに追加する"
+
+[i18n.ja.settings.hashtag_count]
+label = "ハッシュタグ数"
+description = "各投稿に含めるハッシュタグの数"
+
+[i18n.ja.settings.target_audience]
+label = "ターゲットオーディエンス"
+description = "コンテンツの主なターゲット層"
+
+[i18n.ja.settings.language]
+label = "言語"
+description = "投稿とエンゲージメントに使用する言語"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de LinkedIn"
+description = "Gestor autónomo de LinkedIn — optimización de perfil, creación de contenido, networking y engagement profesional"
+category = "Comunicación"
+
+[i18n.es.settings.content_style]
+label = "Estilo de contenido"
+description = "Voz y tono para las publicaciones de LinkedIn"
+
+[i18n.es.settings.post_frequency]
+label = "Frecuencia de publicación"
+description = "Con qué frecuencia crear y publicar contenido"
+
+[i18n.es.settings.content_topics]
+label = "Temas de contenido"
+description = "Temas sobre los que crear contenido (separados por comas, ej. IA, liderazgo, startups)"
+
+[i18n.es.settings.auto_engage]
+label = "Engagement automático"
+description = "Dar like y comentar automáticamente en publicaciones relevantes de tu red"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Poner publicaciones en cola para revisión en lugar de publicarlas directamente"
+
+[i18n.es.settings.hashtag_count]
+label = "Cantidad de hashtags"
+description = "Número de hashtags a incluir por publicación"
+
+[i18n.es.settings.target_audience]
+label = "Audiencia objetivo"
+description = "Audiencia principal para tu contenido"
+
+[i18n.es.settings.language]
+label = "Idioma"
+description = "Idioma para publicaciones e interacciones"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand LinkedIn"
+description = "Gestionnaire LinkedIn autonome — optimisation de profil, création de contenu, réseautage et engagement professionnel"
+category = "Communication"
+
+[i18n.fr.settings.content_style]
+label = "Style de contenu"
+description = "Ton et style pour les publications LinkedIn"
+
+[i18n.fr.settings.post_frequency]
+label = "Fréquence de publication"
+description = "Fréquence de création et de publication de contenu"
+
+[i18n.fr.settings.content_topics]
+label = "Sujets de contenu"
+description = "Sujets sur lesquels créer du contenu (séparés par des virgules, ex. IA, leadership, startups)"
+
+[i18n.fr.settings.auto_engage]
+label = "Engagement automatique"
+description = "Aimer et commenter automatiquement les publications pertinentes de votre réseau"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Mettre les publications en file d'attente pour révision au lieu de les publier directement"
+
+[i18n.fr.settings.hashtag_count]
+label = "Nombre de hashtags"
+description = "Nombre de hashtags à inclure par publication"
+
+[i18n.fr.settings.target_audience]
+label = "Public cible"
+description = "Public principal pour votre contenu"
+
+[i18n.fr.settings.language]
+label = "Langue"
+description = "Langue pour les publications et les interactions"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "LinkedIn-Hand"
+description = "Autonomer LinkedIn-Manager — Profiloptimierung, Content-Erstellung, Networking und professionelles Engagement"
+category = "Kommunikation"
+
+[i18n.de.settings.content_style]
+label = "Inhaltsstil"
+description = "Ton und Stil für LinkedIn-Beiträge"
+
+[i18n.de.settings.post_frequency]
+label = "Veröffentlichungshäufigkeit"
+description = "Wie oft Inhalte erstellt und veröffentlicht werden"
+
+[i18n.de.settings.content_topics]
+label = "Inhaltsthemen"
+description = "Themen für die Content-Erstellung (kommagetrennt, z.B. KI, Führung, Startups)"
+
+[i18n.de.settings.auto_engage]
+label = "Automatisches Engagement"
+description = "Relevante Beiträge im Netzwerk automatisch liken und kommentieren"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Beiträge zur Überprüfung in die Warteschlange stellen, anstatt sie direkt zu veröffentlichen"
+
+[i18n.de.settings.hashtag_count]
+label = "Anzahl Hashtags"
+description = "Anzahl der Hashtags pro Beitrag"
+
+[i18n.de.settings.target_audience]
+label = "Zielgruppe"
+description = "Primäre Zielgruppe für Ihre Inhalte"
+
+[i18n.de.settings.language]
+label = "Sprache"
+description = "Sprache für Beiträge und Interaktionen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "LinkedIn Hand"
+description = "자율 LinkedIn 관리 — 프로필 최적화, 콘텐츠 제작, 네트워킹 및 전문적 소통"
+category = "커뮤니케이션"
+
+[i18n.ko.settings.content_style]
+label = "콘텐츠 스타일"
+description = "LinkedIn 게시물의 어조와 스타일"
+
+[i18n.ko.settings.post_frequency]
+label = "게시 빈도"
+description = "콘텐츠를 작성하고 게시하는 주기"
+
+[i18n.ko.settings.content_topics]
+label = "콘텐츠 주제"
+description = "콘텐츠를 작성할 주제 (쉼표로 구분, 예: AI, 리더십, 스타트업)"
+
+[i18n.ko.settings.auto_engage]
+label = "자동 소통"
+description = "네트워크 내 관련 게시물에 자동으로 좋아요 및 댓글"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "게시물을 직접 게시하지 않고 대기열에 추가하여 검토"
+
+[i18n.ko.settings.hashtag_count]
+label = "해시태그 수"
+description = "게시물당 포함할 해시태그 수"
+
+[i18n.ko.settings.target_audience]
+label = "대상 독자"
+description = "콘텐츠의 주요 대상 독자"
+
+[i18n.ko.settings.language]
+label = "언어"
+description = "게시물 및 소통에 사용하는 언어"
diff --git a/hands/linkedin/SKILL.md b/hands/linkedin/SKILL.md
index 084990d..a136372 100644
--- a/hands/linkedin/SKILL.md
+++ b/hands/linkedin/SKILL.md
@@ -218,3 +218,753 @@ Before posting any content, classify it:
 - Credit sources and tag collaborators
 - Disclose affiliations when discussing products or services
 - Respect intellectual property and copyright
+
+---
+
+## Advanced API Patterns
+
+### Image Post Creation (Media Upload Flow)
+
+Posting an image requires a 3-step flow: register upload, upload binary, then create post.
+
+**Step 1 -- Register the upload**:
+```bash
+curl -s -X POST "https://api.linkedin.com/rest/images?action=initializeUpload" \
+  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "Content-Type: application/json" \
+  -H "LinkedIn-Version: 202405" \
+  -d '{
+    "initializeUploadRequest": {
+      "owner": "urn:li:person:MEMBER_ID"
+    }
+  }'
+```
+Response contains `uploadUrl` and `image` URN (e.g., `urn:li:image:C4E...`).
+
+**Step 2 -- Upload the binary**:
+```bash
+curl -s -X PUT "$UPLOAD_URL" \
+  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "Content-Type: image/png" \
+  --data-binary "@/path/to/image.png"
+```
+
+**Step 3 -- Create post with image**:
+```bash
+curl -s -X POST "https://api.linkedin.com/rest/posts" \
+  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "Content-Type: application/json" \
+  -H "LinkedIn-Version: 202405" \
+  -d '{
+    "author": "urn:li:person:MEMBER_ID",
+    "lifecycleState": "PUBLISHED",
+    "commentary": "Check out our Q3 results!",
+    "visibility": "PUBLIC",
+    "distribution": {"feedDistribution": "MAIN_FEED"},
+    "content": {
+      "media": {
+        "id": "urn:li:image:IMAGE_URN",
+        "title": "Q3 Performance Summary"
+      }
+    }
+  }'
+```
+
+### Document Post Creation (PDF/Carousel)
+
+LinkedIn "document posts" (carousels) follow the same register-upload-post pattern but use the documents API.
+
+**Register document upload**:
+```bash
+curl -s -X POST "https://api.linkedin.com/rest/documents?action=initializeUpload" \
+  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "Content-Type: application/json" \
+  -H "LinkedIn-Version: 202405" \
+  -d '{
+    "initializeUploadRequest": {
+      "owner": "urn:li:person:MEMBER_ID"
+    }
+  }'
+```
+
+**Upload the PDF and create post** (same pattern as image -- PUT binary, then POST with `content.media.id` set to the document URN).
+
+### Article Publishing via API
+
+**Create an article post** (link article hosted externally):
+```bash
+curl -s -X POST "https://api.linkedin.com/rest/posts" \
+  -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "Content-Type: application/json" \
+  -H "LinkedIn-Version: 202405" \
+  -d '{
+    "author": "urn:li:person:MEMBER_ID",
+    "lifecycleState": "PUBLISHED",
+    "commentary": "I wrote about why most engineering teams get incident response wrong.\n\nKey insight: the 5-minute rule changes everything.",
+    "visibility": "PUBLIC",
+    "distribution": {"feedDistribution": "MAIN_FEED"},
+    "content": {
+      "article": {
+        "source": "https://yourblog.com/incident-response",
+        "title": "The 5-Minute Rule for Incident Response",
+        "description": "A practical framework for engineering teams"
+      }
+    }
+  }'
+```
+
+> **Note**: Article-link posts get reduced reach vs native text posts. Prefer putting links in the first comment.
+
+### Analytics Endpoints
+
+**Get post statistics (organic)**:
+```bash
+curl -s -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "LinkedIn-Version: 202405" \
+  "https://api.linkedin.com/rest/organizationalEntityShareStatistics?q=organizationalEntity&organizationalEntity=urn:li:organization:ORG_ID&timeIntervals.timeGranularityType=DAY&timeIntervals.timeRange.start=1704067200000&timeIntervals.timeRange.end=1706745600000"
+```
+
+**Get share statistics for a specific post**:
+```bash
+curl -s -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "LinkedIn-Version: 202405" \
+  "https://api.linkedin.com/rest/organizationalEntityShareStatistics?q=organizationalEntity&organizationalEntity=urn:li:organization:ORG_ID&shares=urn:li:share:SHARE_ID"
+```
+
+Response fields:
+| Field | Description |
+|-------|-------------|
+| `totalShareStatistics.impressionCount` | Total times the post appeared in feeds |
+| `totalShareStatistics.uniqueImpressionsCount` | Unique viewers |
+| `totalShareStatistics.clickCount` | Total clicks (content + read more) |
+| `totalShareStatistics.likeCount` | Total likes/reactions |
+| `totalShareStatistics.commentCount` | Total comments |
+| `totalShareStatistics.shareCount` | Total reposts |
+| `totalShareStatistics.engagement` | Engagement rate (decimal) |
+
+**Get follower statistics** (organization pages):
+```bash
+curl -s -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
+  -H "LinkedIn-Version: 202405" \
+  "https://api.linkedin.com/rest/organizationalEntityFollowerStatistics?q=organizationalEntity&organizationalEntity=urn:li:organization:ORG_ID"
+```
+
+### Webhook / Notification Patterns
+
+LinkedIn does not offer real-time webhooks for most events. Use polling instead:
+
+```
+Polling strategy:
+  - Post engagement: Poll every 15 minutes for first 4 hours after posting
+  - Mentions/comments: Poll every 5 minutes during engagement_hours
+  - Follower counts: Poll once per day
+  - Analytics: Poll once per day (data lags 24-48 hours)
+```
+
+### Error Handling and Rate Limit Retry
+
+```
+Rate limit response headers:
+  X-RateLimit-Limit: 100
+  X-RateLimit-Remaining: 0
+  X-RateLimit-Reset: 1706745600
+
+HTTP 429 response:
+  {"status": 429, "message": "Resource level throttle limit..."}
+```
+
+**Retry strategy**:
+```
+1. On HTTP 429: Read X-RateLimit-Reset header
+2. Calculate wait_seconds = reset_timestamp - current_timestamp
+3. Sleep for wait_seconds + 1 (buffer)
+4. Retry the request (max 3 retries)
+5. On 3 consecutive 429s: back off for 15 minutes
+
+On HTTP 5xx (server error):
+1. Retry with exponential backoff: 1s, 2s, 4s
+2. Max 3 retries
+3. Log failure and queue for later retry
+
+On HTTP 401 (expired token):
+1. Trigger OAuth 2.0 refresh flow
+2. Update stored token
+3. Retry original request once
+```
+
+---
+
+## Content Calendar Template
+
+### Monthly Content Planning Framework
+
+Organize content around weekly themes that rotate through your content pillars.
+
+```
+MONTH: [Month Year]
+THEME ROTATION:
+  Week 1: [Pillar 1 -- e.g., Engineering Leadership]
+  Week 2: [Pillar 2 -- e.g., Industry Trends]
+  Week 3: [Pillar 3 -- e.g., Career Growth]
+  Week 4: [Pillar 1 deep dive OR seasonal/timely topic]
+```
+
+### Weekly Content Schedule
+
+```
+WEEK OF [DATE] — Theme: [Weekly Theme]
+
+Monday:
+  - 8:30 AM: [Personal story] tied to weekly theme
+    Format: Hook + narrative + lesson + question
+    Goal: High engagement to start the week
+
+Tuesday:
+  - 9:00 AM: [Step-by-step guide] or [How-to]
+    Format: Numbered list with tactical advice
+    Goal: Saves and shares (authority building)
+
+Wednesday:
+  - 8:30 AM: [Data/insight post] with original analysis
+    Format: Stat + context + your take + question
+    Goal: Credibility and thought leadership
+
+Thursday:
+  - 9:00 AM: [Contrarian take] or [Industry opinion]
+    Format: Bold statement + reasoning + invitation to debate
+    Goal: Comments and discussion (algorithm boost)
+
+Friday:
+  - 8:00 AM: [Engagement post] — poll, question, or lightweight personal content
+    Format: Short, conversational, easy to respond to
+    Goal: Community building before weekend
+```
+
+### Content Mix Ratios
+
+| Category | % of Posts | Examples |
+|----------|-----------|----------|
+| Educational / Value | 40% | How-tos, frameworks, lessons learned |
+| Personal / Storytelling | 25% | Career stories, failures, reflections |
+| Engagement / Discussion | 20% | Questions, polls, contrarian takes |
+| Promotional / Company | 10% | Product launches, hiring, milestones |
+| Curated / Commentary | 5% | Industry news with your analysis |
+
+**Rule**: Never let promotional content exceed 15%. LinkedIn penalizes overtly sales-y accounts.
+
+### Engagement Windows and Response Strategy
+
+```
+Post published at 8:30 AM:
+  Minutes 0-15:  Reply to EVERY comment immediately (signals activity to algorithm)
+  Minutes 15-60: Reply within 5 minutes of each new comment
+  Hours 1-4:     Reply within 30 minutes
+  Hours 4-24:    Reply within 2 hours (during business hours)
+  Day 2+:        Reply within 24 hours
+
+First-comment strategy:
+  - Post your own comment within 2 minutes of publishing
+  - Use it for: link to resource, additional context, question to spark discussion
+  - This comment acts as engagement seed
+```
+
+---
+
+## Analytics & Optimization
+
+### Key Metrics to Track
+
+| Metric | Formula | Good Benchmark | Great Benchmark |
+|--------|---------|----------------|-----------------|
+| Engagement rate | (reactions + comments + reposts) / impressions | > 2% | > 5% |
+| Comment rate | comments / impressions | > 0.3% | > 1% |
+| Follower growth rate | net new followers / total followers per week | > 0.5% | > 2% |
+| Profile views | weekly profile views trend | Consistent growth | 2x after viral post |
+| SSI (Social Selling Index) | LinkedIn's built-in score (0-100) | > 50 | > 70 |
+| Content saves | saves / impressions | > 0.5% | > 2% |
+| Click-through rate | clicks / impressions | > 1% | > 3% |
+
+### Engagement Rate Calculation
+
+```
+engagement_rate = (reactions + comments + reposts) / impressions * 100
+
+Example:
+  120 reactions + 35 comments + 8 reposts = 163 engagements
+  163 / 5,200 impressions = 3.13% engagement rate
+
+Per-post tracking:
+  | Post Date | Topic | Format | Impressions | Eng Rate | Comments |
+  |-----------|-------|--------|-------------|----------|----------|
+  | Mon 03/03 | Leadership | Story | 5,200 | 3.13% | 35 |
+  | Tue 03/04 | AI Tools | How-to | 3,800 | 4.21% | 22 |
+  | Wed 03/05 | Hiring | Data | 2,100 | 2.85% | 12 |
+```
+
+### A/B Testing Strategies
+
+Test one variable at a time across pairs of similar posts:
+
+| Variable | Option A | Option B | Track |
+|----------|----------|----------|-------|
+| Hook style | Question hook | Bold statement hook | Click-through rate |
+| Post length | Short (< 800 chars) | Long (1200+ chars) | Dwell time, engagement |
+| Posting time | 8:00 AM | 9:30 AM | Impressions after 4 hours |
+| CTA type | Question CTA | "Agree? Repost" CTA | Comment rate vs repost rate |
+| Hashtag count | 3 hashtags | 0 hashtags | Reach beyond network |
+| Format | Plain text | Text + image | Engagement rate |
+
+**How to run a test**:
+1. Pick one variable to test (e.g., posting time)
+2. Keep everything else constant (same pillar, similar format, similar length)
+3. Run for 2 weeks (minimum 4 posts per variant)
+4. Compare average metrics -- ignore outliers
+5. Adopt the winner and move to next variable
+
+### Identifying Top-Performing Content Patterns
+
+After 30+ posts, analyze your data to find patterns:
+
+```
+Sort all posts by engagement rate (descending):
+  1. Look at your top 5 posts — what do they share?
+     - Same content pillar?
+     - Same format (story, how-to, contrarian)?
+     - Same hook style?
+     - Similar length range?
+     - Same posting day/time?
+  2. Look at your bottom 5 posts — what went wrong?
+     - External links in body?
+     - Promotional tone?
+     - Published on Friday/weekend?
+     - Weak hook?
+  3. Create your "hit formula":
+     Best combo: [Pillar] + [Format] + [Hook style] + [Day/Time]
+     Example: "Engineering Leadership + Personal Story + Confession Hook + Tuesday 8:30 AM"
+```
+
+---
+
+## Audience Growth Strategies
+
+### Comment-First Strategy
+
+The fastest way to grow on LinkedIn is strategic commenting on high-visibility posts.
+
+**How it works**:
+1. Identify 15-20 active creators in your niche (10K+ followers)
+2. Turn on notifications for their posts
+3. Be among the first 5 comments on their new posts
+4. Write substantive comments (3-5 sentences) that add genuine value
+
+**Comment templates for growth**:
+```
+Adding a data point:
+  "This resonates. At [Company/Role], we saw [specific metric] when we
+  implemented [related approach]. The key difference was [insight].
+  Curious if others have seen similar results?"
+
+Respectful counterpoint:
+  "Interesting perspective. I'd push back slightly on [point] — in my
+  experience with [context], the opposite was true because [reason].
+  That said, I think [original point] absolutely holds for [use case]."
+
+Extending the idea:
+  "Building on this — one thing I'd add is [new angle]. I wrote about
+  this recently and the biggest takeaway was [specific insight].
+  [Question that invites further discussion]?"
+```
+
+**Target**: 5-10 thoughtful comments per day during peak hours (8-10 AM).
+
+### Collaborative Content Patterns
+
+**Tagging strategy**:
+- Tag 1-3 people who would genuinely find the content relevant
+- Always explain WHY you're tagging them (not drive-by tags)
+- Tag people you've already engaged with (they're more likely to respond)
+
+```
+Example post with strategic tags:
+  "I've been thinking about how engineering teams handle on-call rotations.
+
+  After talking to 20+ eng managers, here are the 3 models that actually work:
+
+  1. Follow-the-sun (best for distributed teams)
+  2. Volunteer-first rotation (best for small teams)
+  3. Tiered escalation (best for complex systems)
+
+  @Name1 — your team's approach to #2 was eye-opening.
+  @Name2 — curious if your distributed team uses #1 or something else?
+
+  What model does your team use? Reply with your team size."
+```
+
+**Co-creation patterns**:
+- Interview a peer and post key insights (tag them, they reshare)
+- "X people I learned from this year" posts (mass tagging, high reshare rate)
+- Collaborative lists: "Drop your best [resource] in the comments, I'll compile and share"
+
+### LinkedIn Newsletter Strategy
+
+Newsletters convert profile visitors into subscribers with direct inbox delivery.
+
+**Newsletter setup checklist**:
+```
+1. Name: Clear, specific, benefit-driven
+   Good: "The Engineering Leader's Playbook"
+   Bad: "My Thoughts on Things"
+
+2. Cadence: Weekly or biweekly (consistency > frequency)
+
+3. Format:
+   - 800-1500 words (longer than posts, shorter than blog articles)
+   - One core idea per issue
+   - Actionable takeaways or frameworks
+   - End with a question to drive comments
+
+4. Promotion:
+   - Announce each issue with a teaser post (don't just auto-share)
+   - Reference newsletter content in regular posts
+   - Cross-promote with other newsletter authors
+```
+
+**Newsletter content structure**:
+```
+Issue #[N]: [Compelling Title]
+
+[Hook paragraph -- why this matters NOW]
+
+[Section 1: The Problem / Context]
+  - 2-3 paragraphs with specific examples
+
+[Section 2: The Framework / Solution]
+  - Numbered steps or clear model
+  - Real-world application examples
+
+[Section 3: How to Apply This]
+  - Actionable next steps the reader can take today
+
+[Closing: Question + CTA]
+  "What's your experience with [topic]? Reply in the comments."
+  "If you found this useful, share it with your team."
+```
+
+### LinkedIn Live and Events
+
+**LinkedIn Live** broadcasts get 7x more reactions and 24x more comments than regular video posts.
+
+**Live session framework**:
+```
+Pre-event (1 week before):
+  - Create LinkedIn Event and post announcement
+  - Send invites to relevant connections
+  - Post 2-3 teaser posts building anticipation
+
+During event:
+  - Start 2 minutes early for tech check
+  - Open with clear agenda (30 seconds)
+  - Acknowledge live commenters by name
+  - Keep sessions 20-40 minutes
+
+Post-event:
+  - Post key takeaways within 2 hours
+  - Reply to all comments on the event post
+  - Repurpose recording into 3-5 short clips for future posts
+```
+
+**Event types that work**:
+| Type | Duration | Best For | Frequency |
+|------|----------|----------|-----------|
+| AMA (Ask Me Anything) | 30 min | Engagement, authority | Monthly |
+| Industry deep dive | 20 min | Thought leadership | Biweekly |
+| Interview / fireside chat | 40 min | Network growth | Monthly |
+| Quick tip / hot take | 10 min | Visibility | Weekly |
+
+---
+
+## Worked Examples
+
+### Example 1: Thought Leadership Campaign
+
+**Scenario**: VP of Engineering building authority in "engineering culture" niche.
+
+**Content pillars**:
+```
+Pillar 1: Engineering Management (40%)
+Pillar 2: Scaling Teams (30%)
+Pillar 3: Career Advice (20%)
+Pillar 4: Personal Lessons (10%)
+```
+
+**Week 1 posting schedule with sample posts**:
+
+**Monday 8:30 AM -- Personal Story (Pillar 1)**:
+```
+I promoted my worst interviewer to Head of Recruiting.
+
+Sounds crazy. Here's what happened.
+
+She kept rejecting candidates everyone else loved.
+Her "pass rate" was 15%. Team average was 60%.
+
+But after 12 months, something became clear:
+
+Her hires had:
+→ 94% retention rate (team avg: 71%)
+→ 2.3x faster time to first meaningful contribution
+→ Zero PIPs in their first year
+
+She wasn't a bad interviewer.
+She was the only one actually doing it right.
+
+The lesson?
+Measure what matters. Pass rates reward speed.
+Retention rates reward judgment.
+
+What's one metric your team optimizes for
+that might be the wrong one?
+
+#EngineeringLeadership #Hiring #TechManagement
+```
+
+**Tuesday 9:00 AM -- How-To Guide (Pillar 2)**:
+```
+How to run a team retrospective that people actually enjoy
+(not the soul-crushing ones everyone dreads):
+
+Step 1: Kill the "what went well / what didn't" format
+→ Use "I wish... I wonder... I'm proud of..." instead
+
+Step 2: Timebox ruthlessly
+→ 45 minutes max. If it takes longer, your team is too big for one retro.
+
+Step 3: One action item per person, max
+→ A retro with 20 action items produces zero change.
+→ One item per person = accountability.
+
+Step 4: Start with appreciation
+→ First 5 minutes: each person thanks someone else on the team.
+→ This changes the entire energy of the room.
+
+Step 5: Rotate the facilitator
+→ The manager should NOT always run retros.
+→ It changes what people feel safe saying.
+
+I've used this format with teams of 5 to teams of 50.
+
+What's your retro format? Drop it below --
+I'm always looking for new approaches.
+
+#Agile #EngineeringCulture #TeamManagement
+```
+
+**Wednesday 8:30 AM -- Data + Insight (Pillar 2)**:
+```
+We tracked every engineering team meeting for 6 months.
+
+The data was uncomfortable.
+
+→ Average engineer: 11.2 hours/week in meetings
+→ Senior engineers: 16.4 hours/week
+→ Time spent in meetings that could've been async: 62%
+
+We cut 40% of recurring meetings.
+
+Result after 3 months:
+→ Sprint velocity: +23%
+→ Engineer satisfaction: +31% (internal survey)
+→ "Deep work" blocks per week: 2.1 → 4.7
+
+The surprising part?
+Nobody missed the deleted meetings.
+Not one person asked to bring them back.
+
+If you haven't audited your meeting load recently,
+you're probably burning 30-40% of your team's capacity.
+
+What % of your meetings could be an async update?
+
+#Engineering #Productivity #Leadership
+```
+
+**Thursday 9:00 AM -- Contrarian Take (Pillar 3)**:
+```
+Unpopular opinion: "Culture fit" interviews should be illegal.
+
+Here's why:
+
+Culture fit = "do I want to get a beer with this person?"
+That's not hiring. That's friend-making.
+
+What actually matters:
+→ Values alignment (do they care about the same outcomes?)
+→ Working style compatibility (async vs sync, docs vs meetings)
+→ Growth trajectory (will they push the team forward?)
+
+None of those require "fitting in."
+
+The best hire I ever made was someone who challenged
+every assumption we had. They didn't "fit" our culture.
+
+They made it better.
+
+Replace "culture fit" with "culture add."
+
+Agree or disagree? I'd love to hear your take.
+
+#Hiring #Diversity #EngineeringCulture #Leadership
+```
+
+**Friday 8:00 AM -- Engagement Post (Pillar 4)**:
+```
+Fill in the blank:
+
+"The best career advice I ever received was ___________."
+
+I'll go first:
+
+"Stop optimizing for your next promotion.
+Start optimizing for your next learning curve."
+
+Changed how I made every career decision since.
+
+Your turn.
+
+#CareerAdvice #ProfessionalGrowth
+```
+
+### Example 2: Company Page Management
+
+**Scenario**: B2B SaaS company (Series B, 80 employees) managing their LinkedIn company page.
+
+**Posting cadence**:
+```
+Company page: 4-5 posts per week
+Employee advocacy: 2-3 employees reshare/post per week
+Executive accounts: CEO + CTO post 2-3x/week each
+```
+
+**Weekly company page schedule**:
+```
+Monday:    Industry insight or thought leadership (educational)
+Tuesday:   Product tip or customer use case (value-driven)
+Wednesday: Team/culture spotlight (employer branding)
+Thursday:  Data or trend analysis (authority)
+Friday:    Milestone, hiring, or community post (engagement)
+```
+
+**Sample company page posts**:
+
+**Tuesday -- Customer Use Case**:
+```
+"We used to spend 3 hours every Monday pulling reports manually."
+
+That's what @CustomerName's ops team told us last quarter.
+
+After switching to [Product] automated workflows:
+→ Report generation: 3 hours → 12 minutes
+→ Data accuracy: 89% → 99.7%
+→ Team freed up: 12 hours/week for strategic work
+
+The best part? They set it up in a single afternoon.
+
+Read the full story: [link in first comment]
+
+#DataAutomation #Operations #CustomerSuccess
+```
+
+**Wednesday -- Team Culture Spotlight**:
+```
+This is Sarah. She joined us as intern #3 two years ago.
+
+Last week she deployed our new ML pipeline to production.
+By herself. On a Tuesday. No drama.
+
+What happened in between:
+→ Mentored by 4 different senior engineers
+→ Shipped 47 PRs in her first year
+→ Gave her first conference talk at 23
+→ Now leads a team of 3
+
+We don't hire for credentials.
+We hire for curiosity and grit.
+
+Sarah had both.
+
+We're hiring 5 more engineers just like her.
+Link in the comments.
+
+#Hiring #Engineering #StartupCulture #WomenInTech
+```
+
+**Employee advocacy tracking**:
+```
+| Employee | Role | Posts/Week | Avg Reach | Topics |
+|----------|------|-----------|-----------|--------|
+| CEO | Executive | 3 | 8,500 | Vision, industry, leadership |
+| CTO | Executive | 2 | 5,200 | Technical, architecture, hiring |
+| VP Eng | Leader | 2 | 3,100 | Engineering culture, management |
+| DevRel | IC | 3 | 4,800 | Tutorials, product, community |
+```
+
+**Analytics tracking cadence**:
+```
+Daily:   Check post-level engagement (reactions, comments, shares)
+Weekly:  Follower growth, top-performing post, engagement rate trend
+Monthly: Content audit — which pillars/formats performed best
+         Adjust next month's content mix based on data
+Quarterly: Competitor benchmarking, SSI review, strategy refresh
+```
+
+### Example 3: Job Seeker Profile Optimization Campaign
+
+**Scenario**: Senior developer transitioning to engineering management role.
+
+**4-week content plan**:
+```
+Week 1: Establish expertise
+  - Post about a technical decision you led and its business impact
+  - Share a "lessons from my first year managing" story
+  - Comment on 10 engineering leadership posts
+
+Week 2: Demonstrate thought leadership
+  - Publish a how-to post: "How I transitioned from IC to manager"
+  - Share data or a framework you've developed
+  - Start engaging with hiring managers' content in target companies
+
+Week 3: Build social proof
+  - Post about a mentoring success story (tag the mentee with permission)
+  - Share a "things I wish I knew" post targeting new managers
+  - Request 3-5 recommendations from colleagues and reports
+
+Week 4: Signal availability
+  - Post about what you're looking for (without desperation)
+  - Engage heavily in target company employees' content
+  - Send personalized connection requests to hiring managers
+```
+
+**Sample "open to opportunities" post**:
+```
+After 8 years of writing code and 2 years of leading teams,
+I'm looking for my next engineering management challenge.
+
+What I bring to the table:
+→ Scaled a team from 4 to 22 engineers
+→ Reduced deployment failures by 73% through better process
+→ Mentored 6 engineers into senior roles
+→ Built hiring pipelines that maintained 85%+ offer acceptance
+
+What I'm looking for:
+→ Series A-C company building something meaningful
+→ Team of 8-20 engineers who care about craft
+→ Leadership that values engineering culture, not just velocity
+
+If your team is growing and you value
+managers who still understand the code --
+I'd love to chat.
+
+DMs are open. Or drop a comment and I'll reach out.
+
+#OpenToWork #EngineeringManager #Hiring #Leadership
+```
diff --git a/hands/predictor/HAND.toml b/hands/predictor/HAND.toml
index 3d87eee..c0c3d7e 100644
--- a/hands/predictor/HAND.toml
+++ b/hands/predictor/HAND.toml
@@ -425,6 +425,241 @@ token_consumption = "high"
 default_active = false
 activation_warning = "Predictor hand runs continuously and generates predictions, consuming tokens."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "预测 Hand"
 description = "自主预测智能体——收集信号、构建推理链、做出校准预测并追踪准确度"
+category = "数据"
+
+[i18n.zh.settings.prediction_domain]
+label = "预测领域"
+description = "预测的主要关注领域"
+
+[i18n.zh.settings.time_horizon]
+label = "时间跨度"
+description = "预测的前瞻时间范围"
+
+[i18n.zh.settings.data_sources]
+label = "数据来源"
+description = "监控信号的来源类型"
+
+[i18n.zh.settings.report_frequency]
+label = "报告频率"
+description = "生成预测报告的频率"
+
+[i18n.zh.settings.predictions_per_report]
+label = "每份报告预测数"
+description = "每份报告包含的预测条目数量"
+
+[i18n.zh.settings.track_accuracy]
+label = "追踪准确度"
+description = "在预测时间窗口到期后对历史预测进行评分"
+
+[i18n.zh.settings.confidence_threshold]
+label = "置信度阈值"
+description = "纳入预测报告的最低置信度"
+
+[i18n.zh.settings.contrarian_mode]
+label = "逆向思维模式"
+description = "主动寻找并展示与主流共识相反的预测"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "予測 Hand"
+description = "自律型予測エージェント——シグナル収集、推論チェーン構築、キャリブレーション済み予測、精度追跡"
+category = "データ"
+
+[i18n.ja.settings.prediction_domain]
+label = "予測ドメイン"
+description = "予測の主な対象分野"
+
+[i18n.ja.settings.time_horizon]
+label = "予測期間"
+description = "どのくらい先まで予測するか"
+
+[i18n.ja.settings.data_sources]
+label = "データソース"
+description = "シグナルを監視するソースの種類"
+
+[i18n.ja.settings.report_frequency]
+label = "レポート頻度"
+description = "予測レポートの生成頻度"
+
+[i18n.ja.settings.predictions_per_report]
+label = "レポートあたりの予測数"
+description = "各レポートに含める予測項目の数"
+
+[i18n.ja.settings.track_accuracy]
+label = "精度追跡"
+description = "予測期間が終了した過去の予測にスコアを付ける"
+
+[i18n.ja.settings.confidence_threshold]
+label = "信頼度しきい値"
+description = "予測をレポートに含めるための最低信頼度"
+
+[i18n.ja.settings.contrarian_mode]
+label = "逆張りモード"
+description = "コンセンサスに反する予測を積極的に探索・提示する"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Predicciones"
+description = "Predictor autónomo del futuro — recopila señales, construye cadenas de razonamiento, genera predicciones calibradas y rastrea la precisión"
+category = "Datos"
+
+[i18n.es.settings.prediction_domain]
+label = "Dominio de predicción"
+description = "Dominio principal para las predicciones"
+
+[i18n.es.settings.time_horizon]
+label = "Horizonte temporal"
+description = "Qué tan lejos en el futuro predecir"
+
+[i18n.es.settings.data_sources]
+label = "Fuentes de datos"
+description = "Qué tipos de fuentes monitorear para señales"
+
+[i18n.es.settings.report_frequency]
+label = "Frecuencia de informes"
+description = "Con qué frecuencia generar informes de predicción"
+
+[i18n.es.settings.predictions_per_report]
+label = "Predicciones por informe"
+description = "Número de predicciones a incluir por informe"
+
+[i18n.es.settings.track_accuracy]
+label = "Rastrear precisión"
+description = "Puntuar predicciones pasadas cuando su horizonte temporal expire"
+
+[i18n.es.settings.confidence_threshold]
+label = "Umbral de confianza"
+description = "Confianza mínima para incluir una predicción"
+
+[i18n.es.settings.contrarian_mode]
+label = "Modo contrario"
+description = "Buscar y presentar activamente predicciones contrarias al consenso"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand de Prédictions"
+description = "Prédicteur autonome — collecte de signaux, construction de chaînes de raisonnement, prédictions calibrées et suivi de la précision"
+category = "Données"
+
+[i18n.fr.settings.prediction_domain]
+label = "Domaine de prédiction"
+description = "Domaine principal pour les prédictions"
+
+[i18n.fr.settings.time_horizon]
+label = "Horizon temporel"
+description = "Jusqu'où prédire dans le futur"
+
+[i18n.fr.settings.data_sources]
+label = "Sources de données"
+description = "Types de sources à surveiller pour les signaux"
+
+[i18n.fr.settings.report_frequency]
+label = "Fréquence des rapports"
+description = "Fréquence de génération des rapports de prédiction"
+
+[i18n.fr.settings.predictions_per_report]
+label = "Prédictions par rapport"
+description = "Nombre de prédictions à inclure par rapport"
+
+[i18n.fr.settings.track_accuracy]
+label = "Suivi de la précision"
+description = "Évaluer les prédictions passées lorsque leur horizon temporel expire"
+
+[i18n.fr.settings.confidence_threshold]
+label = "Seuil de confiance"
+description = "Confiance minimale pour inclure une prédiction"
+
+[i18n.fr.settings.contrarian_mode]
+label = "Mode contraire"
+description = "Rechercher et présenter activement des prédictions contraires au consensus"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Vorhersage-Hand"
+description = "Autonomer Vorhersage-Agent — Signalerfassung, Aufbau von Argumentationsketten, kalibrierte Vorhersagen und Genauigkeitsverfolgung"
+category = "Daten"
+
+[i18n.de.settings.prediction_domain]
+label = "Vorhersagedomäne"
+description = "Hauptdomäne für Vorhersagen"
+
+[i18n.de.settings.time_horizon]
+label = "Zeithorizont"
+description = "Wie weit in die Zukunft vorhergesagt werden soll"
+
+[i18n.de.settings.data_sources]
+label = "Datenquellen"
+description = "Welche Quellentypen auf Signale überwacht werden"
+
+[i18n.de.settings.report_frequency]
+label = "Berichtshäufigkeit"
+description = "Wie oft Vorhersageberichte generiert werden"
+
+[i18n.de.settings.predictions_per_report]
+label = "Vorhersagen pro Bericht"
+description = "Anzahl der Vorhersagen pro Bericht"
+
+[i18n.de.settings.track_accuracy]
+label = "Genauigkeitsverfolgung"
+description = "Vergangene Vorhersagen bewerten, wenn ihr Zeithorizont abläuft"
+
+[i18n.de.settings.confidence_threshold]
+label = "Konfidenzschwelle"
+description = "Mindestvertrauen für die Aufnahme einer Vorhersage"
+
+[i18n.de.settings.contrarian_mode]
+label = "Konträrer Modus"
+description = "Aktiv nach Vorhersagen suchen und präsentieren, die dem Konsens widersprechen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "예측 Hand"
+description = "자율 미래 예측 에이전트 — 신호 수집, 추론 체인 구축, 보정된 예측 수행 및 정확도 추적"
+category = "데이터"
+
+[i18n.ko.settings.prediction_domain]
+label = "예측 분야"
+description = "예측의 주요 관심 분야"
+
+[i18n.ko.settings.time_horizon]
+label = "시간 범위"
+description = "예측의 미래 전망 기간"
+
+[i18n.ko.settings.data_sources]
+label = "데이터 소스"
+description = "신호를 모니터링할 소스 유형"
+
+[i18n.ko.settings.report_frequency]
+label = "보고서 빈도"
+description = "예측 보고서 생성 주기"
+
+[i18n.ko.settings.predictions_per_report]
+label = "보고서당 예측 수"
+description = "각 보고서에 포함할 예측 항목 수"
+
+[i18n.ko.settings.track_accuracy]
+label = "정확도 추적"
+description = "예측 기간 만료 후 과거 예측에 대한 점수 평가"
+
+[i18n.ko.settings.confidence_threshold]
+label = "신뢰도 임계값"
+description = "예측 보고서에 포함하기 위한 최소 신뢰도"
+
+[i18n.ko.settings.contrarian_mode]
+label = "역발상 모드"
+description = "주류 컨센서스에 반하는 예측을 적극적으로 탐색하고 제시"
diff --git a/hands/predictor/SKILL.md b/hands/predictor/SKILL.md
index 4e32476..f7eaaf9 100644
--- a/hands/predictor/SKILL.md
+++ b/hands/predictor/SKILL.md
@@ -184,6 +184,591 @@ PREDICTION: [Specific, falsifiable claim]
 
 ---
 
+## Worked Examples
+
+### Example 1: Corporate Acquisition
+
+**Question**: "Will Acme Corp be acquired within 12 months?" (asked January 2025)
+
+```
+PREDICTION: Acme Corp (mid-cap SaaS, $2B market cap) will be acquired by January 2026
+
+1. REFERENCE CLASS (Outside View)
+   Base rate: ~5-7% of publicly traded mid-cap SaaS companies receive
+   acquisition offers in any given 12-month period.
+   Reference examples:
+   - Splunk acquired by Cisco (2023) — similar scale, strategic buyer
+   - Figma attempted acquisition by Adobe (2022) — regulatory block
+   - Nuance acquired by Microsoft (2021) — vertical SaaS, strategic fit
+   - Mandiant acquired by Google (2022) — security vertical
+   - Cvent acquired by Blackstone (2021) — PE buyout at depressed valuation
+
+   Starting probability: 6%
+
+2. SPECIFIC EVIDENCE (Inside View)
+   Signals FOR (+):
+   a. Board hired Goldman Sachs as advisor (leaked filing)
+      — strength: STRONG — adjustment: +20%
+      (Companies that retain M&A advisors complete a transaction ~40% of the time)
+   b. CEO sold 30% of personal holdings in Q4 (SEC filing)
+      — strength: MODERATE — adjustment: +5%
+   c. Two major competitors acquired in past 18 months (market consolidation)
+      — strength: MODERATE — adjustment: +8%
+   d. Revenue growth decelerated from 35% to 18% YoY (earnings report)
+      — strength: MODERATE — adjustment: +5%
+      (Slower-growth companies more likely to accept acquisition offers)
+
+   Signals AGAINST (-):
+   a. Founder still holds 25% voting control and has said "we're building for
+      the long term" (recent interview)
+      — strength: STRONG — adjustment: -12%
+   b. Stock price at all-time high — acquirer must pay steep premium
+      — strength: MODERATE — adjustment: -5%
+   c. Current antitrust environment — FTC blocking more deals
+      — strength: WEAK — adjustment: -3%
+
+3. SYNTHESIS
+   Starting probability (base rate):  6%
+   Signals for:  +20% +5% +8% +5% =  +38%
+   Signals against: -12% -5% -3%   =  -20%
+   Net adjustment:                     +18%
+   Raw probability:                    24%
+
+   Sanity check: ~1 in 4 feels right given the strong M&A advisor signal
+   balanced against founder control.
+   Final probability: 25%
+
+4. KEY ASSUMPTIONS
+   - Goldman engagement is for M&A (not debt restructuring):
+     If wrong, probability drops to 8%
+   - Founder is willing to sell at the right price:
+     If wrong (founder vetoes any deal), probability drops to 3%
+   - Regulatory environment doesn't tighten further:
+     If wrong, probability drops to 18%
+
+5. RESOLUTION
+   Date: January 31, 2026
+   Criteria: Definitive merger agreement announced (not just rumors)
+   Data source: SEC EDGAR (8-K filing), Bloomberg terminal
+```
+
+### Example 2: Technology Adoption
+
+**Question**: "Will WebAssembly (Wasm) reach mainstream server-side adoption by 2027?"
+
+```
+PREDICTION: >20% of new cloud-deployed services will use Wasm runtimes by
+end of 2027
+
+1. REFERENCE CLASS (Outside View)
+   Technology adoption lifecycle (Rogers curve):
+   - Innovators (2.5%) → Early Adopters (13.5%) → Early Majority (34%)
+   - Crossing from Early Adopters to Early Majority typically takes 3-5 years
+     after first production deployments
+   - First serious server-side Wasm deployments: ~2022 (Fermyon, Cosmonic)
+   - Current status (2025): Late Early Adopter stage
+
+   Historical analogues for infrastructure tech adoption:
+   - Containers (Docker 2013 → mainstream 2017-2018): ~4-5 years
+   - Kubernetes (2014 → mainstream 2018-2019): ~4-5 years
+   - Serverless (Lambda 2014 → mainstream 2018-2020): ~4-6 years
+
+   Base rate for "infrastructure tech reaching 20% adoption within 5 years
+   of first production use": ~30%
+
+   Starting probability: 30%
+
+2. SPECIFIC EVIDENCE (Inside View)
+   Signals FOR (+):
+   a. WASI standard maturing — WASI Preview 2 shipped, component model
+      stabilizing (W3C working group)
+      — strength: STRONG — adjustment: +8%
+   b. Major cloud providers offering Wasm runtimes (Fastly, Cloudflare Workers,
+      Azure, AWS exploring)
+      — strength: STRONG — adjustment: +10%
+   c. Docker adding Wasm support natively (announced 2022, shipping)
+      — strength: MODERATE — adjustment: +5%
+
+   Signals AGAINST (-):
+   a. Ecosystem still fragmented — multiple competing runtimes, toolchain gaps
+      — strength: STRONG — adjustment: -10%
+   b. Containers already "good enough" for most workloads — weak forcing
+      function to switch
+      — strength: STRONG — adjustment: -8%
+   c. Wasm language support uneven — great for Rust/C++, mediocre for Python/JS
+      — strength: MODERATE — adjustment: -5%
+
+   Leading indicators to track:
+   - CNCF survey: % of respondents evaluating/using Wasm
+   - Job postings mentioning Wasm (Indeed/LinkedIn trend)
+   - GitHub stars and contributors for top Wasm runtimes (wasmtime, wasmer)
+   - WASI spec milestone dates vs planned dates
+
+3. SYNTHESIS
+   Starting probability (base rate):  30%
+   Signals for:  +8% +10% +5%      =  +23%
+   Signals against: -10% -8% -5%   =  -23%
+   Net adjustment:                      0%
+   Final probability: 30%
+
+   Interpretation: The positive and negative signals roughly cancel out.
+   The base rate from analogous infrastructure technologies holds.
+   This is genuinely uncertain — the "chasm" crossing is the key risk.
+
+4. KEY ASSUMPTIONS
+   - "Mainstream" defined as >20% of NEW deployments (not total installed base)
+   - WASI component model reaches 1.0 stable by mid-2026
+     If delayed beyond 2026: probability drops to 15%
+   - No competing paradigm emerges (e.g., eBPF expanding scope):
+     If strong competitor: probability drops to 20%
+
+5. RESOLUTION
+   Date: December 31, 2027
+   Criteria: CNCF annual survey shows >20% respondents using Wasm in production
+   Data source: CNCF Annual Survey, Datadog Container Report
+```
+
+### Example 3: Geopolitical Forecast
+
+**Question**: "Will US-China trade tensions escalate significantly in 2025?"
+(Defined as: new tariffs >25% on >$100B of goods, or export controls expanded
+to 3+ new technology categories)
+
+```
+PREDICTION: Significant escalation of US-China trade tensions in 2025
+
+1. REFERENCE CLASS (Outside View)
+   Historical trade conflict escalation pattern:
+   - US-China trade relations since 2018: escalation occurred in 4 of 7 years
+   - In election year +1 (new/returning administration): escalation rate ~60%
+   - Trade wars historically escalate in steps, with retaliation cycles
+
+   Starting probability: 55%
+
+2. SPECIFIC EVIDENCE (Inside View)
+   Signals FOR (+):
+   a. Administration rhetoric on China hawkish across both parties
+      — strength: STRONG — adjustment: +10%
+   b. Semiconductor export controls already expanding (ASML, Tokyo Electron)
+      — strength: STRONG — adjustment: +8%
+   c. China retaliating with rare earth export restrictions
+      — strength: MODERATE — adjustment: +5%
+
+   Signals AGAINST (-):
+   a. Business lobbying against further tariffs (Chamber of Commerce, farm lobby)
+      — strength: MODERATE — adjustment: -5%
+   b. Inflation concerns create political cost for tariffs
+      — strength: MODERATE — adjustment: -5%
+   c. Diplomatic channels active (recent bilateral meetings)
+      — strength: WEAK — adjustment: -3%
+
+   Scenario mapping:
+   ┌─────────────────────────┬─────────────┬────────────────────┐
+   │ Scenario                │ Probability │ Key trigger        │
+   ├─────────────────────────┼─────────────┼────────────────────┤
+   │ Major escalation        │ 25%         │ Taiwan crisis or   │
+   │ (new tariffs + controls │             │ tech IP theft case │
+   │  + retaliatory cycle)   │             │                    │
+   ├─────────────────────────┼─────────────┼────────────────────┤
+   │ Moderate escalation     │ 40%         │ Incremental tariff │
+   │ (meets our threshold)   │             │ increases + 1-2    │
+   │                         │             │ new export controls│
+   ├─────────────────────────┼─────────────┼────────────────────┤
+   │ Status quo / minor      │ 30%         │ Diplomatic deals,  │
+   │ changes                 │             │ election distraction│
+   ├─────────────────────────┼─────────────┼────────────────────┤
+   │ De-escalation           │ 5%          │ Grand bargain      │
+   │ (reduced tariffs)       │             │ (historically rare)│
+   └─────────────────────────┴─────────────┴────────────────────┘
+
+   P(meets our escalation threshold) = 25% + 40% = 65%
+
+3. SYNTHESIS
+   Starting probability (base rate):  55%
+   Signals for:  +10% +8% +5%      =  +23%
+   Signals against: -5% -5% -3%    =  -13%
+   Net adjustment:                     +10%
+   Raw probability:                    65%
+
+   Cross-check with scenario mapping: 65% — consistent.
+   Final probability: 65%
+
+4. KEY ASSUMPTIONS
+   - No major geopolitical crisis (Taiwan strait) that causes extreme
+     escalation or extreme restraint: If crisis occurs, split to
+     80% (escalation) or 20% (restraint/avoidance)
+   - US economy remains stable: If recession hits, probability drops
+     to 45% (political cost of tariffs rises)
+   - China does not make major trade concessions preemptively:
+     If it does, probability drops to 30%
+
+5. RESOLUTION
+   Date: December 31, 2025
+   Criteria: Cumulative new tariffs >25% on >$100B goods OR export controls
+   expanded to 3+ new technology categories (per USTR/BIS announcements)
+   Data source: USTR tariff schedule, BIS Entity List updates, Congressional
+   Research Service reports
+```
+
+---
+
+## Fermi Estimation Techniques
+
+Fermi estimation is the art of making reasonable order-of-magnitude guesses
+by breaking unknowable questions into smaller, estimable pieces.
+
+### Step-by-Step Process
+
+```
+1. DEFINE the quantity you want to estimate
+   → Be specific about units, scope, and timeframe
+
+2. DECOMPOSE into factors you can estimate independently
+   → Prefer multiplication chains: A × B × C
+   → Each factor should be something you can reason about
+
+3. ESTIMATE each factor
+   → Use round numbers (powers of 10 when possible)
+   → State your confidence range for each factor
+
+4. MULTIPLY and sanity-check
+   → Does the result pass the "smell test"?
+   → Cross-check with any known anchors
+
+5. STATE your uncertainty
+   → Fermi estimates are typically accurate within 1 order of magnitude
+   → Give a range: [estimate / 3, estimate × 3] is a reasonable default
+```
+
+### Common Reference Anchors
+
+Keep these memorized for quick estimation:
+
+```
+POPULATION
+  World:        ~8 billion
+  US:           ~340 million
+  EU:           ~450 million
+  China:        ~1.4 billion
+  India:        ~1.4 billion
+
+ECONOMICS
+  World GDP:           ~$100 trillion
+  US GDP:              ~$28 trillion
+  US median household: ~$75,000/year
+  US federal budget:   ~$6.5 trillion
+  S&P 500 total cap:   ~$45 trillion
+
+TIME
+  Seconds in a day:    ~86,400 (~10^5)
+  Seconds in a year:   ~31.5 million (~3 × 10^7)
+  Working hours/year:  ~2,000
+
+TECHNOLOGY
+  Global internet users:    ~5.5 billion
+  Global smartphone users:  ~4.5 billion
+  AWS annual revenue:       ~$90 billion
+  Global IT spending:       ~$5 trillion
+  GitHub developers:        ~100 million
+
+INDUSTRY SIZES (annual, global)
+  Cloud computing:     ~$600 billion
+  Semiconductor:       ~$600 billion
+  Pharmaceutical:      ~$1.5 trillion
+  Automotive:          ~$3 trillion
+  Agriculture:         ~$3 trillion
+  E-commerce:          ~$6 trillion
+```
+
+### Worked Fermi Examples
+
+**Example A: Estimating the TAM for an AI code review tool**
+
+```
+Question: What is the annual TAM for an AI-powered code review SaaS?
+
+Decomposition:
+  TAM = (Number of professional developers)
+      × (% who do code reviews regularly)
+      × (willingness to pay for tooling)
+      × (average annual price)
+
+Estimates:
+  Professional developers worldwide: ~30 million
+  (GitHub has 100M accounts, but ~30% are professional, and
+   not all professionals use GitHub)
+
+  % who do code reviews: ~60%
+  (Standard in companies > 50 engineers, less common in small shops)
+
+  Target market (teams that would buy SaaS): ~40%
+  (Enterprise and mid-market; small teams use free tools)
+
+  Annual price per seat: ~$300/year
+  (Comparable: GitHub Copilot ~$200, Snyk ~$400, middle ground)
+
+Calculation:
+  30M × 0.60 × 0.40 × $300 = $2.16 billion
+
+Sanity check:
+  - GitHub revenue ~$2B (broader product, ~4M paid users)
+  - Snyk valued at $7B (code security, related space)
+  - $2B TAM is plausible for a focused code review tool
+
+Result: ~$2 billion TAM (range: $700M to $6B)
+```
+
+**Example B: Estimating daily active queries to a search engine**
+
+```
+Question: How many search queries does Google process per day?
+
+Decomposition:
+  Queries/day = (Internet users who use Google)
+              × (searches per user per day)
+
+Estimates:
+  Global internet users: ~5.5 billion
+  Google market share: ~90%
+  Google users: 5.5B × 0.90 = ~5 billion
+  But not all use it daily: ~50% daily active rate
+  Daily active Google searchers: ~2.5 billion
+
+  Searches per active user per day: ~3-4
+  (Some people search 10+ times, many search once or not at all)
+
+Calculation:
+  2.5 billion × 3.5 = ~8.5 billion queries/day
+
+Sanity check:
+  Published figure (Google): ~8.5 billion searches/day (2024)
+  Our estimate nailed it — sometimes Fermi estimation gets lucky.
+
+Result: ~8.5 billion/day (range: 3B to 25B)
+```
+
+### Order of Magnitude Sanity Checks
+
+After any estimate, verify it makes sense:
+
+```
+CHECK 1: Per-person reasonableness
+  Divide by relevant population. Is the per-person number realistic?
+  "$50B market ÷ 340M Americans = $147/person" — plausible?
+
+CHECK 2: Comparison to known quantities
+  Is your estimate bigger or smaller than things you know?
+  "Our estimate of X is $3B — that's 5% of AWS revenue. Reasonable?"
+
+CHECK 3: Growth rate implied
+  If you're estimating a future state, what annual growth rate is implied?
+  >50% sustained growth for >3 years is extremely rare.
+
+CHECK 4: Upper bound test
+  What is the theoretical maximum? Is your estimate within it?
+  "Total possible customers × maximum price = ceiling"
+```
+
+---
+
+## Prediction Market Patterns
+
+### Interpreting Market Prices as Probabilities
+
+Prediction market prices map to probabilities, but with important caveats:
+
+```
+Market price $0.65 for "Event X occurs"
+  → Naive interpretation: 65% probability
+  → Adjusted interpretation: depends on market quality
+
+Adjustment factors:
+  Liquid market (Polymarket, Metaculus with many forecasters):
+    Price ≈ true probability (±3-5%)
+
+  Thin market (<50 traders, <$10K volume):
+    Price is noisy — treat as ±15% uncertainty
+    A $0.65 price could represent 50-80% true probability
+
+  Binary vs. multi-outcome:
+    Binary markets are more reliable
+    Multi-outcome markets often have probabilities summing to >100%
+    (overround) — normalize before interpreting
+```
+
+### Common Prediction Market Biases
+
+| Bias | Description | Impact | Correction |
+|------|-------------|--------|------------|
+| Favorite-longshot | Favorites underpriced, longshots overpriced | Longshot events appear ~2-3x more likely than they are | If market says 5%, true probability may be 2-3% |
+| Recency | Recent events dominate pricing | Probability spikes after news, then slowly reverts | Wait 24-48h after major news before trusting market prices |
+| Liquidity premium | Illiquid contracts trade at a discount | Prices biased toward 50% in thin markets | Weight liquid markets more heavily |
+| Expiration clustering | Prices converge to 0 or 1 near expiration | Mid-probability contracts vanish near deadline | Most useful signal is months before resolution |
+| Hedging distortion | Traders hedging other positions, not expressing beliefs | Prices reflect risk management, not pure probability | Cross-reference with non-market forecasts |
+
+### Aggregation Methods
+
+When combining multiple probability estimates (markets, experts, models):
+
+```
+SIMPLE AVERAGE
+  P = (P1 + P2 + P3) / 3
+  Use when: Sources are roughly equally credible
+  Weakness: Susceptible to outliers
+
+MEDIAN
+  P = middle value of sorted estimates
+  Use when: One source might be badly miscalibrated
+  Weakness: Ignores magnitude of disagreement
+
+TRIMMED MEAN
+  Drop highest and lowest, average the rest
+  P = average(P2 ... Pn-1) after sorting
+  Use when: 5+ sources, want outlier robustness
+
+EXTREMIZED AVERAGE
+  P_avg = simple average
+  P_extremized = P_avg^a / (P_avg^a + (1-P_avg)^a), where a > 1
+  Typical a = 1.5 to 2.5 (more extremizing with more independent sources)
+  Use when: Sources are genuinely independent (not reading each other)
+  Rationale: If 5 independent sources all say 70%, the true probability
+  is likely higher than 70% — shared info should push further from 50%
+
+CONFIDENCE-WEIGHTED AVERAGE
+  P = Σ(wi × Pi) / Σ(wi)
+  where wi = track record score or source reliability
+  Use when: Sources have known, differing track records
+```
+
+### When Markets Beat Experts (and Vice Versa)
+
+```
+MARKETS TEND TO WIN when:
+  ✓ Large, liquid, diverse participant pool
+  ✓ Question is well-defined with clear resolution criteria
+  ✓ Information is widely distributed (no single expert has edge)
+  ✓ Time horizon is 1 month to 2 years
+  Examples: Election outcomes, product launch dates, economic indicators
+
+EXPERTS TEND TO WIN when:
+  ✓ Question requires deep domain-specific knowledge
+  ✓ Market is thin or participants lack domain context
+  ✓ Very long time horizons (>5 years) — markets discount distant futures
+  ✓ Novel situations with no historical market precedent
+  Examples: Technical feasibility, scientific breakthroughs, niche regulation
+
+BEST PRACTICE: Use both
+  Start with the market price, then adjust using expert insight.
+  Treat the market as the prior and expert analysis as an update.
+```
+
+---
+
+## Update Protocol
+
+### Bayesian Updating Worked Example
+
+```
+SCENARIO: You predicted 30% chance that Company Z launches Product A in Q1.
+New evidence: A leaked internal slide shows a Q1 launch timeline.
+
+STEP 1: State the prior
+  P(launch in Q1) = 0.30
+
+STEP 2: Assess the evidence
+  E = leaked slide showing Q1 timeline
+  How likely is this evidence if the launch IS happening in Q1?
+    P(E | launch) = 0.85
+    (Internal slides usually reflect real plans, but plans change)
+  How likely is this evidence if the launch is NOT in Q1?
+    P(E | no launch) = 0.15
+    (Could be outdated slide, aspirational, or decoy)
+
+STEP 3: Calculate the likelihood ratio
+  LR = P(E | launch) / P(E | no launch) = 0.85 / 0.15 = 5.67
+
+STEP 4: Convert prior to odds, multiply, convert back
+  Prior odds = 0.30 / 0.70 = 0.429
+  Posterior odds = 0.429 × 5.67 = 2.43
+  Posterior probability = 2.43 / (1 + 2.43) = 0.71
+
+STEP 5: State the update
+  Prior: 30% → Posterior: 71%
+  Update magnitude: +41 percentage points
+  This is a LARGE update, appropriate because the evidence (internal
+  planning document) is strong and directly relevant.
+```
+
+### Evidence Strength Classification
+
+How much to update based on different types of evidence:
+
+```
+EVIDENCE TIER 1 — Large update (likelihood ratio 5-20x)
+  → Official announcement or regulatory filing
+  → Confirmed internal document (not rumor)
+  → Directly observed outcome of prerequisite event
+  → Multiple independent strong sources confirming same fact
+  Typical update: ±15-30 percentage points
+
+EVIDENCE TIER 2 — Moderate update (likelihood ratio 2-5x)
+  → Credible journalist report with named sources
+  → Statistical data that changes the base rate
+  → Expert with strong track record changing their view
+  → Structural/policy change that alters incentives
+  Typical update: ±5-15 percentage points
+
+EVIDENCE TIER 3 — Small update (likelihood ratio 1.2-2x)
+  → Rumor from semi-credible source
+  → Anecdotal evidence (single data point)
+  → Social media sentiment shift
+  → Expert opinion without new information
+  Typical update: ±2-5 percentage points
+
+EVIDENCE TIER 4 — Negligible update (likelihood ratio ~1x)
+  → Repetition of previously known information
+  → Pundit opinion with no domain expertise
+  → Vague statement open to multiple interpretations
+  → Evidence equally consistent with both outcomes
+  Typical update: ±0-2 percentage points (or skip entirely)
+```
+
+### When to Make Large vs. Small Updates
+
+```
+MAKE A LARGE UPDATE when:
+  • Evidence directly addresses your key uncertainty
+  • The source has a strong track record on this topic
+  • The evidence would be very surprising if your prediction were correct
+    (or very unsurprising if it were wrong)
+  • Multiple independent signals shift in the same direction simultaneously
+
+MAKE A SMALL UPDATE when:
+  • Evidence is tangentially related to your prediction
+  • The source's reliability is uncertain
+  • The evidence is consistent with multiple interpretations
+  • You've already incorporated similar evidence
+
+RESIST UPDATING when:
+  • The "evidence" is just someone restating the consensus
+  • A vivid anecdote feels compelling but carries no statistical weight
+  • You're reacting emotionally (fear, excitement) rather than analytically
+  • The evidence source has an obvious incentive to mislead
+```
+
+### Common Updating Mistakes
+
+| Mistake | Description | Fix |
+|---------|-------------|-----|
+| Over-updating on vivid events | A dramatic single event shifts your view by 20+ points when the base rate barely moved | Ask: "Does this event actually change the base rate, or just my emotional state?" |
+| Under-updating on base rate changes | New data shows the reference class frequency shifted, but you keep your old anchor | Periodically re-derive the base rate from scratch instead of only adjusting incrementally |
+| Asymmetric updating | Updating strongly on confirming evidence, weakly on disconfirming evidence | Force yourself to calculate the likelihood ratio for disconfirming evidence explicitly |
+| Double-counting | Updating on a news article, then updating again on a tweet quoting the same article | Track the original source — if two signals share the same root cause, count once |
+| Failure to update | Knowing the evidence should change your view but not bothering because your current number "feels right" | Set calendar reminders to review active predictions monthly with fresh evidence |
+| Stampede updating | A prediction market spikes, causing you to rush your update to match | Market moves are data, not commands — assess independently, then compare |
+
+---
+
 ## Prediction Tracking & Scoring
 
 ### Prediction Ledger Format
diff --git a/hands/reddit/HAND.toml b/hands/reddit/HAND.toml
index f7c2932..eb78b78 100644
--- a/hands/reddit/HAND.toml
+++ b/hands/reddit/HAND.toml
@@ -423,6 +423,241 @@ frequency = "hourly"
 token_consumption = "medium"
 default_active = false
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "Reddit Hand"
 description = "自主 Reddit 管理——监控子版块、发布内容、回复帖子并追踪 Karma 和互动"
+category = "通信"
+
+[i18n.zh.settings.subreddits]
+label = "子版块"
+description = "要监控的子版块列表（逗号分隔，例如 rust,programming,machinelearning）"
+
+[i18n.zh.settings.monitor_mode]
+label = "监控模式"
+description = "在监控的子版块中追踪哪些内容"
+
+[i18n.zh.settings.auto_reply]
+label = "自动回复"
+description = "自动回复相关的帖子和评论"
+
+[i18n.zh.settings.post_frequency]
+label = "发布频率"
+description = "创建原创帖子的频率"
+
+[i18n.zh.settings.content_style]
+label = "内容风格"
+description = "帖子和回复的语气和风格"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "将帖子和回复加入队列等待审核，而非直接发布"
+
+[i18n.zh.settings.min_karma_to_post]
+label = "最低发帖 Karma"
+description = "仅在账户拥有至少此数量 Karma 的子版块中发帖"
+
+[i18n.zh.settings.max_reply_depth]
+label = "最大回复深度"
+description = "回复的最大评论嵌套深度（深层嵌套的可见度较低）"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Reddit"
+description = "Gestor autónomo de Reddit — monitorea subreddits, publica contenido, responde en hilos y rastrea karma e interacciones"
+category = "Comunicación"
+
+[i18n.es.settings.subreddits]
+label = "Subreddits"
+description = "Lista de subreddits a monitorear separados por comas (ej. rust,programming,machinelearning)"
+
+[i18n.es.settings.monitor_mode]
+label = "Modo de monitoreo"
+description = "Qué rastrear en los subreddits monitoreados"
+
+[i18n.es.settings.auto_reply]
+label = "Respuesta automática"
+description = "Responder automáticamente a publicaciones y comentarios relevantes"
+
+[i18n.es.settings.post_frequency]
+label = "Frecuencia de publicación"
+description = "Con qué frecuencia crear publicaciones originales"
+
+[i18n.es.settings.content_style]
+label = "Estilo de contenido"
+description = "Tono y enfoque para publicaciones y respuestas"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Poner publicaciones y respuestas en cola para revisión en lugar de publicarlas directamente"
+
+[i18n.es.settings.min_karma_to_post]
+label = "Karma mínimo para publicar"
+description = "Solo publicar en subreddits donde la cuenta tenga al menos este nivel de karma"
+
+[i18n.es.settings.max_reply_depth]
+label = "Profundidad máxima de respuesta"
+description = "Profundidad máxima del hilo de comentarios para responder (los hilos más profundos tienen menos visibilidad)"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "Reddit Hand"
+description = "自律型Redditマネージャー——サブレディットの監視、コンテンツ投稿、スレッドへの返信、Karmaとエンゲージメントの追跡"
+category = "コミュニケーション"
+
+[i18n.ja.settings.subreddits]
+label = "サブレディット"
+description = "監視するサブレディットのリスト（カンマ区切り、例: rust,programming,machinelearning）"
+
+[i18n.ja.settings.monitor_mode]
+label = "監視モード"
+description = "監視対象のサブレディットで追跡する内容"
+
+[i18n.ja.settings.auto_reply]
+label = "自動返信"
+description = "関連する投稿やコメントに自動で返信する"
+
+[i18n.ja.settings.post_frequency]
+label = "投稿頻度"
+description = "オリジナル投稿を作成する頻度"
+
+[i18n.ja.settings.content_style]
+label = "コンテンツスタイル"
+description = "投稿と返信のトーンとアプローチ"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "投稿や返信を直接公開せず、レビュー用キューに追加する"
+
+[i18n.ja.settings.min_karma_to_post]
+label = "投稿に必要な最低Karma"
+description = "アカウントが最低このKarmaを持つサブレディットでのみ投稿する"
+
+[i18n.ja.settings.max_reply_depth]
+label = "最大返信深度"
+description = "返信するコメントスレッドの最大ネスト深度（深いスレッドほど可視性が低い）"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Reddit"
+description = "Gestionnaire Reddit autonome — surveille les subreddits, publie du contenu, répond dans les fils et suit le karma et l'engagement"
+category = "Communication"
+
+[i18n.fr.settings.subreddits]
+label = "Subreddits"
+description = "Liste de subreddits à surveiller séparés par des virgules (ex. rust,programming,machinelearning)"
+
+[i18n.fr.settings.monitor_mode]
+label = "Mode de surveillance"
+description = "Ce qu'il faut suivre dans les subreddits surveillés"
+
+[i18n.fr.settings.auto_reply]
+label = "Réponse automatique"
+description = "Répondre automatiquement aux publications et commentaires pertinents"
+
+[i18n.fr.settings.post_frequency]
+label = "Fréquence de publication"
+description = "Fréquence de création de publications originales"
+
+[i18n.fr.settings.content_style]
+label = "Style de contenu"
+description = "Ton et approche pour les publications et réponses"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Mettre les publications et réponses en file d'attente pour révision au lieu de les publier directement"
+
+[i18n.fr.settings.min_karma_to_post]
+label = "Karma minimum pour publier"
+description = "Ne publier que dans les subreddits où le compte possède au moins ce niveau de karma"
+
+[i18n.fr.settings.max_reply_depth]
+label = "Profondeur maximale de réponse"
+description = "Profondeur maximale d'imbrication des commentaires pour répondre (les fils plus profonds ont moins de visibilité)"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Reddit-Hand"
+description = "Autonomer Reddit-Manager — überwacht Subreddits, postet Inhalte, antwortet in Threads und verfolgt Karma und Engagement"
+category = "Kommunikation"
+
+[i18n.de.settings.subreddits]
+label = "Subreddits"
+description = "Kommagetrennte Liste der zu überwachenden Subreddits (z.B. rust,programming,machinelearning)"
+
+[i18n.de.settings.monitor_mode]
+label = "Überwachungsmodus"
+description = "Was in den überwachten Subreddits verfolgt werden soll"
+
+[i18n.de.settings.auto_reply]
+label = "Automatische Antwort"
+description = "Automatisch auf relevante Beiträge und Kommentare antworten"
+
+[i18n.de.settings.post_frequency]
+label = "Veröffentlichungshäufigkeit"
+description = "Wie oft originale Beiträge erstellt werden"
+
+[i18n.de.settings.content_style]
+label = "Inhaltsstil"
+description = "Ton und Ansatz für Beiträge und Antworten"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Beiträge und Antworten zur Überprüfung in die Warteschlange stellen, anstatt sie direkt zu veröffentlichen"
+
+[i18n.de.settings.min_karma_to_post]
+label = "Mindest-Karma zum Posten"
+description = "Nur in Subreddits posten, in denen das Konto mindestens dieses Karma-Level hat"
+
+[i18n.de.settings.max_reply_depth]
+label = "Maximale Antworttiefe"
+description = "Maximale Verschachtelungstiefe für Kommentarantworten (tiefere Threads haben geringere Sichtbarkeit)"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "Reddit Hand"
+description = "자율 Reddit 관리 — 서브레딧 모니터링, 콘텐츠 게시, 스레드 댓글 및 카르마와 참여도 추적"
+category = "커뮤니케이션"
+
+[i18n.ko.settings.subreddits]
+label = "서브레딧"
+description = "모니터링할 서브레딧 목록 (쉼표로 구분, 예: rust,programming,machinelearning)"
+
+[i18n.ko.settings.monitor_mode]
+label = "모니터링 모드"
+description = "모니터링 대상 서브레딧에서 추적할 콘텐츠"
+
+[i18n.ko.settings.auto_reply]
+label = "자동 답글"
+description = "관련 게시물과 댓글에 자동으로 답글 작성"
+
+[i18n.ko.settings.post_frequency]
+label = "게시 빈도"
+description = "원본 게시물 작성 주기"
+
+[i18n.ko.settings.content_style]
+label = "콘텐츠 스타일"
+description = "게시물과 답글의 어조와 접근 방식"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "게시물과 답글을 직접 게시하지 않고 대기열에 추가하여 검토"
+
+[i18n.ko.settings.min_karma_to_post]
+label = "최소 게시 카르마"
+description = "계정이 최소 이만큼의 카르마를 보유한 서브레딧에서만 게시"
+
+[i18n.ko.settings.max_reply_depth]
+label = "최대 답글 깊이"
+description = "답글 작성의 최대 댓글 중첩 깊이 (깊은 중첩일수록 노출도 감소)"
diff --git a/hands/reddit/SKILL.md b/hands/reddit/SKILL.md
index 6a6130e..fe167b8 100644
--- a/hands/reddit/SKILL.md
+++ b/hands/reddit/SKILL.md
@@ -247,3 +247,768 @@ Monitor account standing:
 - Build karma organically through genuine engagement
 - Avoid posting too frequently (triggers spam filters)
 - Diversify activity across multiple subreddits
+
+---
+
+## Worked Examples
+
+### Example 1: Product Launch on Reddit
+
+**Scenario**: You are launching a developer CLI tool and want to generate awareness on Reddit.
+
+**Phase 1 — Subreddit Research (Week 1-2 before launch)**
+
+Identify target subreddits and evaluate each:
+```
+Target subreddits (prioritized):
+1. r/commandline       — 350k members, accepts tool announcements, requires demo/screenshot
+2. r/programming       — 5M members, strict anti-marketing, only accepts substantial technical posts
+3. r/opensource         — 200k members, friendly to launches, requires repo link
+4. r/devtools           — 50k members, niche but highly targeted
+5. r/sideproject        — 100k members, launch-friendly, expects "what I built" framing
+```
+
+Fetch subreddit rules programmatically:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/commandline/about/rules"
+```
+
+**Phase 2 — Karma Building (Week 1-2 before launch)**
+
+Before posting about your product, build credibility:
+```
+Day 1-3:   Answer questions in r/commandline and r/programming (3-5 helpful comments/day)
+Day 4-7:   Share a useful tip or short guide unrelated to your product
+Day 8-10:  Engage in discussions, upvote good content, reply to others' posts
+Day 11-14: Share a technical deep-dive related to your product's domain (not the product itself)
+```
+
+**Phase 3 — Launch Posts (Launch Day)**
+
+Craft posts per subreddit culture:
+
+For **r/sideproject** (casual, story-driven):
+```
+Title: "I built a CLI tool that does X — here's what I learned"
+Body:
+- Paragraph on the problem and motivation
+- Short demo (gif/video link or code block)
+- What went wrong during development
+- Link to repo
+- "Would love feedback on X"
+```
+
+For **r/programming** (technical, anti-fluff):
+```
+Title: "X: an open-source CLI for Y written in Rust [with benchmarks]"
+Body:
+- Link directly to repo or blog post with technical depth
+- Performance comparison table
+- Architecture decisions
+- NO "please star my repo" language
+```
+
+For **r/commandline** (practical, demo-focused):
+```
+Title: "X — does Y in Z seconds from your terminal"
+Body:
+- Install instructions (one-liner)
+- Usage example with real output
+- Screenshot or asciinema link
+- Comparison to existing tools
+```
+
+**Phase 4 — Engagement (Launch Day + 48 hours)**
+
+Response templates:
+
+| Comment Type | Response Strategy |
+|-------------|-------------------|
+| "How does this compare to Z?" | Honest comparison table, acknowledge Z's strengths |
+| "Why not just use Z?" | Explain specific use cases where yours differs, no FUD |
+| "Found a bug" | Thank them, ask for details, open GitHub issue immediately |
+| "This is spam" | Do NOT argue. Briefly state this is your project and you're here to discuss |
+| "Great work!" | Thank them, ask what feature they'd want next |
+| Feature request | Acknowledge, add to roadmap, link to issue tracker |
+
+**Phase 5 — Follow-Up (Week after launch)**
+
+- Reply to every comment within 12 hours
+- Post an update in r/sideproject if you hit a milestone (e.g., "Hit 500 stars, here's what I changed based on Reddit feedback")
+- Do NOT cross-post the same content -- write fresh posts per subreddit
+
+---
+
+### Example 2: Community Monitoring and Sentiment Tracking
+
+**Scenario**: You manage a brand's Reddit presence and need to track mentions, sentiment, and emerging issues.
+
+**Step 1 — Set Up Monitoring Queries**
+
+Search for brand mentions across Reddit:
+```bash
+# Search all of Reddit for brand mentions
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/search?q=%22BrandName%22+OR+%22brandname%22&sort=new&limit=25&t=day"
+```
+
+Monitor specific subreddits where your audience lives:
+```bash
+# Monitor r/technology for relevant topics
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/technology/search?q=BrandName&restrict_sr=on&sort=new&limit=25&t=week"
+```
+
+**Step 2 — Sentiment Classification**
+
+Categorize each mention into:
+```
+POSITIVE   — Praise, recommendation, success story
+NEUTRAL    — Factual mention, question, comparison
+NEGATIVE   — Complaint, bug report, frustration
+CRITICAL   — Security concern, viral complaint, legal risk
+```
+
+Scoring signals from Reddit data:
+```
+score > 100 + sentiment=NEGATIVE  → High-priority alert (viral complaint)
+score > 50  + sentiment=POSITIVE  → Amplification opportunity
+num_comments > 20 + any sentiment → Active discussion, monitor closely
+upvote_ratio < 0.5                → Controversial, may escalate
+```
+
+**Step 3 — Alert Thresholds**
+
+| Condition | Action |
+|-----------|--------|
+| CRITICAL mention with score > 10 | Immediate alert to team |
+| 3+ NEGATIVE mentions in 24 hours | Trend alert, investigate root cause |
+| NEGATIVE post in subreddit > 500k members | Monitor hourly for 48 hours |
+| Competitor comparison post trending | Prepare factual response (do NOT post defensively) |
+
+**Step 4 — Weekly Report Template**
+
+```
+## Reddit Weekly Report — [Date Range]
+
+### Summary
+- Total mentions: X (up/down Y% from last week)
+- Sentiment breakdown: X% positive, Y% neutral, Z% negative
+- Top subreddits: r/sub1 (N mentions), r/sub2 (N mentions)
+
+### Trending Topics
+1. [Topic] — [Subreddit] — [Sentiment] — [Link]
+2. ...
+
+### Action Items
+- [ ] Respond to [specific thread] — negative sentiment, high visibility
+- [ ] Engage with [specific thread] — positive, amplification opportunity
+
+### Competitor Activity
+- [Competitor A]: N mentions, trending topics: ...
+- [Competitor B]: N mentions, trending topics: ...
+
+### Metrics
+| Metric | This Week | Last Week | Change |
+|--------|-----------|-----------|--------|
+| Total mentions | | | |
+| Positive % | | | |
+| Avg post score | | | |
+| Response time (hrs) | | | |
+```
+
+---
+
+### Example 3: AMA (Ask Me Anything) Management
+
+**Scenario**: You are organizing an AMA for a tech CEO in r/technology.
+
+**Preparation (2 Weeks Before)**
+
+1. Contact the subreddit moderators:
+   - Message the mod team through modmail (not individual DMs)
+   - Propose date, time, and AMA subject
+   - Ask about specific rules for AMAs (verification, scheduling, flair)
+   - Confirm the post format they expect
+
+2. Schedule for peak engagement:
+   ```
+   Recommended AMA times (US-centric subreddits):
+   - Tuesday-Thursday, 11:00 AM - 1:00 PM EST
+   - Avoid: weekends, holidays, major news days
+   - Post the AMA thread 30-60 minutes before the host starts answering
+   ```
+
+3. Prepare the AMA post:
+   ```
+   Title: "I'm [Name], [Role] at [Company]. [One-line hook]. AMA!"
+
+   Body:
+   - Brief intro (2-3 sentences about credentials)
+   - Why this AMA is happening (new product, milestone, event)
+   - Proof/verification (link to tweet, photo with timestamp)
+   - "I'll start answering at [TIME] [TIMEZONE]. Ask me anything!"
+   - Links to relevant context (website, blog post, prior work)
+   ```
+
+**During the AMA (2-3 Hours)**
+
+Real-time engagement strategy:
+```
+1. Sort comments by "best" and "new" alternately every 15 minutes
+2. Answer top-voted questions first (these set the tone)
+3. Answer at least 20-30 questions in a 2-hour session
+4. Mix short answers with detailed ones — avoid walls of text for every question
+5. Skip hostile/troll questions silently — do NOT acknowledge them
+6. For tough questions: answer honestly or say "I can't discuss that yet"
+7. Upvote good questions (even tough ones) — shows good faith
+```
+
+Response length guide:
+| Question Type | Response Length |
+|--------------|----------------|
+| Simple factual | 1-2 sentences |
+| Technical deep-dive | 2-3 paragraphs |
+| Personal/funny | 1-2 sentences, match the tone |
+| Critical/tough | 2-3 sentences, direct and honest |
+| Off-topic | Brief redirect or polite decline |
+
+**Follow-Up (24-48 Hours After)**
+
+- Post an edit to the original AMA: "Thanks everyone! I answered [N] questions. Check back — I'll try to answer a few more this week."
+- Answer 5-10 more highly-upvoted questions that were missed
+- Share the AMA link on other platforms (Twitter, LinkedIn) to drive continued engagement
+- Compile a "best of" summary with links to the strongest Q&A exchanges
+
+---
+
+## Advanced API Patterns
+
+### Pagination Handling
+
+Reddit uses cursor-based pagination with `after` and `before` fullnames.
+
+**Paginate through subreddit posts**:
+```bash
+# Page 1
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/new?limit=100"
+# Response includes: "after": "t3_abc123"
+
+# Page 2
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/new?limit=100&after=t3_abc123"
+# Response includes: "after": "t3_def456" (or null if last page)
+
+# Page 3
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/new?limit=100&after=t3_def456"
+```
+
+Pagination rules:
+- `limit` max is 100 per request
+- `after` returns items chronologically older than the given fullname
+- `before` returns items chronologically newer (useful for "check for new posts since last poll")
+- When `after` is `null` in the response, you have reached the last page
+- Reddit caps listing depth at ~1000 items regardless of pagination
+
+**Paginate backward (newer items)**:
+```bash
+# Get posts newer than a known fullname
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/new?limit=25&before=t3_abc123"
+```
+
+### Flair Management
+
+**Get available flairs for a subreddit**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/api/link_flair_v2"
+```
+
+**Submit a post with flair**:
+```bash
+curl -s -X POST -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  -d "sr=SUBREDDIT&kind=self&title=TITLE&text=BODY&flair_id=FLAIR_ID&flair_text=FLAIR_TEXT" \
+  "https://oauth.reddit.com/api/submit"
+```
+
+**Set flair on an existing post** (requires mod or post author permissions):
+```bash
+curl -s -X POST -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  -d "link=t3_POST_ID&flair_template_id=FLAIR_ID" \
+  "https://oauth.reddit.com/r/SUBREDDIT/api/selectflair"
+```
+
+### Moderation Endpoints
+
+These require moderator permissions on the target subreddit.
+
+**Get moderation queue**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/about/modqueue?limit=25"
+```
+
+**Approve a post/comment**:
+```bash
+curl -s -X POST -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  -d "id=FULLNAME" \
+  "https://oauth.reddit.com/api/approve"
+```
+
+**Remove a post/comment**:
+```bash
+curl -s -X POST -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  -d "id=FULLNAME&spam=false" \
+  "https://oauth.reddit.com/api/remove"
+```
+
+**Get moderation log**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/about/log?limit=25&type=removelink"
+```
+
+**Distinguish a comment as moderator**:
+```bash
+curl -s -X POST -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  -d "id=FULLNAME&how=yes" \
+  "https://oauth.reddit.com/api/distinguish"
+```
+
+### Multi-Subreddit Monitoring
+
+**Monitor multiple subreddits in a single request**:
+```bash
+# Combine subreddits with "+" for a merged feed
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/python+rust+golang/new?limit=50"
+```
+
+**Search across multiple subreddits**:
+```bash
+# Use the subreddit field in search to restrict
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/search?q=BrandName+subreddit%3Apython+OR+subreddit%3Arust&sort=new&limit=25"
+```
+
+**Get subreddit metadata for comparison**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/about"
+```
+Key fields in response: `subscribers`, `active_user_count`, `created_utc`, `public_description`, `submit_text`, `submission_type`.
+
+### Polling Strategies for Real-Time Awareness
+
+Reddit has no webhook support. Use polling with these patterns:
+
+**Efficient polling loop**:
+```
+1. Fetch /r/SUBREDDIT/new?limit=10 every 60 seconds
+2. Store the fullname of the newest item seen
+3. On next poll, use ?before=LAST_SEEN_FULLNAME to get only new items
+4. If response is empty, no new posts — sleep and retry
+5. If response has items, process them and update LAST_SEEN_FULLNAME
+```
+
+**Polling frequency by priority**:
+| Monitoring Type | Poll Interval | Endpoint |
+|----------------|---------------|----------|
+| Brand crisis monitoring | 30-60 seconds | /search?q=brand&sort=new |
+| Subreddit new posts | 60-120 seconds | /r/SUB/new |
+| Comment replies to own posts | 120 seconds | /message/inbox |
+| Competitor mentions | 300 seconds | /search?q=competitor&sort=new |
+| Weekly trend analysis | Once daily | /r/SUB/top?t=day |
+
+**Respect rate limits while polling**:
+```
+At 30 requests/minute (app-only auth):
+- 1 subreddit at 60s interval = 1 req/min → can monitor ~25 subreddits
+- 1 search query at 60s interval = 1 req/min
+- Reserve 5 req/min for ad-hoc queries
+- Total budget: 30 req/min, plan accordingly
+```
+
+---
+
+## Subreddit Analysis Framework
+
+### Evaluating a Subreddit Before Posting
+
+Before investing effort in any subreddit, run this assessment:
+
+**Step 1 — Pull subreddit metadata**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/about" | python3 -c "
+import sys, json
+d = json.load(sys.stdin)['data']
+print(f'Subscribers: {d[\"subscribers\"]:,}')
+print(f'Active now: {d[\"active_user_count\"]:,}')
+print(f'Created: {d[\"created_utc\"]}')
+print(f'Type: {d[\"submission_type\"]}')
+print(f'Description: {d[\"public_description\"][:200]}')
+"
+```
+
+**Step 2 — Measure actual engagement** (not just subscriber count):
+
+```bash
+# Get top 25 hot posts and examine their scores and comment counts
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/hot?limit=25"
+```
+
+Calculate from the response:
+```
+Engagement Score = median(post_scores) * median(num_comments)
+Activity Ratio = active_user_count / subscribers
+Health Indicator = (posts_per_day > 5) AND (Activity Ratio > 0.001)
+```
+
+**Step 3 — Subreddit quality scorecard**:
+
+| Factor | Good Sign | Bad Sign |
+|--------|-----------|----------|
+| Active/subscriber ratio | > 0.1% | < 0.01% |
+| Median hot post score | > 50 | < 10 |
+| Median comment count | > 10 | < 3 |
+| Posts per day | 5-50 | < 1 or > 500 (noise) |
+| Mod activity | Active modqueue, clear rules | No rules, spam in feed |
+| Top post age | Within last 24h | Weeks old (dead subreddit) |
+| Account age requirements | Reasonable (7 days) | None (spam-prone) or extreme (1 year) |
+
+### Peak Engagement Hours by Subreddit Type
+
+Optimal posting times vary by audience. All times in EST:
+
+| Subreddit Type | Peak Hours | Peak Days | Reasoning |
+|---------------|------------|-----------|-----------|
+| Tech/Programming | 9-11 AM EST | Tue-Thu | Developers browse during morning coffee |
+| Business/Startup | 7-9 AM EST | Mon-Wed | Professionals check before work |
+| Gaming | 6-10 PM EST | Fri-Sun | After work/school |
+| Science/Academic | 10 AM-12 PM EST | Mon-Wed | Researchers between tasks |
+| Lifestyle/Hobby | 12-2 PM EST, 7-9 PM EST | Any | Lunch breaks and evenings |
+| News/Politics | 7-9 AM EST | Mon-Fri | Morning news cycle |
+| Finance/Crypto | 8-10 AM EST | Mon-Fri | Pre-market and market open |
+
+To measure a specific subreddit's peak hours:
+```bash
+# Pull the last 100 posts and extract their timestamps
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/r/SUBREDDIT/new?limit=100"
+# Parse created_utc for each post and bucket by hour-of-day
+# Cross-reference with score to find high-score hours, not just high-volume hours
+```
+
+### Competitor Presence Analysis
+
+**Step 1 — Search for competitor mentions**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/search?q=%22CompetitorName%22&sort=new&t=month&limit=100"
+```
+
+**Step 2 — Build a competitor activity profile**:
+```
+For each competitor, track:
+- Which subreddits they are mentioned in (and by whom -- users vs the company)
+- Frequency of mentions (per week)
+- Sentiment of mentions (positive / neutral / negative)
+- Whether they have official accounts engaging in threads
+- Common complaints about them (your opportunity)
+- Common praise for them (your benchmark)
+```
+
+**Step 3 — Competitor comparison matrix**:
+| Metric | Your Brand | Competitor A | Competitor B |
+|--------|-----------|-------------|-------------|
+| Weekly mentions | | | |
+| Positive sentiment % | | | |
+| Subreddits present in | | | |
+| Official account activity | | | |
+| Top complaint theme | | | |
+| Top praise theme | | | |
+
+### Content Format Preferences by Subreddit Type
+
+| Subreddit Type | Preferred Format | Avoid |
+|---------------|-----------------|-------|
+| Technical (r/programming, r/rust) | Long-form text, code blocks, benchmarks | Short posts, images without context |
+| Q&A (r/AskReddit, r/askscience) | Concise questions, detailed answers | Link-only posts |
+| Showcase (r/sideproject, r/webdev) | Screenshots, demos, before/after | Text-only without visuals |
+| News (r/technology, r/science) | Link to source with summary comment | Self-post opinion pieces |
+| Discussion (r/startups, r/cscareerquestions) | Personal experience, specific details | Generic advice, platitudes |
+| Meme-friendly (r/ProgrammerHumor) | Images, short and punchy | Long text posts |
+
+---
+
+## Growth & Reputation Building
+
+### Karma Building Strategies (Comment-First Approach)
+
+New accounts or accounts entering a new subreddit should follow the comment-first approach:
+
+**Week 1-2: Listen and respond**
+```
+1. Sort by "new" in your target subreddits
+2. Find questions you can genuinely answer
+3. Write substantive, helpful comments (3+ sentences with specifics)
+4. Respond to 3-5 threads per day
+5. Do NOT mention your product, company, or project at all
+```
+
+**Week 3-4: Establish presence**
+```
+1. Start sharing relevant resources (not yours) that help the community
+2. Engage in discussions about trends and opinions in your domain
+3. Build recognition by being consistently helpful
+4. Your username should start becoming familiar to regulars
+```
+
+**Week 5+: Contribute original content**
+```
+1. Share a technical write-up, tutorial, or analysis (unrelated to your product)
+2. If well-received, you have earned the trust to occasionally mention your work
+3. Always frame self-promotional content as "I built X" (transparent) not "Check out X" (spammy)
+4. Maintain the 10:1 ratio — 10 helpful contributions for every 1 self-promotional post
+```
+
+Karma accumulation benchmarks:
+| Milestone | Unlocks |
+|-----------|---------|
+| 10 comment karma | Bypass most anti-spam filters |
+| 50 comment karma | Reduced posting cooldowns |
+| 100+ comment karma in a subreddit | Trusted contributor status in some subreddits |
+| 1000+ total karma | Access to r/lounge and some restricted subreddits |
+
+### Building Authority in Niche Subreddits
+
+Authority is built through consistency and expertise, not volume:
+
+1. **Pick 3-5 subreddits maximum** -- spreading across 20 subreddits builds no authority anywhere
+2. **Develop a recognizable voice** -- consistent formatting, depth of answers, specific expertise area
+3. **Answer the hard questions** -- skip the easy ones that 10 people will answer; tackle the ones that require real expertise
+4. **Follow up on your own answers** -- if someone asks a follow-up, respond promptly
+5. **Cite sources and show work** -- "I benchmarked this myself, here are the numbers" is worth 100x "I think X is faster"
+6. **Accept corrections gracefully** -- being wrong publicly and handling it well builds more trust than never being wrong
+
+### Cross-Posting Etiquette and Strategy
+
+Cross-posting (sharing a post from one subreddit to another) has specific norms:
+
+**Do:**
+- Use Reddit's built-in cross-post feature (preserves attribution)
+- Cross-post to subreddits where the content genuinely fits
+- Add a comment explaining why it is relevant to the new subreddit
+- Wait at least a few hours between cross-posts (avoid appearing spammy)
+
+**Don't:**
+- Cross-post to more than 2-3 subreddits
+- Cross-post to subreddits that explicitly ban it (check rules)
+- Copy-paste the same text as a new post instead of cross-posting (treated as spam)
+- Cross-post your own content excessively
+
+**Strategic cross-posting pattern**:
+```
+1. Post original content in the most specific/niche subreddit first
+2. If it gains traction (>20 upvotes, positive comments), cross-post to a broader subreddit
+3. Customize the title for the new audience
+4. Engage in comments on BOTH subreddits
+```
+
+### Handling Negative Feedback and Criticism
+
+Negative feedback on Reddit is public and permanent. Handle it strategically:
+
+**Response framework**:
+```
+1. PAUSE — Do not respond within the first 15 minutes. Emotional responses backfire.
+2. ASSESS — Is the criticism valid, partially valid, or trolling?
+3. RESPOND (or don't):
+   - Valid criticism: Acknowledge, thank them, explain what you will do about it
+   - Partially valid: Acknowledge the valid part, clarify the rest with facts
+   - Trolling/bad faith: Do NOT respond. Silence is the best response.
+4. FOLLOW UP — If you promised to fix something, come back and confirm when it is done
+```
+
+**Response templates by situation**:
+
+| Situation | Response Pattern |
+|-----------|-----------------|
+| Bug report | "Thanks for reporting this. Can you share [details]? I've opened [issue link] to track it." |
+| Feature complaint | "That's fair feedback. Here's why we made that choice: [reason]. We're considering [alternative]." |
+| Unfair comparison | "Good question. Here's a direct comparison: [facts]. [Competitor] is great at X, we focus on Y." |
+| Personal attack | Do not respond. Report if it violates rules. |
+| "This is trash" | "Sorry it didn't work for you. What specifically went wrong? Happy to help." |
+
+---
+
+## Analytics & Reporting
+
+### Post Performance Metrics
+
+Key metrics to track for every post:
+
+| Metric | Where to Find | What It Means |
+|--------|--------------|---------------|
+| Score | `data.score` | Net upvotes (upvotes minus downvotes) |
+| Upvote ratio | `data.upvote_ratio` | 0.0-1.0, percentage of votes that are upvotes |
+| Number of comments | `data.num_comments` | Total comments including replies |
+| Awards | `data.all_awardings` | List of awards received |
+| Cross-posts | `data.num_crossposts` | How many times others cross-posted it |
+
+**Fetch post performance**:
+```bash
+curl -s -H "Authorization: Bearer $ACCESS_TOKEN" \
+  -A "LibreFang Reddit Hand/1.0" \
+  "https://oauth.reddit.com/by_id/t3_POST_ID"
+```
+
+**Quality indicators**:
+```
+High engagement:    upvote_ratio > 0.85 AND num_comments > 20
+Controversial:      upvote_ratio 0.40-0.60 (heavily split votes)
+Viral potential:    score > 100 within first 2 hours
+Dead on arrival:    score < 5 after 4 hours
+Comment quality:    avg comment length > 100 chars (real discussion vs memes)
+```
+
+### Engagement Trend Tracking
+
+Track performance over time by recording metrics at regular intervals:
+
+```
+For each post, capture at:
+- T+1 hour:   score, num_comments, upvote_ratio
+- T+4 hours:  score, num_comments, upvote_ratio
+- T+24 hours: score, num_comments, upvote_ratio (final snapshot)
+
+For account-level tracking:
+- Weekly comment karma change
+- Weekly post karma change
+- Number of posts/comments per subreddit
+- Average score per post by subreddit
+```
+
+**Growth trajectory assessment**:
+| Period | Healthy Growth | Stagnant | Declining |
+|--------|---------------|----------|-----------|
+| Weekly karma change | > +50 | -10 to +10 | < -10 |
+| Avg post score trend | Increasing | Flat | Decreasing |
+| Comment reply rate | > 30% of comments get replies | 10-30% | < 10% |
+| New subreddit penetration | 1-2 new per month | 0 | Banned from any |
+
+### ROI Measurement for Business-Related Reddit Activity
+
+For teams using Reddit for marketing, community building, or support:
+
+**Trackable outcomes**:
+```
+Direct metrics:
+- Referral traffic from Reddit (track with UTM parameters in shared links)
+- Sign-ups/downloads from Reddit referral
+- Support tickets deflected by Reddit answers
+- GitHub stars/forks from Reddit posts (track with ?ref=reddit)
+
+Indirect metrics:
+- Brand mention volume over time
+- Sentiment ratio trend (positive / total mentions)
+- Share of voice vs competitors on Reddit
+- Community size if you run your own subreddit
+```
+
+**Cost calculation**:
+```
+Time invested:  Hours per week on Reddit * hourly cost
+Content cost:   Time creating Reddit-specific content
+Tool cost:      Monitoring tools, analytics
+─────────────────────────────────────────────
+Total cost/week: Sum of above
+
+ROI = (Value of outcomes - Total cost) / Total cost
+```
+
+**Value assignment for outcomes**:
+| Outcome | Suggested Valuation Method |
+|---------|--------------------------|
+| Referral sign-up | Same as other channel CAC |
+| Support ticket deflected | Average support ticket cost |
+| GitHub star from Reddit | Track conversion to paying user |
+| Positive brand mention | Equivalent ad impression value |
+| Viral post (>1000 score) | Equivalent paid reach cost |
+
+### Weekly Reddit Activity Report Template
+
+```
+## Reddit Activity Report — Week of [Date]
+
+### Account Health
+- Current karma: [post] / [comment]
+- Karma change this week: +/- [N]
+- Account age: [N] days
+- Active subreddits: [list]
+
+### Content Published
+| Date | Subreddit | Type | Title | Score | Comments | Upvote Ratio |
+|------|-----------|------|-------|-------|----------|-------------|
+| | | | | | | |
+
+### Comments Made
+- Total comments: [N]
+- Avg comment score: [N]
+- Top comment: [link] (score: [N])
+- Subreddit breakdown: r/sub1 ([N]), r/sub2 ([N])
+
+### Brand Mentions (External)
+- Total mentions found: [N]
+- Sentiment: [N]% positive, [N]% neutral, [N]% negative
+- Notable threads:
+  1. [Thread title] — [subreddit] — [sentiment] — [link]
+  2. ...
+
+### Engagement Metrics
+| Metric | This Week | Last Week | Trend |
+|--------|-----------|-----------|-------|
+| Posts published | | | |
+| Total post score | | | |
+| Comments made | | | |
+| Replies received | | | |
+| Avg response time | | | |
+
+### Referral Traffic (if tracked)
+- Clicks from Reddit: [N]
+- Sign-ups from Reddit: [N]
+- Top referral post: [link]
+
+### Next Week Plan
+- [ ] Target subreddits: [list]
+- [ ] Content planned: [description]
+- [ ] Threads to follow up on: [links]
+```
diff --git a/hands/researcher/HAND.toml b/hands/researcher/HAND.toml
index 8a6cd5b..7e2a0b3 100644
--- a/hands/researcher/HAND.toml
+++ b/hands/researcher/HAND.toml
@@ -442,6 +442,241 @@ token_consumption = "high"
 default_active = true
 activation_warning = "Researcher hand runs continuously and performs deep research, consuming tokens."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "深度研究 Hand"
 description = "自主深度研究员——全面调研、交叉验证、事实核查和结构化报告"
+category = "生产力"
+
+[i18n.zh.settings.research_depth]
+label = "研究深度"
+description = "每次调研的详尽程度"
+
+[i18n.zh.settings.output_style]
+label = "输出格式"
+description = "研究报告的格式风格"
+
+[i18n.zh.settings.source_verification]
+label = "来源验证"
+description = "在引用前通过多个来源交叉验证论述"
+
+[i18n.zh.settings.max_sources]
+label = "最大来源数"
+description = "每次调研参考的最大来源数量"
+
+[i18n.zh.settings.auto_follow_up]
+label = "自动追问"
+description = "自动研究调查过程中发现的延伸问题"
+
+[i18n.zh.settings.save_research_log]
+label = "保存研究日志"
+description = "保存详细的搜索查询和来源评估记录"
+
+[i18n.zh.settings.citation_style]
+label = "引用格式"
+description = "报告中引用来源的格式"
+
+[i18n.zh.settings.language]
+label = "语言"
+description = "研究和输出的主要语言"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "심층 연구 Hand"
+description = "자율 심층 연구원 — 철저한 조사, 교차 검증, 팩트체크 및 구조화된 보고서"
+category = "생산성"
+
+[i18n.ko.settings.research_depth]
+label = "연구 깊이"
+description = "각 조사의 철저함 정도"
+
+[i18n.ko.settings.output_style]
+label = "출력 스타일"
+description = "연구 보고서의 형식 스타일"
+
+[i18n.ko.settings.source_verification]
+label = "출처 검증"
+description = "인용 전 여러 출처를 통해 주장을 교차 검증"
+
+[i18n.ko.settings.max_sources]
+label = "최대 출처 수"
+description = "조사당 참고할 최대 출처 수"
+
+[i18n.ko.settings.auto_follow_up]
+label = "자동 후속 조사"
+description = "조사 과정에서 발견된 후속 질문을 자동으로 연구"
+
+[i18n.ko.settings.save_research_log]
+label = "연구 로그 저장"
+description = "상세한 검색 쿼리 및 출처 평가 기록 저장"
+
+[i18n.ko.settings.citation_style]
+label = "인용 형식"
+description = "보고서에서 출처를 인용하는 형식"
+
+[i18n.ko.settings.language]
+label = "언어"
+description = "연구 및 출력의 주요 언어"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "ディープリサーチ Hand"
+description = "自律型深層調査エージェント——徹底的な調査、クロスリファレンス、ファクトチェック、構造化レポート"
+category = "生産性"
+
+[i18n.ja.settings.research_depth]
+label = "調査の深さ"
+description = "各調査の徹底度"
+
+[i18n.ja.settings.output_style]
+label = "出力スタイル"
+description = "調査レポートのフォーマットスタイル"
+
+[i18n.ja.settings.source_verification]
+label = "ソース検証"
+description = "引用前に複数のソースでクレームをクロスチェックする"
+
+[i18n.ja.settings.max_sources]
+label = "最大ソース数"
+description = "調査ごとに参照するソースの最大数"
+
+[i18n.ja.settings.auto_follow_up]
+label = "自動フォローアップ"
+description = "調査中に発見されたフォローアップ質問を自動的に調査する"
+
+[i18n.ja.settings.save_research_log]
+label = "調査ログの保存"
+description = "詳細な検索クエリとソース評価の記録を保存する"
+
+[i18n.ja.settings.citation_style]
+label = "引用スタイル"
+description = "レポートでのソース引用の形式"
+
+[i18n.ja.settings.language]
+label = "言語"
+description = "調査と出力の主要言語"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Investigación"
+description = "Investigador autónomo en profundidad — investigación exhaustiva, referencias cruzadas, verificación de hechos e informes estructurados"
+category = "Productividad"
+
+[i18n.es.settings.research_depth]
+label = "Profundidad de investigación"
+description = "Qué tan exhaustiva debe ser cada investigación"
+
+[i18n.es.settings.output_style]
+label = "Estilo de salida"
+description = "Cómo formatear los informes de investigación"
+
+[i18n.es.settings.source_verification]
+label = "Verificación de fuentes"
+description = "Verificar afirmaciones cruzando múltiples fuentes antes de incluirlas"
+
+[i18n.es.settings.max_sources]
+label = "Máximo de fuentes"
+description = "Número máximo de fuentes a consultar por investigación"
+
+[i18n.es.settings.auto_follow_up]
+label = "Seguimiento automático"
+description = "Investigar automáticamente preguntas de seguimiento descubiertas durante la investigación"
+
+[i18n.es.settings.save_research_log]
+label = "Guardar registro de investigación"
+description = "Guardar consultas de búsqueda detalladas y notas de evaluación de fuentes"
+
+[i18n.es.settings.citation_style]
+label = "Estilo de citación"
+description = "Cómo citar fuentes en los informes"
+
+[i18n.es.settings.language]
+label = "Idioma"
+description = "Idioma principal para la investigación y los resultados"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand de Recherche Approfondie"
+description = "Chercheur autonome en profondeur — recherche exhaustive, références croisées, vérification des faits et rapports structurés"
+category = "Productivité"
+
+[i18n.fr.settings.research_depth]
+label = "Profondeur de recherche"
+description = "Niveau de minutie de chaque investigation"
+
+[i18n.fr.settings.output_style]
+label = "Style de sortie"
+description = "Style de formatage du rapport de recherche"
+
+[i18n.fr.settings.source_verification]
+label = "Vérification des sources"
+description = "Vérifier les affirmations auprès de plusieurs sources avant de citer"
+
+[i18n.fr.settings.max_sources]
+label = "Nombre maximum de sources"
+description = "Nombre maximum de sources à consulter par recherche"
+
+[i18n.fr.settings.auto_follow_up]
+label = "Suivi automatique"
+description = "Rechercher automatiquement les questions de suivi découvertes pendant l'investigation"
+
+[i18n.fr.settings.save_research_log]
+label = "Sauvegarder le journal de recherche"
+description = "Conserver les journaux détaillés des requêtes de recherche et des évaluations de sources"
+
+[i18n.fr.settings.citation_style]
+label = "Style de citation"
+description = "Format de citation des sources dans les rapports"
+
+[i18n.fr.settings.language]
+label = "Langue"
+description = "Langue principale pour la recherche et les résultats"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Tiefenforschungs-Hand"
+description = "Autonomer Tiefenforscher — gründliche Untersuchung, Querverweise, Faktencheck und strukturierte Berichte"
+category = "Produktivität"
+
+[i18n.de.settings.research_depth]
+label = "Forschungstiefe"
+description = "Gründlichkeit jeder Untersuchung"
+
+[i18n.de.settings.output_style]
+label = "Ausgabestil"
+description = "Formatierungsstil des Forschungsberichts"
+
+[i18n.de.settings.source_verification]
+label = "Quellenverifikation"
+description = "Behauptungen vor dem Zitieren mit mehreren Quellen gegenkontrollieren"
+
+[i18n.de.settings.max_sources]
+label = "Maximale Quellen"
+description = "Maximale Anzahl der pro Untersuchung zu konsultierenden Quellen"
+
+[i18n.de.settings.auto_follow_up]
+label = "Automatisches Nachfassen"
+description = "Während der Untersuchung entdeckte Folgefragen automatisch recherchieren"
+
+[i18n.de.settings.save_research_log]
+label = "Forschungsprotokoll speichern"
+description = "Detaillierte Protokolle der Suchabfragen und Quellenbewertungen speichern"
+
+[i18n.de.settings.citation_style]
+label = "Zitierstil"
+description = "Format für Quellenangaben in Berichten"
+
+[i18n.de.settings.language]
+label = "Sprache"
+description = "Hauptsprache für Forschung und Ergebnisse"
diff --git a/hands/researcher/SKILL.md b/hands/researcher/SKILL.md
index e4a3b6c..caca695 100644
--- a/hands/researcher/SKILL.md
+++ b/hands/researcher/SKILL.md
@@ -183,6 +183,179 @@ After synthesis, explicitly note:
 
 ---
 
+## Worked Examples
+
+### Example 1: Technology Adoption Decision
+
+**Question**: "Should our company adopt Rust for backend services?"
+
+**Phase 1 — Define**
+
+Decompose into sub-questions:
+```
+Main: "Should our company adopt Rust for backend services?"
+Sub-questions:
+  1. What are Rust's strengths for backend work? (factual)
+  2. What are the real-world costs of adoption? (factual + case studies)
+  3. How does Rust compare to our current stack (Go) on key metrics? (comparative)
+  4. What do teams of our size (15-30 engineers) report? (case studies)
+  5. What is the hiring/training landscape? (survey)
+  6. What are the migration paths and risks? (how-to + risk analysis)
+```
+
+Scope constraints: Backend HTTP services, team of 20 engineers currently using Go, latency-sensitive workloads, 18-month planning horizon.
+
+**Phase 2 — Search (multi-strategy)**
+```
+Strategy 1 (Direct):         "Rust backend production experience"
+Strategy 2 (Authoritative):  site:arxiv.org "Rust" "memory safety" performance
+Strategy 3 (Practical):      "migrating from Go to Rust" blog OR postmortem
+Strategy 4 (Contrarian):     "Rust backend" problems OR regret OR "not worth"
+Strategy 5 (Data):           "Rust" "developer survey" adoption 2024 2025
+Strategy 6 (Case studies):   site:engineering.*.com Rust adoption
+```
+
+**Phase 3 — Evaluate (CRAAP scoring)**
+```
+Source 1: Rust annual survey (rust-lang.org)          → A (primary, current)
+Source 2: Discord engineering blog on Rust migration   → A (primary, practitioner)
+Source 3: Figma "Rust in production" post              → A (primary, detailed metrics)
+Source 4: Random Medium post "Rust is the future"      → D (no credentials, no data)
+Source 5: AWS SDK for Rust announcement                → B (authoritative, but marketing)
+Source 6: "Why we moved back to Go" blog post          → B (primary experience, single case)
+Source 7: Stack Overflow developer survey              → A (large sample, methodology documented)
+```
+
+Drop Source 4 entirely. Use Source 6 as a counterpoint despite being a single case.
+
+**Phase 4 — Synthesize**
+```
+FINDING 1: Rust delivers measurable performance and reliability gains
+  Evidence for: Discord reported 50% memory reduction after migration [2].
+    Figma measured p99 latency improvements of 3-5x for compute-heavy paths [3].
+  Evidence against: Gains may be marginal for I/O-bound CRUD services [6].
+  Confidence: High for compute-intensive workloads, medium for I/O-bound.
+
+FINDING 2: Adoption cost is front-loaded and significant
+  Evidence for: Average ramp-up time for experienced Go/C++ engineers is
+    3-6 months to productive Rust [2][7]. Compile times 2-5x longer than Go [3].
+  Evidence against: Teams report that after the learning curve, maintenance
+    costs drop due to fewer production incidents [2][3].
+  Confidence: High
+
+FINDING 3: Hiring pipeline is narrow but growing
+  Evidence for: Rust ranks as "most admired" language for 8 consecutive years
+    in SO survey, but only ~13% of developers use it professionally [7].
+  Evidence against: Rust job demand is growing ~40% YoY [7].
+  Confidence: Medium — hiring data is self-reported.
+```
+
+**Phase 5 — Verify and deliver**
+
+Cross-check: Discord and Figma metrics are confirmed by independent engineering talks. SO survey methodology is published and peer-reviewed.
+
+Final recommendation structure:
+```
+Adopt for: Latency-sensitive, compute-heavy services (strong evidence)
+Avoid for: Simple CRUD APIs where Go is already performant (low ROI)
+Mitigate hiring risk: Invest in internal training, start with one team
+Timeline: 6-month pilot on a non-critical service before broader adoption
+Confidence: Medium-high — strong technical evidence, moderate organizational evidence
+```
+
+### Example 2: Incident Analysis
+
+**Question**: "What caused the 2024 CrowdStrike outage and what are the implications?"
+
+**Phase 1 — Define**
+
+This is a causal question with survey elements. Decompose:
+```
+Main: "What caused the 2024 CrowdStrike outage?"
+Sub-questions:
+  1. What happened? (timeline — factual)
+  2. What was the technical root cause? (causal)
+  3. What was the scope of impact? (factual, data)
+  4. How did CrowdStrike respond? (factual)
+  5. What systemic issues does this reveal? (analytical)
+  6. What changed in the industry as a result? (survey + predictive)
+```
+
+**Phase 2 — Search**
+```
+Strategy 1 (Primary):     site:crowdstrike.com "July 2024" postmortem OR incident
+Strategy 2 (Technical):   "CrowdStrike" "channel file" root cause analysis
+Strategy 3 (Impact data): "CrowdStrike outage" damages OR cost OR impact 2024
+Strategy 4 (Regulatory):  site:gov "CrowdStrike" review OR hearing OR testimony
+Strategy 5 (Contrarian):  "CrowdStrike" "kernel driver" criticism before:2024-07-01
+Strategy 6 (Expert):      "CrowdStrike outage" analysis site:*.edu OR site:arxiv.org
+```
+
+Note Strategy 5: searching for pre-incident criticism establishes whether warnings existed.
+
+**Phase 3 — Evaluate and build timeline**
+```
+Timeline (verified — Level 3):
+  2024-07-19 04:09 UTC  CrowdStrike deploys Channel File 291 update
+  2024-07-19 04:09-05:27  Falcon sensor crashes → Windows BSOD on boot
+  2024-07-19 05:27 UTC  CrowdStrike reverts the channel file
+  2024-07-19 ~06:00     Scope becomes apparent: 8.5M Windows devices affected
+  2024-07-19-21          Manual remediation required (boot to Safe Mode, delete file)
+  2024-07-20-25          Airlines, hospitals, banks in multi-day recovery
+
+Sources: CrowdStrike PIR [A], Microsoft blog [A], Reuters reporting [B],
+  Congressional testimony transcript [A]
+```
+
+**Phase 4 — Synthesize root cause**
+```
+FINDING 1: Technical root cause was an out-of-bounds memory read
+  A channel file update (type 291) contained malformed data.
+  The Falcon sensor's Content Interpreter triggered an OOB read,
+  causing a kernel-level crash (BSOD). The sensor ran as a kernel
+  driver, so its crash took down the entire OS.
+  Sources: CrowdStrike PIR [A], independent reverse engineering [B]
+  Confidence: High (confirmed by vendor + independent analysis)
+
+FINDING 2: The update bypassed adequate testing
+  Channel files ("rapid response content") used a different validation
+  pipeline than sensor code. The Template Type tested had 20 input
+  fields; the deployed content provided 21. The validator did not
+  catch the mismatch.
+  Sources: CrowdStrike PIR [A], Congressional testimony [A]
+  Confidence: High
+
+FINDING 3: Impact — $5-10B+ in estimated damages
+  8.5M devices affected (Microsoft estimate). Delta Air Lines alone
+  reported $500M in losses. Parametrix estimated $5.4B in direct
+  losses for Fortune 500 companies.
+  Sources: Microsoft [A], Parametrix [B], Delta SEC filing [A]
+  Confidence: Medium-high (total figure is estimated, individual claims are documented)
+
+FINDING 4: Systemic issue — monoculture risk in security infrastructure
+  A single vendor's kernel-level agent was present on ~24% of
+  enterprise Windows endpoints. Pre-incident criticism of kernel-mode
+  security agents existed but was not widely acted upon.
+  Sources: Congressional hearing [A], pre-incident security research [B]
+  Confidence: High
+```
+
+**Phase 5 — Verify and present implications**
+```
+Verified implications (cross-referenced across 3+ independent sources):
+  1. Regulatory pressure on kernel-mode security agents accelerated
+  2. Microsoft announced Windows Resiliency Initiative (user-mode alternatives)
+  3. Enterprise customers began requiring staged/canary rollout for security updates
+  4. Cyber insurance models updated to account for single-vendor concentration
+
+Remaining uncertainties:
+  - Full financial impact is still in litigation (Delta v. CrowdStrike)
+  - Long-term market share impact on CrowdStrike is unclear
+  - Whether kernel-mode restrictions will actually be enforced
+```
+
+---
+
 ## Citation Formats
 
 ### Inline URL
@@ -325,3 +498,99 @@ Be aware of these biases during research:
 - Check if findings have been replicated
 - Preprints have not been peer-reviewed — note this caveat
 - p-values and effect sizes both matter — not just "statistically significant"
+
+---
+
+## Research Shortcuts
+
+### When to Stop Researching
+
+Research has diminishing returns. Recognize these signals:
+
+**Stop signals — you have enough**:
+- Three independent sources converge on the same answer
+- New searches return sources you have already seen
+- The last 3 searches added no new information or perspectives
+- You have found primary source data that directly answers the question
+- Remaining disagreements are about edge cases, not the core finding
+
+**Keep going signals — you do not have enough**:
+- Only one source supports a critical claim
+- Two credible sources directly contradict each other with no resolution
+- The requester's specific context (industry, scale, constraints) is not addressed
+- You have secondary reporting but no primary source for a key fact
+- Your confidence assessment would be "low" on a central finding
+
+**Time-boxing rule**: For a standard research question, allocate effort roughly as:
+```
+Quick facts:       2-4 searches, 1-2 minutes
+Standard question: 6-12 searches, 5-10 minutes
+Deep dive:         15-30 searches, 20-40 minutes
+```
+If you exceed 2x the expected searches without convergence, stop and report what you have with explicit gaps noted.
+
+### Quick Assessment vs Deep Dive
+
+Not every question deserves a full 5-phase research process. Use this decision matrix:
+
+```
+Quick assessment (skip to synthesis fast):
+  ✓ Question has a single factual answer
+  ✓ Authoritative primary source exists and is accessible
+  ✓ Low stakes — wrong answer has minimal consequences
+  ✓ Requester wants speed over thoroughness
+  Example: "What version of Python dropped GIL?"
+    → Check python.org docs/PEPs, answer in one search.
+
+Standard research (full 5-phase process):
+  ✓ Comparative or analytical question
+  ✓ Multiple valid perspectives exist
+  ✓ Answer will inform a decision
+  ✓ Moderate stakes
+  Example: "React vs Svelte for our new dashboard?"
+    → Full decomposition, multi-source, synthesis needed.
+
+Deep dive (extended research with formal deliverable):
+  ✓ High-stakes decision (architecture, vendor, strategy)
+  ✓ Conflicting information is likely
+  ✓ Historical context and trend analysis needed
+  ✓ Requester expects a report they can share with others
+  Example: "Should we move from AWS to multi-cloud?"
+    → Multiple sub-questions, 10+ sources, formal report.
+```
+
+### Source Reuse Patterns
+
+Not every question starts from zero. Build efficiency by recognizing reusable sources.
+
+**Tier 1 — Canonical references (always check first for their domain)**:
+```
+Programming languages: Official docs, language spec, release notes
+Cloud services:        AWS/GCP/Azure docs, status pages, pricing pages
+Security:              CVE databases, vendor advisories, NIST NVD
+Statistics:            Official census/survey data, World Bank, OECD
+Companies:             SEC filings (EDGAR), official IR pages
+Open source:           GitHub repo, CHANGELOG, issue tracker
+```
+
+**Tier 2 — High-signal aggregators (good starting points)**:
+```
+Technology trends:     ThoughtWorks Radar, Stack Overflow survey, TIOBE
+Security incidents:    CISA advisories, Krebs on Security
+Academic papers:       Google Scholar, Semantic Scholar, arXiv
+Industry analysis:     Gartner (with bias caveat), a16z, Sequoia
+Developer experience:  JetBrains survey, GitHub Octoverse
+```
+
+**Tier 3 — Practitioner sources (for real-world validation)**:
+```
+Engineering blogs:     Company engineering blogs (Netflix, Uber, Stripe, Discord)
+Conference talks:      Recorded talks from Strange Loop, QCon, KubeCon
+Community discussion:  Hacker News (comments often more valuable than articles),
+                       Reddit (r/programming, r/devops, domain-specific subs)
+```
+
+**Anti-patterns to avoid**:
+- Do not reuse a source across topics just because it scored well once — re-evaluate CRAAP for the new topic
+- Do not treat aggregator rankings (Gartner Magic Quadrant, G2 reviews) as primary evidence — they are influenced by vendor spending
+- Do not assume a source's authority transfers across domains — a security vendor's blog is authoritative on threats but not on database performance
diff --git a/hands/strategist/HAND.toml b/hands/strategist/HAND.toml
index a8e4165..c9642ca 100644
--- a/hands/strategist/HAND.toml
+++ b/hands/strategist/HAND.toml
@@ -378,6 +378,241 @@ token_consumption = "medium"
 default_active = true
 activation_warning = "Strategist hand runs continuously and performs strategic analysis, consuming tokens."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "策略分析 Hand"
 description = "自主策略分析师——市场研究、竞争分析、商业规划和战略建议"
+category = "生产力"
+
+[i18n.zh.settings.focus_area]
+label = "聚焦方向"
+description = "战略分析的主要方向"
+
+[i18n.zh.settings.analysis_depth]
+label = "分析深度"
+description = "每次战略分析的详尽程度"
+
+[i18n.zh.settings.industry]
+label = "行业"
+description = "重点分析的行业（例如 SaaS、金融科技、医疗健康）"
+
+[i18n.zh.settings.competitors]
+label = "主要竞争对手"
+description = "需要追踪的竞争对手列表（逗号分隔）"
+
+[i18n.zh.settings.auto_monitor]
+label = "自动监控"
+description = "自动追踪竞争对手动态和市场变化"
+
+[i18n.zh.settings.report_format]
+label = "报告格式"
+description = "战略报告的格式风格"
+
+[i18n.zh.settings.confidence_threshold]
+label = "置信度阈值"
+description = "报告中纳入分析结论的最低置信度"
+
+[i18n.zh.settings.frameworks]
+label = "首选分析框架"
+description = "优先使用的战略分析框架（逗号分隔，例如 SWOT,Porter,PESTEL）"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Estrategia"
+description = "Analista estratégico autónomo — investigación de mercado, análisis competitivo, planificación empresarial y recomendaciones estratégicas"
+category = "Productividad"
+
+[i18n.es.settings.focus_area]
+label = "Área de enfoque"
+description = "Área principal del análisis estratégico"
+
+[i18n.es.settings.analysis_depth]
+label = "Profundidad del análisis"
+description = "Nivel de detalle de cada análisis estratégico"
+
+[i18n.es.settings.industry]
+label = "Industria"
+description = "Industria principal en la que enfocar el análisis (ej. SaaS, fintech, salud)"
+
+[i18n.es.settings.competitors]
+label = "Competidores clave"
+description = "Lista de competidores a rastrear separados por comas"
+
+[i18n.es.settings.auto_monitor]
+label = "Monitoreo automático"
+description = "Rastrear automáticamente movimientos de competidores y cambios del mercado"
+
+[i18n.es.settings.report_format]
+label = "Formato de informe"
+description = "Formato de los informes estratégicos"
+
+[i18n.es.settings.confidence_threshold]
+label = "Umbral de confianza"
+description = "Nivel mínimo de confianza para incluir hallazgos en los informes"
+
+[i18n.es.settings.frameworks]
+label = "Marcos de análisis preferidos"
+description = "Marcos estratégicos a priorizar (separados por comas, ej. SWOT, Porter, PESTEL)"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "戦略分析 Hand"
+description = "自律型戦略アナリスト——市場調査、競合分析、事業計画、戦略的提言"
+category = "生産性"
+
+[i18n.ja.settings.focus_area]
+label = "フォーカスエリア"
+description = "戦略分析の主な対象分野"
+
+[i18n.ja.settings.analysis_depth]
+label = "分析の深さ"
+description = "各戦略分析の詳細度"
+
+[i18n.ja.settings.industry]
+label = "業界"
+description = "分析の対象となる主要業界（例: SaaS、フィンテック、ヘルスケア）"
+
+[i18n.ja.settings.competitors]
+label = "主要な競合"
+description = "追跡する競合のリスト（カンマ区切り）"
+
+[i18n.ja.settings.auto_monitor]
+label = "自動監視"
+description = "競合の動向と市場の変化を自動的に追跡する"
+
+[i18n.ja.settings.report_format]
+label = "レポート形式"
+description = "戦略レポートのフォーマット"
+
+[i18n.ja.settings.confidence_threshold]
+label = "信頼度しきい値"
+description = "レポートに分析結果を含めるための最低信頼度"
+
+[i18n.ja.settings.frameworks]
+label = "優先フレームワーク"
+description = "優先的に使用する戦略分析フレームワーク（カンマ区切り、例: SWOT,Porter,PESTEL）"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Stratégique"
+description = "Analyste stratégique autonome — étude de marché, analyse concurrentielle, planification d'entreprise et recommandations stratégiques"
+category = "Productivité"
+
+[i18n.fr.settings.focus_area]
+label = "Domaine d'intérêt"
+description = "Domaine principal de l'analyse stratégique"
+
+[i18n.fr.settings.analysis_depth]
+label = "Profondeur d'analyse"
+description = "Niveau de détail de chaque analyse stratégique"
+
+[i18n.fr.settings.industry]
+label = "Secteur"
+description = "Secteur principal d'analyse (ex. SaaS, fintech, santé)"
+
+[i18n.fr.settings.competitors]
+label = "Concurrents clés"
+description = "Liste de concurrents à suivre séparée par des virgules"
+
+[i18n.fr.settings.auto_monitor]
+label = "Surveillance automatique"
+description = "Suivre automatiquement les mouvements des concurrents et les évolutions du marché"
+
+[i18n.fr.settings.report_format]
+label = "Format de rapport"
+description = "Format des rapports stratégiques"
+
+[i18n.fr.settings.confidence_threshold]
+label = "Seuil de confiance"
+description = "Niveau de confiance minimum pour inclure les résultats dans les rapports"
+
+[i18n.fr.settings.frameworks]
+label = "Cadres d'analyse préférés"
+description = "Cadres d'analyse stratégique à privilégier (séparés par des virgules, ex. SWOT, Porter, PESTEL)"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Strategie-Hand"
+description = "Autonomer Strategieanalyst — Marktforschung, Wettbewerbsanalyse, Geschäftsplanung und strategische Empfehlungen"
+category = "Produktivität"
+
+[i18n.de.settings.focus_area]
+label = "Fokusbereich"
+description = "Hauptbereich der strategischen Analyse"
+
+[i18n.de.settings.analysis_depth]
+label = "Analysetiefe"
+description = "Detailgrad jeder strategischen Analyse"
+
+[i18n.de.settings.industry]
+label = "Branche"
+description = "Hauptbranche für die Analyse (z.B. SaaS, Fintech, Gesundheitswesen)"
+
+[i18n.de.settings.competitors]
+label = "Wichtige Wettbewerber"
+description = "Kommagetrennte Liste der zu verfolgenden Wettbewerber"
+
+[i18n.de.settings.auto_monitor]
+label = "Automatische Überwachung"
+description = "Wettbewerberbewegungen und Marktveränderungen automatisch verfolgen"
+
+[i18n.de.settings.report_format]
+label = "Berichtsformat"
+description = "Format der Strategieberichte"
+
+[i18n.de.settings.confidence_threshold]
+label = "Konfidenzschwelle"
+description = "Mindest-Konfidenzniveau für die Aufnahme von Ergebnissen in Berichte"
+
+[i18n.de.settings.frameworks]
+label = "Bevorzugte Analyse-Frameworks"
+description = "Bevorzugte strategische Analyse-Frameworks (kommagetrennt, z.B. SWOT, Porter, PESTEL)"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "전략 분석 Hand"
+description = "자율 전략 분석가 — 시장 조사, 경쟁 분석, 사업 계획 및 전략적 권고"
+category = "생산성"
+
+[i18n.ko.settings.focus_area]
+label = "집중 분야"
+description = "전략 분석의 주요 방향"
+
+[i18n.ko.settings.analysis_depth]
+label = "분석 깊이"
+description = "각 전략 분석의 철저함 정도"
+
+[i18n.ko.settings.industry]
+label = "산업"
+description = "분석의 중점 산업 (예: SaaS, 핀테크, 헬스케어)"
+
+[i18n.ko.settings.competitors]
+label = "주요 경쟁사"
+description = "추적할 경쟁사 목록 (쉼표로 구분)"
+
+[i18n.ko.settings.auto_monitor]
+label = "자동 모니터링"
+description = "경쟁사 동향 및 시장 변화를 자동으로 추적"
+
+[i18n.ko.settings.report_format]
+label = "보고서 형식"
+description = "전략 보고서의 형식 스타일"
+
+[i18n.ko.settings.confidence_threshold]
+label = "신뢰도 임계값"
+description = "보고서에 분석 결과를 포함하기 위한 최소 신뢰도"
+
+[i18n.ko.settings.frameworks]
+label = "선호 분석 프레임워크"
+description = "우선적으로 사용할 전략 분석 프레임워크 (쉼표로 구분, 예: SWOT,Porter,PESTEL)"
diff --git a/hands/strategist/SKILL.md b/hands/strategist/SKILL.md
index dd5e8bd..e8e9d98 100644
--- a/hands/strategist/SKILL.md
+++ b/hands/strategist/SKILL.md
@@ -236,3 +236,725 @@ Employee Count: [Growth indicator]
 ## Implementation
 [How to execute the recommendation]
 ```
+
+---
+
+## Worked Examples
+
+### Example 1: B2B SaaS Market Entry into Japan
+
+**Context**: A US-based B2B SaaS company (project management tool, $15M ARR, 200 employees) evaluating entry into the Japanese market.
+
+**PESTEL Analysis — Japan B2B SaaS (2025):**
+
+| Factor | Assessment | Impact | Score (1-5) |
+|--------|-----------|--------|-------------|
+| **Political** | Stable democracy; strong US-Japan trade relations; Digital Agency pushing government digitization | Positive | 4 |
+| **Economic** | GDP $4.2T; weak yen (150 JPY/USD) makes USD-priced SaaS expensive; enterprise IT spend growing 4% YoY | Mixed | 3 |
+| **Social** | Aging workforce accelerates automation need; consensus-driven decision making lengthens sales cycles (avg 6-9 months); strong preference for local-language support | Critical constraint | 2 |
+| **Technological** | High internet penetration (93%); cloud adoption lagging US by 3-5 years but accelerating; 5G rollout complete in urban areas | Opportunity | 4 |
+| **Environmental** | ESG reporting mandated for listed companies from 2023; sustainability-linked procurement gaining traction | Moderate opportunity | 3 |
+| **Legal** | APPI (Act on Protection of Personal Information) requires data residency consideration; strict labor laws affect HR SaaS | Compliance cost | 2 |
+
+**PESTEL Score**: 18/30 — Moderately favorable. Key risk: social/cultural factors demand significant localization investment.
+
+**Porter's Five Forces — Japan Project Management SaaS:**
+
+| Force | Rating | Evidence |
+|-------|--------|---------|
+| New Entrants | 2/5 | High localization cost ($500K-$1M); relationship-driven market favors incumbents |
+| Supplier Power | 1/5 | Cloud infrastructure (AWS Tokyo, Azure Japan) is commodity; no supplier concentration |
+| Buyer Power | 4/5 | Enterprise buyers demand customization; long procurement cycles give buyers leverage; RFP-driven purchasing |
+| Substitutes | 3/5 | Excel/spreadsheet culture deeply entrenched; domestic tools (Backlog, Jooto) have cultural fit advantage |
+| Rivalry | 4/5 | Asana, Monday.com, Notion already present; domestic players Backlog (Nulab) and Redmine have loyal bases |
+
+**Go-to-Market Recommendation:**
+
+```
+Strategy: Partner-Led Entry (not direct sales)
+Timeline: 18 months to first enterprise deal
+
+Phase 1 (Months 1-6): Foundation
+  - Hire Country Manager (must be bilingual Japanese national)
+  - Full UI/UX localization (not just translation — date formats, name order, honorifics)
+  - Achieve ISMAP certification (required for government/enterprise procurement)
+  - Data residency: Deploy on AWS Tokyo region
+  - Budget: $800K
+
+Phase 2 (Months 4-12): Channel Development
+  - Sign 2-3 SIer (System Integrator) partners: target NTT Data, Fujitsu, NEC
+  - Japanese SIers control 60% of enterprise software purchasing decisions
+  - Co-develop integration with domestic tools (kintone, Sansan, freee)
+  - Budget: $600K (partner enablement + integration development)
+
+Phase 3 (Months 8-18): Market Penetration
+  - Target mid-market first (500-2000 employees) — faster decision cycles than enterprise
+  - Launch at Japan IT Week (Spring/Autumn) and SaaS Industry Conference
+  - Content marketing: Japanese-language case studies, webinars with local customers
+  - Target: 20 paying customers, $500K ARR by month 18
+  - Budget: $400K
+
+Total Investment: $1.8M over 18 months
+Break-even: Month 30 (projected)
+```
+
+**Decision**: Proceed with caution. The $4.2T economy and cloud adoption tailwind justify the investment, but only with proper localization and channel strategy. Direct sales without SIer partnerships has a historically high failure rate (>70% for foreign SaaS in Japan).
+
+---
+
+### Example 2: Competitive Response — Major Player Enters Your Niche
+
+**Context**: You run a $5M ARR vertical SaaS for veterinary clinics (500 customers, 15% market share). Salesforce just announced "Salesforce for Veterinary" — a vertical solution built on their platform.
+
+**Threat Assessment:**
+
+| Dimension | Your Position | Salesforce | Gap |
+|-----------|--------------|------------|-----|
+| Brand recognition | Niche leader | Global enterprise brand | Large — but irrelevant in vet niche |
+| Product depth | Purpose-built (8 years domain expertise) | Horizontal platform with vertical skin | Strong advantage |
+| Price point | $200/mo per clinic | $500/mo estimated (Salesforce pricing) | 2.5x cheaper |
+| Implementation time | 2 weeks | 3-6 months (typical SF implementation) | Strong advantage |
+| Integration depth | Deep PMS/PIMS integration | API-based, requires middleware | Strong advantage |
+| Sales motion | Direct + word-of-mouth | Enterprise sales team + SI partners | Different segments |
+| Switching cost for your customers | Moderate (data migration + retraining) | High (Salesforce ecosystem lock-in) | Neutral |
+
+**Strategic Response Framework:**
+
+```
+IMMEDIATE (Week 1-4): Defend the Base
+  1. Customer communication campaign
+     - CEO letter to all 500 customers: "Our commitment to veterinary"
+     - Emphasize: purpose-built > horizontal platform
+     - Announce product roadmap acceleration
+
+  2. Lock in at-risk accounts
+     - Identify top 50 accounts by revenue
+     - Offer annual contract discounts (15-20% for 2-year commitment)
+     - Schedule QBRs with all enterprise accounts within 30 days
+
+  3. Competitive battle card
+     - Create internal sales doc: feature-by-feature comparison
+     - "Why vets choose us over Salesforce" — 5 key differentiators
+     - Objection handling for "shouldn't we go with the safe choice?"
+
+SHORT-TERM (Month 2-6): Deepen the Moat
+  4. Accelerate domain-specific features
+     - AI-powered treatment plan suggestions (Salesforce can't match this)
+     - Telemedicine integration (vertical-specific)
+     - Inventory management tied to treatment protocols
+
+  5. Build switching costs
+     - Launch data analytics dashboard (clinics depend on historical trends)
+     - Introduce multi-location management (target growing chains)
+     - API marketplace for vet-specific integrations (lab equipment, imaging)
+
+  6. Community defense
+     - Launch "Vet Tech Community" — user forum + knowledge base
+     - Annual user conference (even virtual — creates tribal loyalty)
+     - Customer advisory board (top 10 clinics = co-development partners)
+
+MEDIUM-TERM (Month 6-18): Counterattack
+  7. Move upmarket selectively
+     - Enterprise tier for 10+ location chains ($500/mo — match SF pricing)
+     - Offer white-glove migration from legacy systems
+     - This is the segment Salesforce will target — contest it
+
+  8. Geographic expansion
+     - Salesforce announcement creates awareness of the category
+     - Ride the wave: "Already purpose-built, already proven"
+     - Target UK, Australia, Canada (English-speaking, similar vet market structure)
+```
+
+**Pricing Response Decision Matrix:**
+
+| Option | Revenue Impact | Competitive Effect | Risk |
+|--------|---------------|-------------------|------|
+| No change | Neutral | Salesforce still 2.5x more expensive | Low — price isn't the battleground |
+| Cut prices 20% | -$1M ARR | Signals weakness; Salesforce won't match | High |
+| Add premium tier | +$500K potential | Compete at enterprise level; justify R&D | Medium |
+| Usage-based addon | +$300K potential | Expand ARPU without base price war | Low |
+
+**Recommendation**: Add premium tier + usage-based addons. Do NOT cut base prices. Salesforce's entry validates your market — use it to raise your valuation narrative ("Salesforce sees a $2B market opportunity in vet SaaS — we already own 15%").
+
+**Confidence**: Medium-High (75%) — Historical pattern: when Salesforce enters verticals, purpose-built incumbents retain 80%+ of existing customers. Risk is in new customer acquisition where brand matters more.
+
+---
+
+### Example 3: Platform Sunset Decision — Migrate or Maintain Legacy Product
+
+**Context**: A mid-stage startup ($20M ARR) runs two products: a legacy desktop app (60% of revenue, declining 10% YoY) and a modern cloud product (40% of revenue, growing 50% YoY). Should they sunset the desktop app?
+
+**Decision Matrix:**
+
+| Criterion (Weight) | Option A: Maintain Both | Option B: Sunset in 12mo | Option C: Sunset in 24mo |
+|--------------------|------------------------|--------------------------|--------------------------|
+| Revenue protection (30%) | 5 — No disruption | 2 — Lose 40% of legacy revenue | 4 — Gradual migration |
+| Engineering efficiency (25%) | 1 — Two codebases drain resources | 5 — Full focus on cloud | 3 — Phased transition |
+| Customer satisfaction (20%) | 3 — Legacy stagnates | 2 — Forced migration angers users | 4 — Supported migration path |
+| Market positioning (15%) | 2 — Confused narrative | 5 — Clear cloud-first story | 4 — Transitional narrative |
+| Financial risk (10%) | 3 — Slow bleed sustainable | 2 — Revenue cliff risk | 4 — Manageable decline |
+| **Weighted Score** | **2.95** | **3.35** | **3.75** |
+
+**Recommendation**: Option C — 24-month sunset with structured migration program.
+
+```
+Migration Program:
+  Months 1-6:   Feature parity audit; build top 20 missing cloud features
+  Months 7-12:  Migration incentive (20% discount for annual cloud commitment)
+  Months 13-18: Desktop enters maintenance-only mode; no new features
+  Months 19-24: End-of-life announcement; dedicated migration support team
+  Month 24:     Desktop product sunsets; legacy support for 6 more months
+
+Financial Model:
+  Current state:     $12M desktop + $8M cloud = $20M ARR
+  Month 12 (projected): $9M desktop + $14M cloud = $23M ARR
+  Month 24 (projected): $2M desktop + $22M cloud = $24M ARR
+  Month 30 (projected): $0 desktop + $26M cloud = $26M ARR
+
+  Net ARR risk: ~$3M from non-migrating desktop customers
+  Offset: Engineering savings of $1.5M/yr + faster cloud feature velocity
+```
+
+---
+
+## Financial Analysis Frameworks
+
+### Unit Economics
+
+Core metrics every strategy should quantify:
+
+```
+CAC (Customer Acquisition Cost)
+  = Total Sales & Marketing Spend / New Customers Acquired
+  Example: $500K spend / 100 new customers = $5,000 CAC
+
+LTV (Lifetime Value)
+  = ARPU x Gross Margin % x (1 / Churn Rate)
+  Example: $500/mo x 80% x (1 / 0.03) = $13,333 LTV
+
+LTV:CAC Ratio
+  Target: > 3:1 for healthy SaaS
+  Example: $13,333 / $5,000 = 2.67:1 (below target — reduce CAC or increase retention)
+
+CAC Payback Period
+  = CAC / (ARPU x Gross Margin %)
+  Example: $5,000 / ($500 x 0.80) = 12.5 months
+  Target: < 18 months for SaaS
+```
+
+**Unit Economics Health Check:**
+
+| Metric | Danger Zone | Acceptable | Excellent |
+|--------|------------|------------|-----------|
+| LTV:CAC | < 1:1 | 3:1 | > 5:1 |
+| CAC Payback | > 24 months | 12-18 months | < 12 months |
+| Gross Margin | < 60% | 70-80% | > 80% |
+| Net Revenue Retention | < 90% | 100-110% | > 120% |
+| Logo Churn (monthly) | > 5% | 2-3% | < 1% |
+
+### Revenue Modeling
+
+**SaaS Revenue Waterfall:**
+
+```
+Beginning ARR:                    $10,000,000
+  + New Business:                 +$3,000,000  (new logos)
+  + Expansion:                    +$1,500,000  (upsell/cross-sell)
+  - Contraction:                    -$500,000  (downgrades)
+  - Churn:                        -$1,200,000  (lost customers)
+  = Ending ARR:                   $12,800,000
+
+Net New ARR:         $2,800,000
+Net Revenue Retention:   113%  = ($10M + $1.5M - $0.5M - $1.2M) / $10M
+Gross Revenue Retention:  88%  = ($10M - $0.5M - $1.2M) / $10M
+```
+
+**MRR Growth Decomposition:**
+
+```
+MRR Growth Rate = New MRR + Expansion MRR - Churned MRR - Contraction MRR
+                  ─────────────────────────────────────────────────────────
+                                     Beginning MRR
+
+Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)
+Target: > 4 for high-growth SaaS
+```
+
+### Break-Even Analysis
+
+```
+Break-Even Revenue = Fixed Costs / Gross Margin %
+
+Example:
+  Fixed Costs (monthly): $200K (salaries, rent, tools)
+  Gross Margin: 80%
+  Break-Even Revenue = $200K / 0.80 = $250K/month = $3M ARR
+
+Break-Even Customers = Break-Even Revenue / ARPU
+  = $250K / $500 = 500 customers
+```
+
+**Scenario Table:**
+
+| Scenario | Fixed Costs | Gross Margin | Break-Even ARR | Break-Even Customers |
+|----------|------------|--------------|-----------------|---------------------|
+| Lean | $150K/mo | 85% | $2.1M | 353 |
+| Base | $200K/mo | 80% | $3.0M | 500 |
+| Growth | $350K/mo | 75% | $5.6M | 933 |
+
+### Project Evaluation — Simplified DCF
+
+Use for evaluating strategic investments (new market entry, build vs buy, major feature investment):
+
+```
+NPV = Σ [Cash Flow_t / (1 + r)^t] - Initial Investment
+
+Where:
+  r = discount rate (typically 10-15% for startups, 8-10% for established companies)
+  t = year (0, 1, 2, ... n)
+```
+
+**Worked Example — Should we build a mobile app?**
+
+```
+Initial Investment: $500K (development cost)
+Discount Rate: 12%
+
+Year  | Incremental Revenue | Incremental Cost | Net Cash Flow | PV Factor | Present Value
+------|--------------------|--------------------|---------------|-----------|-------------
+  0   | $0                 | $500,000           | -$500,000     | 1.000     | -$500,000
+  1   | $200,000           | $80,000            | $120,000      | 0.893     | $107,143
+  2   | $400,000           | $100,000           | $300,000      | 0.797     | $239,158
+  3   | $600,000           | $120,000           | $480,000      | 0.712     | $341,655
+  4   | $700,000           | $130,000           | $570,000      | 0.636     | $362,204
+
+NPV = $550,160  → Positive NPV → Project is financially justified
+Payback Period: ~2.3 years (cumulative cash flow turns positive in Year 3)
+```
+
+**Decision Rule:**
+- NPV > 0 → Proceed (project creates value)
+- NPV < 0 → Reject (project destroys value)
+- Compare NPV across mutually exclusive options; pick highest
+
+---
+
+## Go-to-Market Strategy Patterns
+
+### Growth Motion Selection
+
+| Growth Motion | Best For | Sales Cycle | CAC | Key Metric |
+|--------------|---------|-------------|-----|------------|
+| **Product-Led Growth (PLG)** | Self-serve products; low price point (<$500/mo); individual users | Minutes to days | Low ($50-$500) | Activation rate, PQL conversion |
+| **Sales-Led Growth** | Enterprise products; complex deployment; >$50K ACV | Weeks to months | High ($5K-$50K) | Pipeline velocity, win rate |
+| **Community-Led Growth** | Developer tools; open-source; platform products | Varies | Very low ($10-$100) | Community size, contribution rate |
+| **Partner-Led Growth** | Market entry; regulated industries; ecosystem products | Varies | Medium ($1K-$10K) | Partner-sourced revenue % |
+
+**PLG Funnel:**
+```
+Visitor → Sign-up → Activated User → PQL → Paid Customer → Expanded Account
+  100%     10%        40%             25%      15%            30%
+
+Key levers:
+  - Sign-up friction: Reduce form fields, add SSO
+  - Time-to-value: Get user to "aha moment" in < 5 minutes
+  - PQL definition: User hits usage threshold that correlates with purchase
+  - Expansion trigger: Team features, usage limits, premium capabilities
+```
+
+**Sales-Led Funnel:**
+```
+Lead → MQL → SQL → Opportunity → Proposal → Closed Won
+ 100%   20%   50%     60%          70%        30%
+
+Key levers:
+  - Lead quality: ICP fit scoring
+  - MQL→SQL handoff: Alignment between marketing and sales
+  - Discovery: Deep pain identification
+  - Champion building: Enable internal advocate
+  - Procurement: Legal/security review preparation
+```
+
+### Pricing Strategy Frameworks
+
+**Value-Based Pricing (recommended for most SaaS):**
+```
+1. Quantify customer value created
+   Example: Your tool saves 10 hours/week per user
+   Value = 10 hrs x $75/hr x 52 weeks = $39,000/year
+
+2. Capture 10-20% of value created
+   Price = $39,000 x 15% = $5,850/year = $487/month
+
+3. Validate with willingness-to-pay research
+   Van Westendorp Price Sensitivity Meter:
+   - "At what price is this too expensive?" → $600/mo
+   - "At what price is this a bargain?" → $200/mo
+   - "At what price does it seem expensive but you'd still consider?" → $450/mo
+   - "At what price does it seem too cheap to trust?" → $100/mo
+   → Optimal price range: $200-$450/mo
+```
+
+**Pricing Tier Architecture:**
+
+```
+Tier Structure (Good-Better-Best):
+
+| | Starter | Professional | Enterprise |
+|---|---------|-------------|------------|
+| Target | Individual/SMB | Mid-market team | Large organization |
+| Price | $29/mo | $99/mo/user | Custom (>$500/mo) |
+| Anchor role | Drive adoption | Revenue driver (~60% of revenue) | Margin driver |
+| Features | Core functionality | Full platform | Custom + SLA + support |
+| Support | Self-serve/email | Priority email + chat | Dedicated CSM + phone |
+| Billing | Monthly/Annual | Annual preferred | Annual contract |
+
+Design principles:
+  - Middle tier should be the obvious best value
+  - Top tier exists to make middle tier look reasonable (anchoring effect)
+  - Feature gates should align with natural usage growth
+  - Price metric should scale with value received (per user, per GB, per transaction)
+```
+
+**Competitive Pricing Analysis:**
+
+```
+Competitor Price Map:
+
+Competitor    | Entry Price | Mid-Tier | Enterprise | Price Metric
+-------------|-------------|----------|------------|-------------
+Competitor A  | $49/mo      | $149/mo  | Custom     | Per user
+Competitor B  | $0 (free)   | $99/mo   | $299/mo    | Flat rate
+Competitor C  | $29/mo      | $79/mo   | Custom     | Per user
+Your Product  | ???         | ???      | ???        | ???
+
+Positioning options:
+  - Price leader: 20-30% below average → requires cost advantage
+  - Value leader: At or above average → requires clear differentiation
+  - Premium: 30%+ above average → requires brand and feature superiority
+```
+
+### Channel Strategy
+
+| Channel | Margin | Control | Scale | Best For |
+|---------|--------|---------|-------|----------|
+| Direct sales | High (85-95%) | Full | Slow | Enterprise, complex products |
+| Inside sales | High (80-90%) | Full | Medium | Mid-market, $5K-$50K ACV |
+| Self-serve | Highest (95%+) | Full | Fast | PLG, low ACV |
+| Reseller/VAR | Low (60-70%) | Medium | Medium | Regional coverage, compliance |
+| Marketplace (AWS/Azure) | Low (70-85%) | Low | Fast | Enterprise procurement shortcuts |
+| System Integrator | Low (50-70%) | Low | Medium | Complex implementations |
+| Affiliate/Referral | High (80-90%) | Low | Fast | Consumer, SMB |
+
+### Launch Playbook Template
+
+```
+LAUNCH PLAYBOOK: [Product/Feature Name]
+Launch Date: YYYY-MM-DD
+Launch Type: [Major / Minor / Feature / Beta]
+
+PRE-LAUNCH (T-8 weeks to T-0)
+  Week -8: Finalize positioning and messaging
+  Week -6: Create sales enablement materials (battle cards, one-pagers, demo script)
+  Week -4: Brief analyst relations (Gartner, Forrester) if applicable
+  Week -3: Seed beta customers (5-10 design partners); collect testimonials
+  Week -2: Pre-brief press/media under embargo
+  Week -1: Internal all-hands; sales team training; support team training
+
+LAUNCH DAY (T-0)
+  - Blog post (SEO-optimized)
+  - Email to customer base
+  - Social media campaign (LinkedIn, Twitter/X)
+  - Press release (if major launch)
+  - Product Hunt submission (if applicable)
+  - In-app announcement for existing users
+  - Founder/CEO LinkedIn post (highest engagement channel)
+
+POST-LAUNCH (T+1 to T+8 weeks)
+  Week +1: Monitor activation metrics; respond to all feedback
+  Week +2: Publish customer case study
+  Week +4: Webinar / live demo for pipeline
+  Week +6: Analyze launch metrics vs targets
+  Week +8: Retrospective and iteration plan
+
+METRICS TO TRACK:
+  - Awareness: Blog views, social impressions, press mentions
+  - Activation: Sign-ups, trial starts, feature adoption rate
+  - Revenue: Pipeline generated, deals influenced, new ARR
+  - Sentiment: NPS from beta users, social sentiment, support ticket volume
+```
+
+---
+
+## Scenario Planning
+
+### Best / Base / Worst Case Framework
+
+Structure every major strategic decision with three scenarios:
+
+```
+SCENARIO PLANNING: [Decision or Initiative]
+
+                    | Worst Case      | Base Case       | Best Case
+--------------------|-----------------|-----------------|------------------
+Revenue impact      | [quantify]      | [quantify]      | [quantify]
+Timeline            | [duration]      | [duration]      | [duration]
+Key assumption      | [what goes wrong]| [most likely]   | [what goes right]
+Probability         | [15-25%]        | [50-60%]        | [15-25%]
+Trigger indicators  | [early signals] | [tracking metrics]| [early signals]
+Response plan       | [pivot/exit]    | [continue/adjust]| [accelerate/expand]
+```
+
+**Worked Example — Launching a New Product Line:**
+
+```
+SCENARIO PLANNING: Launch enterprise analytics add-on ($200/mo)
+
+                    | Worst Case (20%)  | Base Case (55%)   | Best Case (25%)
+--------------------|-------------------|-------------------|-------------------
+Adoption rate       | 5% of customers   | 15% of customers  | 30% of customers
+Year 1 revenue      | $120K             | $360K             | $720K
+Development cost    | $400K             | $400K             | $400K
+Year 1 ROI          | -70%              | -10%              | +80%
+Break-even          | Never (kill it)   | Month 18          | Month 8
+Key assumption      | Customers don't   | Moderate demand;  | Strong demand;
+                    | see value; churn  | gradual adoption  | pulls forward
+                    | increases 2%      |                   | enterprise deals
+
+Trigger Indicators:
+  Worst: < 3% adoption after 3 months; NPS < 20 for add-on
+  Base:  8-12% adoption after 3 months; positive but slow pipeline
+  Best:  > 20% adoption after 3 months; inbound enterprise interest
+
+Response Plans:
+  Worst: Pivot to bundling analytics into existing plan (retention play)
+  Base:  Continue; invest in onboarding and customer education
+  Best:  Hire dedicated analytics PM; accelerate roadmap; raise prices 20%
+```
+
+**Expected Value Calculation:**
+
+```
+Expected Revenue = (Worst Revenue x Worst Prob) + (Base Revenue x Base Prob) + (Best Revenue x Best Prob)
+                 = ($120K x 0.20) + ($360K x 0.55) + ($720K x 0.25)
+                 = $24K + $198K + $180K
+                 = $402K
+
+Expected ROI = ($402K - $400K) / $400K = 0.5%
+→ Marginal on expected value alone — proceed only if strategic upside justifies the bet
+```
+
+### Sensitivity Analysis
+
+Identify which variables have the highest impact on outcomes:
+
+```
+SENSITIVITY ANALYSIS: New Market Entry
+
+Base Case NPV: $550K
+
+Variable            | -20% Change    | Base     | +20% Change    | Sensitivity
+--------------------|---------------|----------|----------------|------------
+Customer price      | $280K (-49%)  | $550K    | $820K (+49%)   | HIGH
+Customer volume     | $310K (-44%)  | $550K    | $790K (+44%)   | HIGH
+Churn rate          | $720K (+31%) | $550K    | $380K (-31%)   | HIGH
+Development cost    | $650K (+18%) | $550K    | $450K (-18%)   | MEDIUM
+CAC                 | $610K (+11%) | $550K    | $490K (-11%)   | MEDIUM
+Discount rate       | $590K (+7%)  | $550K    | $510K (-7%)    | LOW
+```
+
+**Interpretation**: Price and volume are the highest-leverage variables. Strategy should prioritize pricing power and demand generation over cost optimization.
+
+**Tornado Chart Format (text representation):**
+
+```
+Variable Impact on NPV (base = $550K):
+
+Customer price     |████████████████████| -49% to +49%
+Customer volume    |███████████████████ | -44% to +44%
+Churn rate         |██████████████      | -31% to +31%
+Development cost   |█████████           | -18% to +18%
+CAC                |██████              | -11% to +11%
+Discount rate      |████                | -7% to +7%
+```
+
+### Risk-Adjusted Decision Making
+
+**Risk Register Template:**
+
+| Risk | Probability (1-5) | Impact (1-5) | Risk Score | Mitigation | Residual Risk |
+|------|-------------------|-------------|------------|------------|---------------|
+| Key hire doesn't work out | 3 | 4 | 12 | Pipeline of 2 backup candidates | 6 |
+| Competitor launches first | 4 | 3 | 12 | Focus on differentiation not speed | 8 |
+| Technical architecture fails to scale | 2 | 5 | 10 | Prototype load test at 10x before commit | 4 |
+| Regulatory change blocks approach | 1 | 5 | 5 | Legal review + pivot plan documented | 3 |
+| Customer demand lower than projected | 3 | 4 | 12 | Pre-sell to 10 design partners before building | 6 |
+
+**Risk-Adjusted NPV:**
+```
+Risk-Adjusted NPV = Base NPV x (1 - Risk Discount)
+
+Where Risk Discount = Σ (Probability x Impact x Weight) for all material risks
+
+Example:
+  Base NPV: $550K
+  Combined risk score: 0.15 (derived from risk register)
+  Risk-Adjusted NPV: $550K x (1 - 0.15) = $467.5K
+```
+
+---
+
+## Industry Analysis Templates
+
+### Market Landscape Map
+
+Plot all players in a market on two strategic dimensions:
+
+```
+MARKET LANDSCAPE: [Industry/Category]
+
+                        Enterprise-Grade
+                              |
+                    Quadrant 2|  Quadrant 1
+                    Niche     |  Market Leaders
+                    Enterprise|
+         Narrow ──────────────┼────────────── Broad
+         Solution             |               Platform
+                    Quadrant 3|  Quadrant 4
+                    Point     |  Mass-Market
+                    Solutions |  Platforms
+                              |
+                         SMB-Focused
+
+Example — Project Management SaaS (2025):
+
+Quadrant 1 (Leaders):   Asana, Monday.com, Smartsheet
+Quadrant 2 (Niche):     Targetprocess (SAFe), Planview (PPM), Kantata (services)
+Quadrant 3 (Point):     Todoist, Basecamp, Trello
+Quadrant 4 (Platforms): Notion, ClickUp, Microsoft Planner
+
+Your Position: [X]
+Desired Position: [→ direction of strategic movement]
+```
+
+**Building a Landscape Map:**
+
+1. Select two dimensions that represent the most important strategic trade-offs in the market
+2. Commonly used axes:
+   - Price / Complexity
+   - Breadth of platform / Depth of solution
+   - Enterprise / SMB focus
+   - Horizontal / Vertical specialization
+   - Self-serve / High-touch
+3. Plot all known competitors (minimum 8-10 for useful map)
+4. Identify white space — under-served quadrant combinations
+5. Draw your strategic vector — where are you moving and why?
+
+### Technology Adoption Lifecycle Positioning
+
+```
+THE ADOPTION CURVE:
+
+  Innovators   Early        Early       Late        Laggards
+  (2.5%)      Adopters     Majority    Majority     (16%)
+              (13.5%)      (34%)       (34%)
+     ___
+    /   \
+   /     \____
+  /            \________
+ /                      \_________
+/                                  \___
+
+       ↑                ↑
+    THE CHASM      MAINSTREAM
+    (biggest       (revenue
+     risk point)    acceleration)
+```
+
+**Positioning by Stage:**
+
+| Stage | Customer Profile | Sales Approach | Pricing Strategy | Key Risk |
+|-------|-----------------|----------------|------------------|----------|
+| Innovators | Tech enthusiasts; will tolerate bugs | Community; direct outreach | Free/very low; usage-based | Building for wrong use case |
+| Early Adopters | Visionaries; want competitive advantage | Consultative selling; pilots | Value-based; ROI-justified | Chasm — can't cross to mainstream |
+| Early Majority | Pragmatists; want proven solutions | References; case studies; demos | Competitive; published pricing | Scaling sales and support |
+| Late Majority | Conservatives; want complete solutions | Standard procurement; RFPs | Bundled; enterprise agreements | Margin compression |
+| Laggards | Skeptics; forced by circumstance | Compliance-driven; mandates | Legacy pricing; long contracts | Market is commoditizing |
+
+**Chasm-Crossing Checklist:**
+```
+□ Whole product: Does the product solve the complete use case without workarounds?
+□ References: Do you have 3-5 referenceable customers in the target segment?
+□ Repeatability: Can you sell and implement without founder involvement?
+□ Support: Can you support customers at scale (not just white-glove)?
+□ Positioning: Is the messaging pragmatist-friendly (ROI, risk reduction) not visionary?
+□ Competition: Have you defined the competitive set for pragmatist comparison?
+□ Pricing: Is pricing simple, transparent, and aligned with buyer expectations?
+```
+
+### Value Chain Analysis
+
+Decompose industry activities to find competitive advantage:
+
+```
+VALUE CHAIN: [Industry]
+
+PRIMARY ACTIVITIES:
+┌─────────────┬──────────────┬──────────────┬──────────────┬──────────────┐
+│  Inbound    │  Operations  │  Outbound    │  Marketing   │  Service     │
+│  Logistics  │              │  Logistics   │  & Sales     │              │
+├─────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
+│ Sourcing    │ Production   │ Distribution │ Branding     │ Support      │
+│ Inventory   │ Quality      │ Delivery     │ Pricing      │ Maintenance  │
+│ Supplier    │ Assembly     │ Warehousing  │ Channel mgmt │ Returns      │
+│ management  │ Testing      │ Order mgmt   │ Positioning  │ Training     │
+└─────────────┴──────────────┴──────────────┴──────────────┴──────────────┘
+
+SUPPORT ACTIVITIES:
+┌──────────────────────────────────────────────────────────────────────────┐
+│ Infrastructure: Finance, Legal, Management, Planning                     │
+│ Human Resources: Recruiting, Training, Compensation, Culture             │
+│ Technology: R&D, IT systems, Automation, Data analytics                  │
+│ Procurement: Vendor selection, Negotiation, Contract management          │
+└──────────────────────────────────────────────────────────────────────────┘
+```
+
+**Analysis Process:**
+
+```
+For each activity:
+  1. Cost: What % of total cost does this activity represent?
+  2. Value: How much does this activity contribute to customer willingness-to-pay?
+  3. Capability: Rate your performance vs competitors (1-5)
+  4. Strategic importance: Is this a source of differentiation? (Yes/No)
+
+Activity             | Cost % | Value Contribution | Capability | Differentiator?
+---------------------|--------|-------------------|------------|----------------
+Inbound logistics    | 15%    | Low               | 3/5        | No
+Operations           | 25%    | High              | 4/5        | Yes
+Outbound logistics   | 10%    | Medium            | 3/5        | No
+Marketing & Sales    | 30%    | High              | 2/5        | Needs improvement
+Service              | 20%    | High              | 5/5        | Yes
+
+Strategic Implications:
+  - Invest: Operations (current strength + high value) and Service (strength to protect)
+  - Improve: Marketing & Sales (high cost + low capability = drag on growth)
+  - Optimize: Logistics (non-differentiating — minimize cost)
+```
+
+**SaaS-Specific Value Chain:**
+
+```
+┌────────────┬───────────────┬──────────────┬────────────────┬─────────────┐
+│ Product    │ Customer      │ Customer     │ Customer       │ Expansion   │
+│ Development│ Acquisition   │ Onboarding   │ Success        │ & Retention │
+├────────────┼───────────────┼──────────────┼────────────────┼─────────────┤
+│ R&D        │ Marketing     │ Implementation│ Support       │ Upsell      │
+│ Design     │ Sales         │ Training     │ Account mgmt  │ Cross-sell  │
+│ QA         │ Partnerships  │ Migration    │ Health scoring │ Renewals    │
+│ Platform   │ Growth/PLG    │ Integration  │ Community      │ Advocacy    │
+└────────────┴───────────────┴──────────────┴────────────────┴─────────────┘
+
+Key insight for SaaS: The majority of LTV is created AFTER the initial sale.
+Disproportionate investment should go to Onboarding → Success → Expansion.
+```
diff --git a/hands/trader/HAND.toml b/hands/trader/HAND.toml
index ba458a4..233263f 100644
--- a/hands/trader/HAND.toml
+++ b/hands/trader/HAND.toml
@@ -781,6 +781,337 @@ default_active = false
 # Warning shown when user tries to activate
 activation_warning = "Trading hand runs continuously and consumes tokens. Deactivate when not trading."
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "交易 Hand"
 description = "自主市场情报与交易引擎——多信号分析、对抗性多空推理、校准置信度评分、严格风控和投资组合分析"
+category = "数据"
+
+[i18n.zh.settings.trading_mode]
+label = "交易模式"
+description = "交易 Hand 的运行方式——仅分析、模拟交易或实盘交易"
+
+[i18n.zh.settings.market_focus]
+label = "市场关注"
+description = "监控和交易的目标市场"
+
+[i18n.zh.settings.strategy_style]
+label = "策略风格"
+description = "交易时间框架和策略类型"
+
+[i18n.zh.settings.risk_per_trade]
+label = "单笔风险"
+description = "单笔交易允许承受的最大仓位占比"
+
+[i18n.zh.settings.max_daily_loss]
+label = "单日最大亏损"
+description = "触发熔断机制的每日最大允许亏损比例"
+
+[i18n.zh.settings.analysis_depth]
+label = "分析深度"
+description = "每个标的收集和交叉验证的信号数量"
+
+[i18n.zh.settings.scan_schedule]
+label = "扫描频率"
+description = "扫描市场和更新分析的频率"
+
+[i18n.zh.settings.watchlist]
+label = "关注列表"
+description = "要监控的标的代码列表（逗号分隔，股票: AAPL，加密货币: BTC，ETF: SPY）"
+
+[i18n.zh.settings.initial_capital]
+label = "初始资金"
+description = "模拟交易或追踪的起始投资组合金额（美元）"
+
+[i18n.zh.settings.alpaca_api_key]
+label = "Alpaca API 密钥"
+description = "用于实盘/模拟交易的 Alpaca API 密钥（可在 alpaca.markets 免费获取）"
+
+[i18n.zh.settings.alpaca_secret_key]
+label = "Alpaca Secret 密钥"
+description = "Alpaca API 的 Secret 密钥"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "执行实盘交易前需要用户明确审批——强烈建议开启"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Trading"
+description = "Motor autónomo de inteligencia de mercado y trading — análisis multi-señal, razonamiento adversarial alcista/bajista, puntuación de confianza calibrada, gestión de riesgo estricta y analítica de cartera"
+category = "Datos"
+
+[i18n.es.settings.trading_mode]
+label = "Modo de trading"
+description = "Cómo opera el Hand de Trading — solo análisis, trading simulado o trading real"
+
+[i18n.es.settings.market_focus]
+label = "Enfoque de mercado"
+description = "Qué mercados monitorear y operar"
+
+[i18n.es.settings.strategy_style]
+label = "Estilo de estrategia"
+description = "Marco temporal y enfoque de la estrategia de trading"
+
+[i18n.es.settings.risk_per_trade]
+label = "Riesgo por operación"
+description = "Porcentaje máximo de la cartera en riesgo en una sola operación"
+
+[i18n.es.settings.max_daily_loss]
+label = "Pérdida diaria máxima"
+description = "Porcentaje máximo de pérdida diaria de la cartera antes de activar el disyuntor"
+
+[i18n.es.settings.analysis_depth]
+label = "Profundidad del análisis"
+description = "Cuántas señales recopilar y cruzar por activo"
+
+[i18n.es.settings.scan_schedule]
+label = "Frecuencia de escaneo"
+description = "Con qué frecuencia escanear los mercados y actualizar el análisis"
+
+[i18n.es.settings.watchlist]
+label = "Lista de seguimiento"
+description = "Lista de tickers a monitorear separados por comas (acciones: AAPL, cripto: BTC, ETFs: SPY)"
+
+[i18n.es.settings.initial_capital]
+label = "Capital inicial"
+description = "Valor inicial de la cartera para trading simulado o seguimiento (en USD)"
+
+[i18n.es.settings.alpaca_api_key]
+label = "Clave API de Alpaca"
+description = "Clave API de Alpaca para trading real/simulado (obtener gratis en alpaca.markets)"
+
+[i18n.es.settings.alpaca_secret_key]
+label = "Clave secreta de Alpaca"
+description = "Clave secreta de la API de Alpaca"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Requerir aprobación explícita del usuario antes de ejecutar cualquier operación real — altamente recomendado"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "トレーディング Hand"
+description = "自律型マーケットインテリジェンス＆トレーディングエンジン——マルチシグナル分析、対立的ブル/ベア推論、キャリブレーション済み信頼度スコアリング、厳格なリスク管理、ポートフォリオ分析"
+category = "データ"
+
+[i18n.ja.settings.trading_mode]
+label = "トレーディングモード"
+description = "トレーディングHandの動作方式——分析のみ、ペーパートレード、またはライブトレード"
+
+[i18n.ja.settings.market_focus]
+label = "マーケットフォーカス"
+description = "監視・取引する対象市場"
+
+[i18n.ja.settings.strategy_style]
+label = "戦略スタイル"
+description = "取引の時間軸と戦略アプローチ"
+
+[i18n.ja.settings.risk_per_trade]
+label = "1トレードあたりのリスク"
+description = "1回の取引でリスクにさらすポートフォリオの最大割合"
+
+[i18n.ja.settings.max_daily_loss]
+label = "1日の最大損失"
+description = "サーキットブレーカーが作動するまでの1日あたりの最大損失割合"
+
+[i18n.ja.settings.analysis_depth]
+label = "分析の深さ"
+description = "銘柄ごとに収集・クロスリファレンスするシグナルの数"
+
+[i18n.ja.settings.scan_schedule]
+label = "スキャンスケジュール"
+description = "市場スキャンと分析更新の頻度"
+
+[i18n.ja.settings.watchlist]
+label = "ウォッチリスト"
+description = "監視するティッカーのリスト（カンマ区切り、株式: AAPL、暗号通貨: BTC、ETF: SPY）"
+
+[i18n.ja.settings.initial_capital]
+label = "初期資金"
+description = "ペーパートレードまたはトラッキングの開始ポートフォリオ額（USD）"
+
+[i18n.ja.settings.alpaca_api_key]
+label = "Alpaca APIキー"
+description = "ライブ/ペーパートレード用のAlpaca APIキー（alpaca.marketsで無料取得可能）"
+
+[i18n.ja.settings.alpaca_secret_key]
+label = "Alpaca Secretキー"
+description = "Alpaca APIのSecretキー"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "ライブトレード実行前にユーザーの明示的な承認を必要とする——強く推奨"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand de Trading"
+description = "Moteur autonome d'intelligence de marché et de trading — analyse multi-signaux, raisonnement adversarial haussier/baissier, score de confiance calibré, gestion stricte des risques et analytique de portefeuille"
+category = "Données"
+
+[i18n.fr.settings.trading_mode]
+label = "Mode de trading"
+description = "Mode de fonctionnement du Hand de Trading — analyse seule, trading simulé ou trading réel"
+
+[i18n.fr.settings.market_focus]
+label = "Focus marché"
+description = "Quels marchés surveiller et sur lesquels opérer"
+
+[i18n.fr.settings.strategy_style]
+label = "Style de stratégie"
+description = "Horizon temporel et approche de la stratégie de trading"
+
+[i18n.fr.settings.risk_per_trade]
+label = "Risque par opération"
+description = "Pourcentage maximum du portefeuille en risque sur une seule opération"
+
+[i18n.fr.settings.max_daily_loss]
+label = "Perte quotidienne maximale"
+description = "Pourcentage maximum de perte quotidienne du portefeuille avant déclenchement du coupe-circuit"
+
+[i18n.fr.settings.analysis_depth]
+label = "Profondeur d'analyse"
+description = "Nombre de signaux à collecter et recouper par actif"
+
+[i18n.fr.settings.scan_schedule]
+label = "Fréquence de scan"
+description = "Fréquence de scan des marchés et de mise à jour de l'analyse"
+
+[i18n.fr.settings.watchlist]
+label = "Liste de surveillance"
+description = "Liste de tickers à surveiller séparés par des virgules (actions : AAPL, crypto : BTC, ETF : SPY)"
+
+[i18n.fr.settings.initial_capital]
+label = "Capital initial"
+description = "Valeur initiale du portefeuille pour le trading simulé ou le suivi (en USD)"
+
+[i18n.fr.settings.alpaca_api_key]
+label = "Clé API Alpaca"
+description = "Clé API Alpaca pour le trading réel/simulé (obtenir gratuitement sur alpaca.markets)"
+
+[i18n.fr.settings.alpaca_secret_key]
+label = "Clé secrète Alpaca"
+description = "Clé secrète de l'API Alpaca"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Exiger l'approbation explicite de l'utilisateur avant d'exécuter toute opération réelle — fortement recommandé"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Trading-Hand"
+description = "Autonomer Marktintelligenz- und Trading-Motor — Multi-Signal-Analyse, adversariales Bull/Bear-Reasoning, kalibriertes Konfidenz-Scoring, striktes Risikomanagement und Portfolio-Analytik"
+category = "Daten"
+
+[i18n.de.settings.trading_mode]
+label = "Trading-Modus"
+description = "Betriebsmodus des Trading-Hand — nur Analyse, simuliertes Trading oder Live-Trading"
+
+[i18n.de.settings.market_focus]
+label = "Marktfokus"
+description = "Welche Märkte überwacht und gehandelt werden"
+
+[i18n.de.settings.strategy_style]
+label = "Strategiestil"
+description = "Zeithorizont und Ansatz der Handelsstrategie"
+
+[i18n.de.settings.risk_per_trade]
+label = "Risiko pro Trade"
+description = "Maximaler Prozentsatz des Portfolios, der bei einem einzelnen Trade riskiert wird"
+
+[i18n.de.settings.max_daily_loss]
+label = "Maximaler Tagesverlust"
+description = "Maximaler täglicher Portfolioverlust in Prozent, bevor der Circuit Breaker auslöst"
+
+[i18n.de.settings.analysis_depth]
+label = "Analysetiefe"
+description = "Anzahl der pro Asset zu sammelnden und gegenzuprüfenden Signale"
+
+[i18n.de.settings.scan_schedule]
+label = "Scan-Zeitplan"
+description = "Wie oft Märkte gescannt und Analysen aktualisiert werden"
+
+[i18n.de.settings.watchlist]
+label = "Watchlist"
+description = "Kommagetrennte Liste der zu überwachenden Ticker (Aktien: AAPL, Krypto: BTC, ETFs: SPY)"
+
+[i18n.de.settings.initial_capital]
+label = "Anfangskapital"
+description = "Anfänglicher Portfoliowert für simuliertes Trading oder Tracking (in USD)"
+
+[i18n.de.settings.alpaca_api_key]
+label = "Alpaca API-Schlüssel"
+description = "Alpaca API-Schlüssel für Live-/Papierhandel (kostenlos auf alpaca.markets erhältlich)"
+
+[i18n.de.settings.alpaca_secret_key]
+label = "Alpaca Secret-Schlüssel"
+description = "Secret-Schlüssel der Alpaca API"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Ausdrückliche Benutzergenehmigung vor der Ausführung von Live-Trades erforderlich — dringend empfohlen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "트레이딩 Hand"
+description = "자율 시장 인텔리전스 및 트레이딩 엔진 — 다중 신호 분석, 대립적 매수/매도 추론, 보정된 신뢰도 평가, 엄격한 리스크 관리 및 포트폴리오 분석"
+category = "데이터"
+
+[i18n.ko.settings.trading_mode]
+label = "트레이딩 모드"
+description = "트레이딩 Hand의 운영 방식 — 분석 전용, 모의 거래 또는 실거래"
+
+[i18n.ko.settings.market_focus]
+label = "시장 관심"
+description = "모니터링 및 거래할 대상 시장"
+
+[i18n.ko.settings.strategy_style]
+label = "전략 스타일"
+description = "거래 시간 프레임 및 전략 유형"
+
+[i18n.ko.settings.risk_per_trade]
+label = "거래당 리스크"
+description = "단일 거래에서 허용되는 최대 포트폴리오 비율"
+
+[i18n.ko.settings.max_daily_loss]
+label = "일일 최대 손실"
+description = "서킷 브레이커 발동 전 허용되는 일일 최대 손실 비율"
+
+[i18n.ko.settings.analysis_depth]
+label = "분석 깊이"
+description = "자산별 수집 및 교차 검증할 신호 수"
+
+[i18n.ko.settings.scan_schedule]
+label = "스캔 일정"
+description = "시장 스캔 및 분석 업데이트 주기"
+
+[i18n.ko.settings.watchlist]
+label = "관심 목록"
+description = "모니터링할 종목 코드 목록 (쉼표로 구분, 주식: AAPL, 암호화폐: BTC, ETF: SPY)"
+
+[i18n.ko.settings.initial_capital]
+label = "초기 자본"
+description = "모의 거래 또는 추적을 위한 시작 포트폴리오 금액 (USD)"
+
+[i18n.ko.settings.alpaca_api_key]
+label = "Alpaca API 키"
+description = "실거래/모의 거래용 Alpaca API 키 (alpaca.markets에서 무료 발급)"
+
+[i18n.ko.settings.alpaca_secret_key]
+label = "Alpaca 시크릿 키"
+description = "Alpaca API 시크릿 키"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "실거래 실행 전 사용자의 명시적 승인 필요 — 강력히 권장"
diff --git a/hands/twitter/HAND.toml b/hands/twitter/HAND.toml
index fc6f4dc..e0f11c0 100644
--- a/hands/twitter/HAND.toml
+++ b/hands/twitter/HAND.toml
@@ -526,6 +526,313 @@ frequency = "hourly"
 token_consumption = "medium"
 default_active = false
 
+# ─── Internationalization (optional) ─────────────────────────────────────────
+# All i18n sections are optional. Without them, the English values above are used.
+# To localize, add [i18n.LANG] sections (e.g. zh, ja, ko, es, fr, de).
+# Settings translations are also optional — omit to keep English labels.
+
+# ─── Chinese (简体中文) ────────────────────────────────────────────────────
+
 [i18n.zh]
 name = "Twitter Hand"
 description = "自主 Twitter/X 管理——内容创作、定时发布、互动管理和效果追踪"
+category = "通信"
+
+[i18n.zh.settings.twitter_bearer_token]
+label = "Twitter Bearer Token"
+description = "Twitter/X 开发者平台的 Bearer Token，所有 Twitter API 操作均需此凭证。"
+
+[i18n.zh.settings.twitter_style]
+label = "内容风格"
+description = "推文的语气和风格"
+
+[i18n.zh.settings.post_frequency]
+label = "发布频率"
+description = "创建和发布内容的频率"
+
+[i18n.zh.settings.auto_reply]
+label = "自动回复"
+description = "自动回复提及和相关对话"
+
+[i18n.zh.settings.auto_like]
+label = "自动点赞"
+description = "自动为人脉网络中的推文和相关内容点赞"
+
+[i18n.zh.settings.content_topics]
+label = "内容主题"
+description = "要创作内容的主题（逗号分隔，例如 AI、创业、效率提升）"
+
+[i18n.zh.settings.brand_voice]
+label = "品牌调性"
+description = "描述你的独特风格（例如"用幽默简化复杂技术的创业者"）"
+
+[i18n.zh.settings.thread_mode]
+label = "推文串模式"
+description = "在内容组合中加入推文串（多条推文组成的故事）"
+
+[i18n.zh.settings.content_queue_size]
+label = "内容队列大小"
+description = "待发队列中保持的推文数量"
+
+[i18n.zh.settings.engagement_hours]
+label = "互动时段"
+description = "检查提及和互动的时间段"
+
+[i18n.zh.settings.approval_mode]
+label = "审批模式"
+description = "将推文写入队列等待审核，而非直接发布"
+
+# ─── Spanish (Español) ────────────────────────────────────────────────────
+
+[i18n.es]
+name = "Hand de Twitter"
+description = "Gestor autónomo de Twitter/X — creación de contenido, publicación programada, interacciones y seguimiento de rendimiento"
+category = "Comunicación"
+
+[i18n.es.settings.twitter_bearer_token]
+label = "Token Bearer de Twitter"
+description = "Token Bearer del Portal de Desarrolladores de Twitter/X. Requerido para todas las operaciones de la API de Twitter."
+
+[i18n.es.settings.twitter_style]
+label = "Estilo de contenido"
+description = "Voz y tono para tus tweets"
+
+[i18n.es.settings.post_frequency]
+label = "Frecuencia de publicación"
+description = "Con qué frecuencia crear y publicar contenido"
+
+[i18n.es.settings.auto_reply]
+label = "Respuesta automática"
+description = "Responder automáticamente a menciones y conversaciones relevantes"
+
+[i18n.es.settings.auto_like]
+label = "Me gusta automático"
+description = "Dar me gusta automáticamente a tweets de tu red y contenido relevante"
+
+[i18n.es.settings.content_topics]
+label = "Temas de contenido"
+description = "Temas sobre los que crear contenido (separados por comas, ej. IA, startups, productividad)"
+
+[i18n.es.settings.brand_voice]
+label = "Voz de marca"
+description = "Describe tu voz única (ej. 'fundador sarcástico que simplifica la tecnología compleja')"
+
+[i18n.es.settings.thread_mode]
+label = "Modo de hilos"
+description = "Incluir hilos de tweets (historias de múltiples tweets) en la mezcla de contenido"
+
+[i18n.es.settings.content_queue_size]
+label = "Tamaño de la cola de contenido"
+description = "Número de tweets a mantener en la cola preparada"
+
+[i18n.es.settings.engagement_hours]
+label = "Horario de interacción"
+description = "Cuándo verificar menciones e interactuar"
+
+[i18n.es.settings.approval_mode]
+label = "Modo de aprobación"
+description = "Escribir los tweets en un archivo de cola para revisión en lugar de publicarlos directamente"
+
+# ─── Japanese (日本語) ────────────────────────────────────────────────────
+
+[i18n.ja]
+name = "Twitter Hand"
+description = "自律型Twitter/Xマネージャー——コンテンツ作成、スケジュール投稿、エンゲージメント管理、パフォーマンス追跡"
+category = "コミュニケーション"
+
+[i18n.ja.settings.twitter_bearer_token]
+label = "Twitter Bearerトークン"
+description = "Twitter/X開発者ポータルのBearerトークン。すべてのTwitter API操作に必要です。"
+
+[i18n.ja.settings.twitter_style]
+label = "コンテンツスタイル"
+description = "ツイートの語調とスタイル"
+
+[i18n.ja.settings.post_frequency]
+label = "投稿頻度"
+description = "コンテンツの作成・投稿の頻度"
+
+[i18n.ja.settings.auto_reply]
+label = "自動返信"
+description = "メンションや関連する会話に自動で返信する"
+
+[i18n.ja.settings.auto_like]
+label = "自動いいね"
+description = "ネットワーク内のツイートや関連コンテンツに自動でいいねする"
+
+[i18n.ja.settings.content_topics]
+label = "コンテンツトピック"
+description = "作成するコンテンツのトピック（カンマ区切り、例: AI、スタートアップ、生産性）"
+
+[i18n.ja.settings.brand_voice]
+label = "ブランドボイス"
+description = "あなた独自の語り口を記述（例:「複雑なテクノロジーをわかりやすく伝える皮肉屋の起業家」）"
+
+[i18n.ja.settings.thread_mode]
+label = "スレッドモード"
+description = "コンテンツミックスにツイートスレッド（複数ツイートで構成するストーリー）を含める"
+
+[i18n.ja.settings.content_queue_size]
+label = "コンテンツキューサイズ"
+description = "準備キューに保持するツイートの数"
+
+[i18n.ja.settings.engagement_hours]
+label = "エンゲージメント時間帯"
+description = "メンションの確認とエンゲージメントを行う時間帯"
+
+[i18n.ja.settings.approval_mode]
+label = "承認モード"
+description = "ツイートを直接投稿せず、レビュー用のキューファイルに書き出す"
+
+# ─── French (Français) ────────────────────────────────────────────────────
+
+[i18n.fr]
+name = "Hand Twitter"
+description = "Gestionnaire Twitter/X autonome — création de contenu, publication programmée, interactions et suivi des performances"
+category = "Communication"
+
+[i18n.fr.settings.twitter_bearer_token]
+label = "Jeton Bearer Twitter"
+description = "Jeton Bearer du Portail Développeurs Twitter/X. Requis pour toutes les opérations de l'API Twitter."
+
+[i18n.fr.settings.twitter_style]
+label = "Style de contenu"
+description = "Ton et style pour vos tweets"
+
+[i18n.fr.settings.post_frequency]
+label = "Fréquence de publication"
+description = "Fréquence de création et de publication de contenu"
+
+[i18n.fr.settings.auto_reply]
+label = "Réponse automatique"
+description = "Répondre automatiquement aux mentions et conversations pertinentes"
+
+[i18n.fr.settings.auto_like]
+label = "Like automatique"
+description = "Aimer automatiquement les tweets de votre réseau et le contenu pertinent"
+
+[i18n.fr.settings.content_topics]
+label = "Sujets de contenu"
+description = "Sujets sur lesquels créer du contenu (séparés par des virgules, ex. IA, startups, productivité)"
+
+[i18n.fr.settings.brand_voice]
+label = "Voix de marque"
+description = "Décrivez votre voix unique (ex. 'fondateur sarcastique qui simplifie la technologie complexe')"
+
+[i18n.fr.settings.thread_mode]
+label = "Mode fil de discussion"
+description = "Inclure des fils de tweets (histoires à plusieurs tweets) dans le mix de contenu"
+
+[i18n.fr.settings.content_queue_size]
+label = "Taille de la file de contenu"
+description = "Nombre de tweets à maintenir dans la file d'attente préparée"
+
+[i18n.fr.settings.engagement_hours]
+label = "Heures d'engagement"
+description = "Quand vérifier les mentions et interagir"
+
+[i18n.fr.settings.approval_mode]
+label = "Mode d'approbation"
+description = "Écrire les tweets dans un fichier d'attente pour révision au lieu de les publier directement"
+
+# ─── German (Deutsch) ────────────────────────────────────────────────────
+
+[i18n.de]
+name = "Twitter-Hand"
+description = "Autonomer Twitter/X-Manager — Content-Erstellung, geplante Veröffentlichung, Engagement-Management und Performance-Tracking"
+category = "Kommunikation"
+
+[i18n.de.settings.twitter_bearer_token]
+label = "Twitter Bearer-Token"
+description = "Bearer-Token vom Twitter/X-Entwicklerportal. Erforderlich für alle Twitter-API-Operationen."
+
+[i18n.de.settings.twitter_style]
+label = "Inhaltsstil"
+description = "Ton und Stil für Ihre Tweets"
+
+[i18n.de.settings.post_frequency]
+label = "Veröffentlichungshäufigkeit"
+description = "Wie oft Inhalte erstellt und veröffentlicht werden"
+
+[i18n.de.settings.auto_reply]
+label = "Automatische Antwort"
+description = "Automatisch auf Erwähnungen und relevante Gespräche antworten"
+
+[i18n.de.settings.auto_like]
+label = "Automatisches Like"
+description = "Tweets im Netzwerk und relevante Inhalte automatisch liken"
+
+[i18n.de.settings.content_topics]
+label = "Inhaltsthemen"
+description = "Themen für die Content-Erstellung (kommagetrennt, z.B. KI, Startups, Produktivität)"
+
+[i18n.de.settings.brand_voice]
+label = "Markenstimme"
+description = "Beschreiben Sie Ihre einzigartige Stimme (z.B. 'sarkastischer Gründer, der komplexe Technologie vereinfacht')"
+
+[i18n.de.settings.thread_mode]
+label = "Thread-Modus"
+description = "Tweet-Threads (mehrteilige Tweet-Geschichten) in den Content-Mix aufnehmen"
+
+[i18n.de.settings.content_queue_size]
+label = "Größe der Content-Warteschlange"
+description = "Anzahl der Tweets in der vorbereiteten Warteschlange"
+
+[i18n.de.settings.engagement_hours]
+label = "Engagement-Zeiten"
+description = "Wann Erwähnungen geprüft und interagiert werden soll"
+
+[i18n.de.settings.approval_mode]
+label = "Genehmigungsmodus"
+description = "Tweets in eine Warteschlangendatei zur Überprüfung schreiben, anstatt sie direkt zu veröffentlichen"
+
+# ─── Korean (한국어) ────────────────────────────────────────────────────
+
+[i18n.ko]
+name = "Twitter Hand"
+description = "자율 Twitter/X 관리 — 콘텐츠 제작, 예약 게시, 소통 관리 및 성과 추적"
+category = "커뮤니케이션"
+
+[i18n.ko.settings.twitter_bearer_token]
+label = "Twitter Bearer 토큰"
+description = "Twitter/X 개발자 포털의 Bearer 토큰. 모든 Twitter API 작업에 필수."
+
+[i18n.ko.settings.twitter_style]
+label = "콘텐츠 스타일"
+description = "트윗의 어조와 스타일"
+
+[i18n.ko.settings.post_frequency]
+label = "게시 빈도"
+description = "콘텐츠를 작성하고 게시하는 주기"
+
+[i18n.ko.settings.auto_reply]
+label = "자동 답글"
+description = "멘션 및 관련 대화에 자동으로 답글 작성"
+
+[i18n.ko.settings.auto_like]
+label = "자동 좋아요"
+description = "네트워크 내 트윗 및 관련 콘텐츠에 자동으로 좋아요"
+
+[i18n.ko.settings.content_topics]
+label = "콘텐츠 주제"
+description = "콘텐츠를 작성할 주제 (쉼표로 구분, 예: AI, 스타트업, 생산성)"
+
+[i18n.ko.settings.brand_voice]
+label = "브랜드 보이스"
+description = "고유한 스타일을 설명 (예: '복잡한 기술을 쉽게 풀어내는 유머러스한 창업자')"
+
+[i18n.ko.settings.thread_mode]
+label = "스레드 모드"
+description = "콘텐츠 구성에 트윗 스레드 (다중 트윗 스토리) 포함"
+
+[i18n.ko.settings.content_queue_size]
+label = "콘텐츠 대기열 크기"
+description = "준비 대기열에 유지할 트윗 수"
+
+[i18n.ko.settings.engagement_hours]
+label = "소통 시간대"
+description = "멘션 확인 및 소통 활동을 수행하는 시간대"
+
+[i18n.ko.settings.approval_mode]
+label = "승인 모드"
+description = "트윗을 직접 게시하지 않고 대기열 파일에 기록하여 검토"
diff --git a/hands/twitter/SKILL.md b/hands/twitter/SKILL.md
index b949f79..9278bae 100644
--- a/hands/twitter/SKILL.md
+++ b/hands/twitter/SKILL.md
@@ -226,6 +226,72 @@ Do NOT auto-like:
 
 ---
 
+## Advanced Engagement Patterns
+
+### Quote Tweet vs Reply vs Retweet
+
+Choosing the right interaction type determines whether you gain visibility or waste it.
+
+**Use a Quote Tweet when**:
+- You have a distinct take or added context (not just "this!")
+- The original tweet has high impressions and you want to draft off its reach
+- You are crediting someone while adding your own insight for your audience
+- The original author has a similar or larger following (exposes you to their audience)
+
+**Use a Reply when**:
+- You want to build a direct relationship with the author
+- Your comment only makes sense in context of the original
+- The original author has a much larger following (replies show on their thread, giving you visibility without looking self-promotional)
+- You are answering a question or adding a correction
+
+**Use a plain Retweet when**:
+- The original says everything perfectly and you have nothing to add
+- You want to signal-boost a community member, customer, or partner
+- The content is time-sensitive (breaking news, event announcements)
+
+**Avoid**:
+- Quote tweeting with only emojis or "this" -- adds no value, looks lazy
+- Quote tweeting someone with fewer followers just to dunk -- punching down
+- Retweeting more than 3-4 times per day -- dilutes your original content ratio
+
+### Thread Repurposing
+
+A thread that performed well contains 5-7 standalone content pieces. Extract them over the following week to maximize ROI.
+
+**Process**:
+1. Day 0 (original): Post the full thread
+2. Day 2: Pull the single most quotable tweet from the thread. Post it standalone with slightly different wording. No link back to the thread
+3. Day 4: Turn a data point or example from the thread into a graphic or screenshot tweet
+4. Day 6: Post the thread's core thesis as a hot take (one tweet, punchy)
+5. Day 8+: If engagement stayed strong, post a "Part 2" thread that goes deeper on whichever tweet in the original got the most replies
+
+**Rules**:
+- Change the wording each time -- copy-pasting feels like spam to followers who saw the original
+- Space extractions at least 48 hours apart
+- Stop if any extraction underperforms significantly -- the topic is tapped out
+- Never repurpose a thread that got low engagement; the content did not resonate
+
+### Trending Topic Participation
+
+**When to participate**:
+- The trend directly intersects one of your content pillars
+- You have a genuine, informed perspective (not a generic reaction)
+- The trend is still rising (check the "Trending" tab; if it has been trending for >12 hours, you are late)
+- The tone of the trend matches your brand voice
+
+**When to avoid**:
+- Tragedy, disaster, or crisis events -- opportunistic posting destroys trust
+- Highly polarized political or social debates outside your expertise
+- Trends driven by outrage mobs -- associating your brand is high-risk, low-reward
+- You would need to force-fit your product or message into the trend
+
+**Execution**:
+- Lead with your actual insight, not the hashtag. The hashtag goes at the end or is omitted entirely if the topic keyword is in your text
+- Be early or be different. If 50 people have already made the same joke, skip it
+- Tie back to your pillar: "Trend X is exactly why [your pillar topic] matters more than ever"
+
+---
+
 ## Content Calendar Template
 
 ```
@@ -254,6 +320,169 @@ Friday:
 
 ---
 
+## Worked Examples
+
+### Example 1: Product Launch Twitter Campaign (1-Week Plan)
+
+**Context**: A dev tools startup is launching "FastDB," an open-source embedded database. The account has 2,400 followers, mostly backend engineers.
+
+**Pre-launch (3 days before)**:
+- Seed curiosity without revealing the product name
+- Engage heavily in database-related threads to increase profile visits before launch
+
+**Day 1 (Monday) -- Teaser**:
+```
+We've been heads-down for 8 months building something
+we think embedded databases have been missing.
+
+Shipping it open-source this Thursday.
+
+More soon.
+```
+Purpose: Create anticipation. No hashtags, no links. Let curiosity drive profile visits.
+
+**Day 2 (Tuesday) -- Problem framing**:
+```
+SQLite is incredible for what it does.
+
+But if you need concurrent writes, ACID transactions,
+AND sub-millisecond reads in the same embedded DB...
+your options get thin fast.
+
+We've been living in that gap. Fix incoming Thursday.
+```
+Purpose: Define the problem space. People who feel this pain will follow for the reveal.
+
+**Day 3 (Wednesday) -- Social proof / build-up**:
+```
+Shipped our embedded DB to 12 beta testers last month.
+
+Results so far:
+- 4.2x faster concurrent writes vs SQLite WAL mode
+- Zero-config replication
+- Single static binary, 3.8 MB
+
+One more day.
+```
+Purpose: Concrete numbers build credibility. "One more day" maintains tension.
+
+**Day 4 (Thursday) -- Launch day thread** (6-tweet thread):
+```
+1/6 [HOOK]: Introducing FastDB -- embedded DB for concurrent-write-heavy
+     workloads. Open source. Single binary. Here's why we built it:
+2/6 [PROBLEM]: SQLite = single-writer. Fine for reads, hits a wall on
+     write-heavy apps (event logging, IoT, realtime sync). FastDB uses
+     MVCC -- writers never block readers, readers never block writers.
+3/6 [PROOF]: Benchmarks (M2 Mac, 8 threads): concurrent writes 51K ops/s
+     vs SQLite WAL 12K ops/s. Point reads on par at ~900K ops/s.
+4/6 [ONBOARD]: Getting started: `cargo add fastdb` then 3 lines of code.
+     Full SQLite-compatible query layer coming in v0.2.
+5/6 [ROADMAP]: v0.1 ships ACID transactions, built-in replication, crash
+     recovery, zero deps beyond libc. v0.2: SQL layer, S3 cold storage.
+6/6 [CTA]: Star the repo: github.com/example/fastdb -- open issues, roast
+     the benchmarks, tell us what's missing.
+```
+Key structural choices: tweet 1 is a standalone hook, tweet 3 has hard numbers, tweet 6 ends with a specific ask (not just "check it out").
+
+**Day 4 afternoon** -- Post a standalone tweet answering the most common reply question publicly (drives docs traffic). **Day 5 (Friday)** -- Reply to every substantive comment. Templates for common reactions:
+- "How is this different from X?" -> Concrete comparison, link to docs
+- "Benchmarks look suspicious" -> Link the reproduction steps, invite them to run it
+- "Will you support [feature]?" -> Link the tracking issue
+
+**Day 6-7 (Weekend)** -- Repurpose: extract the benchmark tweet as a standalone with a chart image; post a "5 things I learned launching an open-source DB" reflection thread.
+
+### Example 2: Building Thought Leadership from Scratch (Month 1)
+
+**Context**: An individual ML engineer with 180 followers wants to become a recognized voice in applied machine learning. No existing audience. No viral content history.
+
+**Core principle for month 1**: Do not broadcast. Contribute. Your first 500 followers come from being consistently useful in other people's threads, not from your own tweets.
+
+**Week 1 -- Comment-first growth**:
+- Post 0 original tweets
+- Find 10 accounts in your niche with 5K-50K followers who post regularly
+- Reply to 5-8 of their tweets per day with substantive comments (not "great post!")
+- Goal: Get 3-5 of those authors to like or reply to your comments by end of week
+
+**What a good reply looks like**:
+```
+Original tweet: "Fine-tuning LLMs is overrated. Most use cases
+are better served by good prompting + RAG."
+
+Bad reply: "Agreed!"
+
+Good reply: "Mostly agree, but there's a middle ground --
+LoRA fine-tuning on 500 domain-specific examples
+consistently beats RAG for structured extraction tasks.
+
+We saw 23% higher F1 on invoice parsing after a 2-hour
+fine-tune vs our best RAG setup.
+
+RAG still wins for open-domain QA though."
+```
+This reply adds data, shows experience, and invites further discussion. People reading the thread see your expertise and check your profile.
+
+**Week 2 -- First original content**:
+- Continue the reply strategy (5/day minimum)
+- Post 2-3 original tweets. Keep them observational, not promotional:
+```
+Something I've noticed after fine-tuning 30+ models
+this year:
+
+The quality of your eval set matters 10x more than
+the size of your training set.
+
+50 carefully labeled examples with clear edge cases
+beats 5000 noisy scraped examples every time.
+```
+- Post 1 "ask the audience" tweet to start conversations:
+```
+ML engineers: what's the most counterintuitive lesson
+you've learned about deploying models to production?
+
+I'll start: the model is almost never the bottleneck.
+Data pipelines are.
+```
+
+**Week 3 -- First thread** (5-tweet authority thread):
+```
+1/5 [HOOK]: I've deployed 12 ML models to production this year. The ones
+     that worked all had one thing in common. It wasn't the architecture.
+2/5 [THESIS]: Every success had a tight feedback loop -- predictions
+     validated by a human within 24 hours, not "we'll evaluate next quarter."
+3/5 [EVIDENCE]: Model A (invoice classifier): accountants flagged errors
+     same-day, retrained weekly, 84% -> 97% in 6 weeks. Model B (churn
+     predictor): sales ignored outputs, no feedback 3 months, drifted to
+     coin-flip accuracy.
+4/5 [FRAMEWORK]: The pattern: (1) deploy with human-in-the-loop review,
+     (2) log every correction, (3) retrain on corrections every 1-2 weeks,
+     (4) remove human review once accuracy stabilizes.
+5/5 [CTA]: If you're skipping the feedback loop, you're building on sand.
+     What's your experience?
+```
+Notice the structure: personal credibility in tweet 1, a clear thesis in tweet 2, contrasting real examples in tweet 3, an actionable takeaway in tweet 4, and a discussion prompt in tweet 5.
+
+**Week 4 -- Establish rhythm**:
+- Settle into a sustainable cadence: 1 thread/week, 1-2 standalone tweets/day, 5+ replies/day
+- Review metrics from week 2-3 content to identify which topics resonated
+- Double down on the topic that got the most replies (not likes -- replies indicate deeper engagement)
+
+**Month 1 milestones**:
+| Metric | Target | Why it matters |
+|--------|--------|----------------|
+| Followers | 350-500 | 2-3x growth signals the approach is working |
+| Avg impressions per tweet | 800-2000 | Shows the algorithm is distributing your content |
+| Replies received per original tweet | 3-5 | People are engaging, not just scrolling past |
+| Mutual follows from target accounts | 5-10 | Your niche peers are noticing you |
+| Profile visits / week | 200+ | Your replies are driving curiosity |
+
+**What to avoid in month 1**:
+- Posting 10 tweets/day hoping something sticks -- looks desperate, exhausts your ideas
+- Buying followers or using engagement pods -- Twitter's algorithm detects and penalizes this
+- Talking about yourself or your product -- earn attention through insight first
+- Getting discouraged by low numbers -- 180 to 400 followers in a month is strong growth
+
+---
+
 ## Performance Metrics
 
 ### Key Metrics