feat(hands): complete i18n fixes, SKILL.md enhancements, and README overhaul
- Fix French accent characters (é/è/ê/ç/â/ô) across all 14 HAND.toml files - Fix German special characters (ä/ö/ü/ß) across all 14 HAND.toml files - Add category translations to all 6 i18n language blocks in all 14 hands - Enhance SKILL.md content for 9 hands with practical examples and workflows - Trim bloated SKILL.md files (apitester 1400→892, devops 1301→870) - Rewrite root README.md with accurate stats, complete hand/integration tables - Update hands/README.md with full 14-hand listing and i18n documentation
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@@ -269,3 +269,744 @@ Before including data in the knowledge graph, evaluate:
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6. **Track record**: Has this source been reliable in the past?
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If a claim fails 3+ checks, downgrade its confidence to "low".
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---
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## Worked Examples
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### Example 1: Competitor Monitoring Campaign
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**Scenario**: A B2B SaaS company wants continuous intelligence on three direct competitors: AlphaCloud, BetaStack, and GammaSuite.
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**Step 1 — Define targets and collection requirements**
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Configure the hand with:
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```
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target_subject: "AlphaCloud, BetaStack, GammaSuite"
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focus_area: competitor
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collection_depth: deep
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update_frequency: daily
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alert_on_changes: true
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track_sentiment: true
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max_sources_per_cycle: 50
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```
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Build the initial query set:
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```
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"AlphaCloud" pricing OR plans OR tiers
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"AlphaCloud" product launch OR release OR update
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"AlphaCloud" review site:g2.com OR site:capterra.com
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"AlphaCloud" customer case study
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"AlphaCloud" hiring site:linkedin.com OR site:greenhouse.io
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"switch from AlphaCloud to"
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(repeat for BetaStack and GammaSuite)
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```
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**Step 2 — Run first collection cycle**
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Execute queries, fetch top results, extract entities:
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```json
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[
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{"type": "product", "name": "AlphaCloud v4.2", "company": "AlphaCloud", "launch_date": "2025-11-15", "source": "alphacloud.com/blog"},
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{"type": "person", "name": "Sarah Chen", "role": "New VP Engineering", "company": "BetaStack", "source": "linkedin.com/in/sarachen"},
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{"type": "event", "name": "GammaSuite Series C", "amount": "$85M", "date": "2025-11-10", "source": "techcrunch.com/2025/11/10/gammasuite-series-c"}
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]
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```
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**Step 3 — Build knowledge graph entries**
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```
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knowledge_add_entity type=company name="AlphaCloud" industry="SaaS" funding_stage="Series B"
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knowledge_add_entity type=product name="AlphaCloud v4.2" category="cloud platform"
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knowledge_add_entity type=person name="Sarah Chen" role="VP Engineering" company="BetaStack"
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knowledge_add_relation source="AlphaCloud" relation="launched" target="AlphaCloud v4.2"
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knowledge_add_relation source="Sarah Chen" relation="works_at" target="BetaStack"
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```
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**Step 4 — Process findings into change detection**
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| Change | Type | Significance | Action |
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|--------|------|-------------|--------|
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| AlphaCloud released v4.2 with AI features | Product launch | IMPORTANT | Include in report, compare against own roadmap |
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| BetaStack hired VP Engineering from FAANG | Leadership change | IMPORTANT | Track subsequent hiring patterns |
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| GammaSuite raised $85M Series C | Major funding | CRITICAL | Immediate alert, expect aggressive expansion |
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**Step 5 — Generate intelligence brief**
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```markdown
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# Competitor Intelligence Brief
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**Date**: 2025-11-16 | **Cycle**: 1 | **Sources**: 47
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## Priority Changes
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1. [CRITICAL] GammaSuite closed $85M Series C led by Sequoia (TechCrunch, confirmed via Crunchbase)
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2. [IMPORTANT] AlphaCloud shipped v4.2 with AI-assisted workflow builder
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3. [IMPORTANT] BetaStack hired Sarah Chen (ex-Google) as VP Engineering
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## Executive Summary
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GammaSuite's large funding round signals intent to accelerate growth — expect increased
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marketing spend and possible M&A activity in the next 6 months. AlphaCloud's v4.2
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introduces direct feature overlap with our AI pipeline. BetaStack's engineering
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leadership hire suggests a product quality push.
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## Recommended Actions
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- Review AlphaCloud v4.2 feature parity against our roadmap
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- Monitor GammaSuite job postings for expansion signals
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- Track BetaStack engineering team growth over next 3 cycles
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```
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---
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### Example 2: Technology Landscape Mapping
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**Scenario**: Map the emerging real-time AI inference landscape — track frameworks, adoption signals, key players, and performance benchmarks.
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**Step 1 — Define scope and seed entities**
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```
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target_subject: "real-time AI inference (vLLM, TensorRT-LLM, Triton, Ollama, llama.cpp)"
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focus_area: technology
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collection_depth: exhaustive
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update_frequency: weekly
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```
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Initial seed queries:
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```
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"real-time AI inference" benchmark 2025
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"vLLM" vs "TensorRT-LLM" performance
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"llama.cpp" release changelog
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"AI inference" startup funding 2025
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"edge AI inference" adoption enterprise
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"AI inference" tokens per second benchmark
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site:github.com "vLLM" stars OR contributors
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site:arxiv.org "inference optimization" 2025
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```
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**Step 2 — Build entity graph from first sweep**
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Entities collected:
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```json
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[
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{"type": "technology", "name": "vLLM", "version": "0.6.3", "vendor": "UC Berkeley / community", "category": "inference engine"},
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{"type": "technology", "name": "TensorRT-LLM", "version": "0.15", "vendor": "NVIDIA", "category": "inference engine"},
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{"type": "company", "name": "Groq", "industry": "AI hardware", "product": "LPU Inference Engine"},
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{"type": "number", "metric": "tokens_per_second", "value": 523, "context": "Groq Llama 3 70B", "date": "2025-10"},
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{"type": "number", "metric": "github_stars", "value": 32400, "context": "vLLM", "date": "2025-11"}
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]
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```
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Relationships:
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```
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vLLM --competes_with--> TensorRT-LLM
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vLLM --competes_with--> Ollama
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Groq --launched--> "LPU Inference Engine"
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NVIDIA --launched--> TensorRT-LLM
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llama.cpp --uses--> GGUF format
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```
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**Step 3 — Track adoption signals across cycles**
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| Signal Type | What to Watch | Detection Method |
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|-------------|--------------|-----------------|
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| GitHub velocity | Stars, forks, contributor count week-over-week | Snapshot comparison |
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| Enterprise adoption | Case studies, "we migrated to X" blog posts | Keyword search |
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| Benchmark results | Tokens/sec, latency, cost-per-token comparisons | Structured extraction |
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| Job postings | "Experience with vLLM" in job descriptions | Job board queries |
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| Conference talks | Accepted papers, keynote mentions | Conference program search |
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**Step 4 — Detect trends over 4 weekly cycles**
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```
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Cycle 1: vLLM 31,800 stars | TensorRT-LLM 9,200 stars | Ollama 98,000 stars
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Cycle 2: vLLM 32,400 stars | TensorRT-LLM 9,500 stars | Ollama 101,000 stars
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Cycle 3: vLLM 33,500 stars | TensorRT-LLM 9,600 stars | Ollama 103,500 stars
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Cycle 4: vLLM 35,200 stars | TensorRT-LLM 9,700 stars | Ollama 105,000 stars
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Trend: vLLM accelerating (+1,700/wk avg → +1,700 last week)
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Ollama decelerating (+3,000/wk → +1,500/wk)
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TensorRT-LLM flat (~200/wk)
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```
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**Step 5 — Produce technology landscape report**
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Include a positioning summary:
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| Framework | Strengths | Weaknesses | Momentum | Best For |
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|-----------|-----------|------------|----------|----------|
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| vLLM | High throughput, PagedAttention | GPU-only, complex setup | Accelerating | Production serving at scale |
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| TensorRT-LLM | NVIDIA optimization, low latency | Vendor lock-in, NVIDIA GPUs only | Flat | NVIDIA-stack deployments |
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| Ollama | Simple UX, local-first | Lower throughput, less tunable | Decelerating | Developer experimentation |
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| llama.cpp | CPU support, portable | Manual optimization needed | Steady | Edge/embedded inference |
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| Groq LPU | Extreme speed, low latency | Limited model support, cloud-only | Growing | Latency-critical applications |
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---
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### Example 3: M&A Signal Detection
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**Scenario**: Detect early acquisition indicators for companies in the enterprise observability space (Datadog, Grafana Labs, Chronosphere, Honeycomb).
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**Step 1 — Define M&A signal categories**
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| Signal Category | Indicators | Weight |
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|----------------|-----------|--------|
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| Executive changes | CEO/CFO departure, new "Chief Strategy Officer", board additions | High |
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| Hiring patterns | Sudden corporate development/M&A roles, legal team expansion | High |
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| Financial signals | Unusual funding, secondary sales, down round, runway concerns | High |
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| Strategic moves | Exclusive partnerships, technology licensing, IP transfers | Medium |
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| Market behavior | Quiet period (no product updates), website changes, domain changes | Medium |
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| Social signals | Founder tone shifts, "exciting news soon" posts, unusual silence | Low |
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**Step 2 — Build targeted queries**
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```
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"Chronosphere" AND ("acquisition" OR "acquire" OR "acqui-hire" OR "merger")
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"Honeycomb" AND ("strategic alternatives" OR "exploring options" OR "advisors")
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"Grafana Labs" AND ("corporate development" OR "M&A" OR "strategic partnership")
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site:linkedin.com "Chronosphere" "corporate development" OR "M&A"
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site:sec.gov "Honeycomb" OR "Hound Technology"
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"[company]" "quiet period" OR "exciting announcement"
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"[company]" hiring "corporate development" OR "business development director"
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"[company]" board of directors new appointment
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```
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**Step 3 — Entity and event extraction**
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From collected sources, extract and classify:
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```json
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[
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{
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"type": "event",
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"name": "Chronosphere CFO departure",
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"date": "2025-10-28",
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"entities": ["Chronosphere", "Lisa Park"],
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"signal_category": "executive_change",
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"m_and_a_weight": "high",
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"source": "linkedin.com/posts/lisapark-farewell"
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},
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{
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"type": "event",
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"name": "Honeycomb hires Goldman Sachs advisor",
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"date": "2025-11-02",
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"entities": ["Honeycomb", "Goldman Sachs"],
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"signal_category": "financial",
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"m_and_a_weight": "high",
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"source": "theinformation.com/articles/honeycomb-advisors"
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},
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{
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"type": "event",
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"name": "Datadog acquires incident.io",
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"date": "2025-11-08",
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"entities": ["Datadog", "incident.io"],
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"signal_category": "strategic",
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"m_and_a_weight": "confirmed_event",
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"source": "datadog.com/blog/incident-io-acquisition"
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}
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]
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```
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**Step 4 — Score composite M&A probability**
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Aggregate signals per company over a rolling 90-day window:
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```
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Chronosphere:
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- CFO departed (high) +3
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- 2 corp dev job postings +2
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- No product release in 90d +1
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- Composite score: 6/10 → ELEVATED
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Honeycomb:
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- Hired investment bank +4
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- Board added PE partner +2
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- Founder "grateful" post +1
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- Composite score: 7/10 → HIGH
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Grafana Labs:
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- New enterprise partnerships +1
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- Active hiring across all -1 (normal growth, reduces M&A signal)
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- Composite score: 0/10 → LOW
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```
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**Step 5 — Generate M&A signal alert**
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```markdown
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# M&A Signal Alert: Enterprise Observability Sector
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**Date**: 2025-11-10 | **Window**: 90 days
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## HIGH probability
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- **Honeycomb**: Investment bank engagement + board changes suggest active process.
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Key evidence: Goldman Sachs advisory (The Information), new PE board member.
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Likely acquirers: Datadog, Cisco, ServiceNow.
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## ELEVATED probability
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- **Chronosphere**: Leadership turnover + hiring freeze + corp dev roles.
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Key evidence: CFO departure, no product releases, corp dev postings on LinkedIn.
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Could indicate: acquisition target OR internal restructuring.
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## LOW probability
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- **Grafana Labs**: Normal operating patterns, active hiring, regular releases.
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- **Datadog**: Active acquirer (incident.io deal closed), not a target.
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```
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---
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## Advanced Entity Extraction
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### Relationship Mapping from Unstructured Text
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Extract relationships by identifying sentence-level patterns that connect two named entities.
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**Pattern templates**:
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```
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[Person] joined [Company] as [Role]
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→ relation: works_at, attributes: {role: Role, event: "joined"}
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[Company] acquired [Company] for [Amount]
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→ relation: acquired, attributes: {amount: Amount}
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[Person] and [Person] co-founded [Company]
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→ relations: founded (x2), co_founded_with (between persons)
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[Company] partnered with [Company] to [Purpose]
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→ relation: partnered_with, attributes: {purpose: Purpose}
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[Person] left [Company] to join [Company]
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→ relation: left (old), works_at (new), attributes: {event: "departure"}
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```
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**Multi-hop relationships**: When A relates to B and B relates to C, infer indirect connections:
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```
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Sarah Chen works_at BetaStack
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BetaStack competes_with AlphaCloud
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→ Indirect: Sarah Chen is key_person_at competitor of AlphaCloud
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```
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**Negation detection**: Watch for negated relationships that should NOT be added:
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```
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"Company X denied it was in acquisition talks with Company Y"
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→ Do NOT add acquired relation. Add entity note: "denied acquisition rumor, [date]"
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"Former CEO of Company X" → Person left. Mark works_at as ended.
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```
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### Temporal Event Extraction (Timeline Construction)
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Extract dates and temporal markers to build event timelines.
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**Explicit dates**:
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```
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"On March 15, 2025, Acme launched ProductX"
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→ event: product_launch, date: 2025-03-15, entities: [Acme, ProductX]
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```
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**Relative dates** (resolve against article publication date):
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```
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"last week" → pub_date - 7 days
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"earlier today" → pub_date
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"next quarter" → pub_date + next fiscal quarter boundary
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"in Q3" → July-September of article's year
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"recently" → pub_date - 30 days (approximate, confidence: medium)
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```
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**Temporal ordering heuristics**:
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```
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"before the acquisition" → event precedes known acquisition date
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"following the launch" → event follows known launch date
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"amid layoffs" → event concurrent with layoff period
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```
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**Timeline output format**:
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```json
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{
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"entity": "Acme Corp",
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"timeline": [
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{"date": "2025-01-15", "event": "Series B ($40M)", "type": "funding", "confidence": "high"},
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{"date": "2025-03-20", "event": "Hired new CTO (Jane Lee)", "type": "leadership", "confidence": "high"},
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{"date": "2025-06-01", "event": "Launched v3.0", "type": "product", "confidence": "high"},
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{"date": "2025-08-10", "event": "Partnership with CloudCo", "type": "partnership", "confidence": "medium"},
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{"date": "2025-11-05", "event": "Acquired by BigCorp", "type": "acquisition", "confidence": "high"}
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]
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}
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```
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### Quantitative Data Extraction
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Extract numerical data points with units, context, and time reference.
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**Financial figures**:
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```
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Pattern: "[Company] raised $[amount][M/B] in [round]"
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Example: "Acme raised $40M in Series B"
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→ {metric: "funding", value: 40000000, currency: "USD", context: "Series B", entity: "Acme"}
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Pattern: "[Company] revenue of $[amount][M/B]"
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Example: "reported annual revenue of $120M"
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→ {metric: "revenue", value: 120000000, currency: "USD", period: "annual", entity: subject}
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```
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**Growth rates**:
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```
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Pattern: "[metric] grew [X]% [period]"
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Example: "ARR grew 45% year-over-year"
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→ {metric: "ARR_growth", value: 0.45, period: "YoY", entity: subject}
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Pattern: "from [X] to [Y]"
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Example: "headcount grew from 200 to 350"
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→ {metric: "headcount", previous: 200, current: 350, growth: 0.75, entity: subject}
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```
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|
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**Headcounts and scale metrics**:
|
||||
```
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"[Company] now has [N] employees"
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"[Company] serves [N] customers"
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"[Product] has [N] monthly active users"
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"[Company] operates in [N] countries"
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```
|
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|
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**Extraction validation rules**:
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- Currency amounts without a clear entity reference: discard or mark confidence "low"
|
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- Growth percentages without a base period: mark confidence "medium"
|
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- Round numbers (e.g., "about 1,000 employees"): flag as approximate
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- Conflicting numbers from different sources: record both, note discrepancy
|
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|
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### Multi-Source Entity Resolution
|
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|
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When the same entity appears across different sources with variations, deduplicate.
|
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|
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**Company name normalization**:
|
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```
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"Acme Corp" = "Acme Corporation" = "Acme, Inc." = "ACME" (when context matches)
|
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"Google" = "Alphabet" (parent) — but keep as separate entities with parent_of relation
|
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```
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|
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**Resolution rules**:
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| Signal | Match Confidence | Action |
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|--------|-----------------|--------|
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||||
| Exact name match | High | Merge immediately |
|
||||
| Name + same industry + same location | High | Merge |
|
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| Abbreviated name + same context | Medium | Merge with note |
|
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| Similar name, different industry | Low | Keep separate, flag for review |
|
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| Person same name, different company | Low | Keep separate unless linked by career event |
|
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|
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**Deduplication process**:
|
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1. Normalize: lowercase, strip legal suffixes, expand abbreviations
|
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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
|
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{
|
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"canonical": "entity_acme_corp",
|
||||
"aliases": ["Acme Corp", "Acme Corporation", "Acme, Inc.", "ACME"],
|
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"merged_from": ["source_techcrunch_entity_12", "source_linkedin_entity_89"],
|
||||
"merge_confidence": "high",
|
||||
"merge_reason": "exact name + same industry (SaaS) + same HQ (San Francisco)"
|
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}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 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.
|
||||
Reference in new issue
Block a user