feat(hands): improve 6 lower-scoring hands — system prompts and SKILL.md depth
- browser: 5→7 phases, SPA detection, error recovery decision tree, 3 new settings - strategist: framework integration methodology, 7 anti-patterns, uncertainty quantification - lead: remove clip language, add BANT/MEDDIC qualification, 3 new settings + CRM export - researcher: CRAAP→CRAAP+, 7-step conflict resolution, 6-item cognitive bias audit - collector: concrete change classification (structural/content/metadata), 5-factor scoring, 2 new settings - apitester: OWASP Top 10 checklist, 4 load test profiles, contract testing phase, GraphQL/Webhook patterns
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@@ -196,6 +196,67 @@ label = "Standard (+ company size, industry, tech stack)"
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value = "deep"
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label = "Deep (+ funding, recent news, social profiles)"
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[[settings]]
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key = "lead_score_threshold"
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label = "Lead Score Threshold"
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description = "Minimum score (0-100) for a lead to be included in reports"
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setting_type = "select"
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default = "60"
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[[settings.options]]
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value = "40"
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label = "40 — Include warm and hot leads"
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[[settings.options]]
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value = "60"
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label = "60 — Warm leads and above (recommended)"
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[[settings.options]]
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value = "80"
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label = "80 — Hot leads only"
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[[settings]]
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key = "qualification_framework"
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label = "Qualification Framework"
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description = "Sales qualification methodology to apply during lead scoring"
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setting_type = "select"
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default = "bant"
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[[settings.options]]
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value = "bant"
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label = "BANT (Budget, Authority, Need, Timeline)"
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[[settings.options]]
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value = "meddic"
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label = "MEDDIC (Metrics, Economic Buyer, Decision Criteria, Process, Pain, Champion)"
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[[settings.options]]
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value = "auto"
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label = "Auto (BANT for SMB, MEDDIC for Enterprise)"
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[[settings]]
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key = "crm_export_format"
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label = "CRM Export Format"
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description = "Generate an additional CRM-ready export alongside the standard report"
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setting_type = "select"
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default = "none"
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[[settings.options]]
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value = "none"
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label = "None (standard report only)"
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[[settings.options]]
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value = "hubspot"
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label = "HubSpot"
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[[settings.options]]
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value = "salesforce"
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label = "Salesforce"
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[[settings.options]]
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value = "pipedrive"
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label = "Pipedrive"
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# ─── Agent configuration ─────────────────────────────────────────────────────
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[agent]
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@@ -207,7 +268,7 @@ model = "default"
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max_tokens = 16384
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temperature = 0.3
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max_iterations = 50
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system_prompt = """You are Lead Hand — an autonomous lead generation engine that discovers, enriches, and delivers qualified leads 24/7.
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system_prompt = """You are Lead Hand — an autonomous lead generation engine that discovers, qualifies, enriches, and delivers sales-ready leads 24/7. You combine systematic web research with structured qualification frameworks (BANT/MEDDIC) to produce leads that sales teams can act on immediately.
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## Phase 0 — Platform Detection (ALWAYS DO THIS FIRST)
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@@ -225,7 +286,7 @@ Then set your approach:
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On first run:
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1. Check memory_recall for `lead_hand_state` — if it exists, you're resuming
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2. Read the **User Configuration** section for target_industry, target_role, company_size, geo_focus, etc.
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2. Read the **User Configuration** section for target_industry, target_role, company_size, geo_focus, qualification_framework, lead_score_threshold, crm_export_format, etc.
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3. Create your delivery schedule using schedule_create based on `delivery_schedule` setting
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4. Load any existing lead database from `leads_database.json` via file_read (if it exists)
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@@ -236,7 +297,7 @@ On subsequent runs:
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---
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## Phase 2 — Target Profile Construction
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## Phase 2 — Ideal Customer Profile Construction & Refinement
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Build an Ideal Customer Profile (ICP) from user settings:
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- Industry: from `target_industry` setting
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@@ -244,6 +305,13 @@ Build an Ideal Customer Profile (ICP) from user settings:
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- Company size filter: from `company_size` setting
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- Geography: from `geo_focus` setting
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**ICP Refinement Loop** (run after every 3 reports):
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1. Analyze the top 20% of leads by score — what attributes do they share?
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2. Analyze the bottom 20% — what attributes caused low scores?
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3. Tighten ICP criteria based on patterns: narrow industry keywords, adjust company size range, add tech stack requirements
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4. Log ICP revisions to `icp_revision_log.json` with date and rationale
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5. memory_store `lead_hand_icp_version` with the current ICP revision number
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Store the ICP in the knowledge graph:
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- knowledge_add_entity: ICP profile node
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- knowledge_add_relation: link ICP to target attributes
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@@ -263,22 +331,27 @@ Execute a multi-query web research loop:
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3. For promising results, use web_fetch to extract company/person details
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4. Extract structured lead data: name, title, company, company_url, linkedin_url (if public), email pattern
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Target: discover 2-3x the `leads_per_report` setting to allow for filtering.
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Target: discover 2-3x the `leads_per_report` setting to allow for filtering and qualification.
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---
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## Phase 4 — Lead Enrichment
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For each discovered lead, based on `enrichment_depth`:
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Apply enrichment based on `enrichment_depth` setting. Higher depth costs more tool calls but produces better-qualified leads.
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**Basic**: name, title, company — already have this from discovery
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**Standard**: additionally fetch:
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**Basic**: name, title, company — already have this from discovery. Use for high-volume, low-touch lists.
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**Standard** (recommended default): additionally fetch:
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- Company website (web_fetch company_url) — extract: employee count, industry, tech stack, product description
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- Look for company on job boards — hiring signals indicate growth
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**Deep**: additionally fetch:
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- Cross-reference at least 2 sources per company to verify data accuracy
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**Deep** (best for enterprise targets): additionally fetch:
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- Recent funding news (web_search "[company] funding round")
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- Recent company news (web_search "[company] news 2025")
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- Social profiles (web_search "[person name] [company] linkedin twitter")
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- Competitive landscape (what tools/vendors they currently use)
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- Negative signals: layoffs, lawsuits, executive departures
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**Enrichment depth escalation**: If a lead scores above 70 at Standard depth, automatically re-enrich at Deep depth to maximize qualification data. This targets deep enrichment resources only at the most promising leads.
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Store enriched entities in knowledge graph:
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- knowledge_add_entity for each lead and company
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@@ -286,10 +359,44 @@ Store enriched entities in knowledge graph:
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---
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## Phase 5 — Deduplication & Scoring
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## Phase 5 — Qualification
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Apply the qualification framework configured by the `qualification_framework` setting.
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### BANT Qualification (default — best for SMB/startup targets, short sales cycles)
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For each lead, assess four dimensions from enrichment data:
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- **Budget**: funding rounds, revenue estimates, pricing tier of current tools, job postings for related roles
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- **Authority**: is the contact a decision-maker? VP+, C-level, Director, listed on Leadership page
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- **Need**: job postings mentioning the pain point, tech stack gaps, competitor tool usage, forum complaints
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- **Timeline**: contract renewals, compliance deadlines, product launches, recent leadership changes
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Apply BANT bonus points on top of the base score:
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Budget confirmed: +5 | Authority confirmed: +5 | Need confirmed: +5 | Timeline confirmed: +5 (max +20)
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### MEDDIC Qualification (best for enterprise targets, $100K+ deal size)
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For each enterprise lead (500+ employees or score > 80), attempt to discover:
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- **Metrics**: quantifiable outcomes the buyer cares about (case studies, KPIs in job postings)
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- **Economic Buyer**: person with budget authority (CFO, CEO, VP Finance, Head of Procurement)
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- **Decision Criteria**: how they evaluate vendors (RFP docs, comparison posts, compliance requirements)
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- **Decision Process**: steps from evaluation to purchase (procurement team, legal review, pilot mentions)
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- **Identify Pain**: specific problems driving a purchase (support forums, reviews, analyst reports)
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- **Champion**: internal advocate (conference speakers, blog authors, open-source contributors)
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Log the MEDDIC score as X/6 dimensions discovered per lead.
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### Mixed-list strategy
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When the target list contains both SMB and enterprise leads:
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1. Run BANT on all leads (fast first pass)
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2. For enterprise leads that score A-grade (80+), run a MEDDIC deep pass
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3. Include the qualification framework used in the output for each lead
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---
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## Phase 6 — Deduplication & Scoring
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1. Compare new leads against existing `leads_database.json`:
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- Match on: normalized company name + person name
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- Match on: company website domain (most stable identifier)
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- Skip exact duplicates
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- Update existing leads with new enrichment data
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2. Score each lead (0-100):
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@@ -298,40 +405,59 @@ Store enriched entities in knowledge graph:
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- Enrichment completeness: +20 (all fields populated)
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- Recency: +15 (company active recently)
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- Accessibility: +15 (public contact info available)
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3. Sort by score descending
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4. Take top N leads per `leads_per_report` setting
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Then apply qualification bonuses (BANT: up to +20, MEDDIC: up to +10 for 5+ dimensions)
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Then apply negative modifiers:
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- Recent layoffs (>10% headcount): -10
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- Lawsuit / regulatory action: -5
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- Executive turnover (CEO/CTO departed): -5
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3. Apply the `lead_score_threshold` — only include leads at or above this score
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4. Sort by score descending
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5. Take top N leads per `leads_per_report` setting
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6. If fewer leads meet the threshold than requested, report honestly: "Found X leads meeting quality threshold; Y additional leads are partial matches below threshold"
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### Score interpretation for output:
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- 80-100 (A): Hot lead — prioritize immediate outreach
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- 60-79 (B): Warm lead — worth nurturing
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- 40-59 (C): Cool lead — needs further enrichment
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- 0-39 (D): Cold lead — deprioritize unless ICP changes
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---
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## Phase 6 — Report Generation
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## Phase 7 — Report Generation
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Generate the report in the configured `output_format`:
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**CSV format**:
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```csv
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Name,Title,Company,Company URL,Industry,Company Size,Score,Discovery Date,Notes
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Name,Title,Company,Company URL,Industry,Company Size,Score,Grade,Qualification,Discovery Date,Notes
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```
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**JSON format**:
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```json
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[{"name": "...", "title": "...", "company": "...", "company_url": "...", "industry": "...", "size": "...", "score": 85, "discovered": "2025-01-15", "enrichment": {...}}]
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[{"name": "...", "title": "...", "company": "...", "company_url": "...", "industry": "...", "size": "...", "score": 85, "grade": "A", "qualification": {"framework": "BANT", "budget": true, "authority": true, "need": true, "timeline": false}, "discovered": "2025-01-15", "enrichment": {...}}]
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```
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**Markdown Table format**:
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```markdown
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| # | Name | Title | Company | Score | Signal |
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|---|------|-------|---------|-------|--------|
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| # | Name | Title | Company | Score | Grade | Qualification | Key Signal |
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|---|------|-------|---------|-------|-------|---------------|------------|
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```
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**CRM export** (when `crm_export_format` is set):
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- **hubspot**: JSON with HubSpot contact property names (firstname, lastname, jobtitle, company, hs_lead_status)
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- **salesforce**: CSV with Salesforce standard field names (FirstName, LastName, Title, Company, LeadSource, Rating)
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- **pipedrive**: JSON with Pipedrive person/organization fields (name, org_id, title, email)
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Save report to: `lead_report_YYYY-MM-DD.{csv,json,md}`
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If CRM export is enabled, also save: `lead_report_YYYY-MM-DD_crm.{csv,json}`
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---
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## Phase 7 — State Persistence
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## Phase 8 — State Persistence
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After each run:
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1. Update `leads_database.json` with all known leads (new + existing)
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2. memory_store `lead_hand_state` with: last_run, total_leads, report_count
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2. memory_store `lead_hand_state` with: last_run, total_leads, report_count, icp_version
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3. Update dashboard stats:
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- memory_store `lead_hand_leads_found` — total unique leads discovered
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- memory_store `lead_hand_reports_generated` — increment report count
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@@ -348,6 +474,7 @@ After each run:
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- If a search yields no results, try alternative queries before giving up
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- Always deduplicate before reporting — users hate seeing the same lead twice
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- Include your confidence level for enriched data (e.g. "email pattern: likely" vs "email: verified")
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- Quality over quantity: 10 well-qualified A-grade leads beat 50 unqualified names
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- If the user messages you directly, pause the pipeline and respond to their question
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"""
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@@ -428,6 +555,18 @@ description = "优先关注的地理区域（例如美国、欧洲、亚太、
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label = "信息丰富度"
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description = "对每条线索收集多少上下文信息"
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[i18n.zh.settings.lead_score_threshold]
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label = "线索评分阈值"
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description = "报告中包含线索的最低评分（0-100）"
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[i18n.zh.settings.qualification_framework]
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label = "资质评估框架"
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description = "线索评分时使用的销售资质评估方法论"
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[i18n.zh.settings.crm_export_format]
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label = "CRM 导出格式"
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description = "在标准报告之外生成 CRM 可导入的文件"
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# ─── Korean (한국어) ────────────────────────────────────────────────────
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[i18n.ko]
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@@ -471,6 +610,18 @@ description = "우선적으로 집중할 지역 (예: 미국, 유럽, 아시아
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label = "보강 깊이"
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description = "리드당 수집할 컨텍스트 정보의 수준"
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[i18n.ko.settings.lead_score_threshold]
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label = "리드 점수 기준"
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description = "보고서에 포함할 리드의 최소 점수 (0-100)"
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[i18n.ko.settings.qualification_framework]
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label = "자격 평가 프레임워크"
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description = "리드 스코어링 시 적용할 영업 자격 평가 방법론"
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[i18n.ko.settings.crm_export_format]
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label = "CRM 내보내기 형식"
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description = "표준 보고서와 함께 CRM 가져오기용 파일 생성"
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# ─── Japanese (日本語) ────────────────────────────────────────────────────
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[i18n.ja]
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@@ -514,6 +665,18 @@ description = "優先する地理的リージョン（例: 米国、欧州、APA
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label = "情報付加の深さ"
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description = "リードごとに収集するコンテキスト情報の量"
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[i18n.ja.settings.lead_score_threshold]
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label = "リードスコア閾値"
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description = "レポートに含めるリードの最低スコア（0-100）"
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[i18n.ja.settings.qualification_framework]
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label = "資格評価フレームワーク"
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description = "リードスコアリング時に適用する営業資格評価の方法論"
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[i18n.ja.settings.crm_export_format]
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label = "CRMエクスポート形式"
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description = "標準レポートに加えてCRMインポート用ファイルを生成"
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# ─── Spanish (Español) ────────────────────────────────────────────────────
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[i18n.es]
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@@ -557,6 +720,18 @@ description = "Región geográfica a priorizar (ej. EE.UU., Europa, Asia-Pacífi
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label = "Profundidad de enriquecimiento"
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description = "Cuánto contexto recopilar por cada lead"
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[i18n.es.settings.lead_score_threshold]
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label = "Umbral de puntuación"
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description = "Puntuación mínima (0-100) para incluir un lead en los informes"
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[i18n.es.settings.qualification_framework]
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label = "Marco de cualificación"
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description = "Metodología de cualificación comercial a aplicar durante la puntuación de leads"
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[i18n.es.settings.crm_export_format]
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label = "Formato de exportación CRM"
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description = "Generar un archivo importable para CRM junto al informe estándar"
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# ─── French (Français) ────────────────────────────────────────────────────
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[i18n.fr]
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@@ -600,6 +775,18 @@ description = "Région géographique prioritaire (ex. USA, Europe, Asie-Pacifiqu
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label = "Profondeur d'enrichissement"
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description = "Niveau d'informations contextuelles à collecter par prospect"
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[i18n.fr.settings.lead_score_threshold]
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label = "Seuil de score"
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description = "Score minimum (0-100) pour inclure un prospect dans les rapports"
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[i18n.fr.settings.qualification_framework]
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label = "Cadre de qualification"
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description = "Méthodologie de qualification commerciale appliquée lors du scoring des prospects"
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[i18n.fr.settings.crm_export_format]
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label = "Format d'export CRM"
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description = "Générer un fichier importable CRM en plus du rapport standard"
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||||
# ─── German (Deutsch) ────────────────────────────────────────────────────
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|
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[i18n.de]
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@@ -642,3 +829,15 @@ description = "Priorisierte geografische Region (z.B. USA, Europa, Asien-Pazifik
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[i18n.de.settings.enrichment_depth]
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label = "Anreicherungstiefe"
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description = "Umfang der pro Lead gesammelten Kontextinformationen"
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[i18n.de.settings.lead_score_threshold]
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label = "Lead-Score-Schwelle"
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description = "Mindestpunktzahl (0-100), um einen Lead in Berichte aufzunehmen"
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|
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[i18n.de.settings.qualification_framework]
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label = "Qualifizierungsrahmen"
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description = "Vertriebsqualifizierungsmethodik für die Lead-Bewertung"
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|
||||
[i18n.de.settings.crm_export_format]
|
||||
label = "CRM-Exportformat"
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||||
description = "Zusätzlich zum Standardbericht eine CRM-importierbare Datei erstellen"
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||||
+64
-4
@@ -25,6 +25,16 @@ A good ICP answers these questions:
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||||
| SMB | 50-500 | $25K-$250K/yr | 1-3 months |
|
||||
| Enterprise | 500+ | $250K+/yr | 3-12 months |
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||||
|
||||
### ICP Refinement Loop
|
||||
|
||||
The ICP should not be static. After every 3 report cycles, refine it:
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||||
|
||||
1. **Analyze top performers**: Look at leads scored 80+ — what industry sub-segments, company sizes, and role patterns appear most often?
|
||||
2. **Analyze low performers**: Look at leads scored below 40 — which ICP criteria were they missing? Were there false positives from overly broad keywords?
|
||||
3. **Tighten criteria**: Narrow industry keywords (e.g., "fintech" becomes "payment infrastructure fintech"), adjust company size range, add or remove geographic regions, refine role titles.
|
||||
4. **Track revisions**: Log each ICP revision with date, changes made, and rationale. This creates an audit trail showing how targeting improved over time.
|
||||
5. **Measure impact**: Compare average lead score before and after each ICP revision. A well-refined ICP should produce higher average scores with fewer total leads — quality over quantity.
|
||||
|
||||
---
|
||||
|
||||
## Web Research Techniques for Lead Discovery
|
||||
@@ -171,6 +181,17 @@ site:thomasnet.com "[product category]"
|
||||
- Company blog/content activity (engagement level)
|
||||
- Executive team changes
|
||||
|
||||
### Enrichment Depth Escalation Strategy
|
||||
|
||||
Not all leads deserve the same enrichment investment. Use a two-pass approach:
|
||||
|
||||
1. **First pass (Standard depth)**: Enrich all discovered leads at Standard depth. This is cost-effective and provides enough data for initial scoring.
|
||||
2. **Score checkpoint**: After the first pass, score all leads. Any lead scoring 70+ at Standard depth is a strong candidate.
|
||||
3. **Second pass (Deep depth)**: Re-enrich only leads scoring 70+ at Deep depth. This focuses expensive research (funding history, news, competitive analysis) on leads most likely to convert.
|
||||
4. **Skip threshold**: Leads scoring below 30 after Standard enrichment should not be enriched further — the data is unlikely to improve their score enough to matter.
|
||||
|
||||
This approach typically reduces total enrichment cost by 40-60% while maintaining the same output quality for top-tier leads.
|
||||
|
||||
### Email Pattern Discovery
|
||||
Common corporate email formats (try in order):
|
||||
1. `firstname@company.com` (most common for small companies)
|
||||
@@ -275,6 +296,9 @@ For each enterprise lead, attempt to discover:
|
||||
```
|
||||
|
||||
### Choosing Between BANT and MEDDIC
|
||||
|
||||
The `qualification_framework` setting controls which framework is applied. When set to "auto", use this decision table:
|
||||
|
||||
| Scenario | Recommended Framework |
|
||||
|----------|----------------------|
|
||||
| SMB / startup targets, short sales cycle | BANT |
|
||||
@@ -343,12 +367,48 @@ Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
|
||||
|
||||
### Markdown Table Format
|
||||
```markdown
|
||||
| # | Name | Title | Company | Score | Key Signal |
|
||||
|---|------|-------|---------|-------|------------|
|
||||
| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | Series B funded, hiring |
|
||||
| 2 | John Doe | CTO | Beta Inc | 72 | Product launch Q1 2025 |
|
||||
| # | Name | Title | Company | Score | Grade | Qualification | Key Signal |
|
||||
|---|------|-------|---------|-------|-------|---------------|------------|
|
||||
| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | A | BANT 4/4 | Series B funded, hiring |
|
||||
| 2 | John Doe | CTO | Beta Inc | 72 | B | BANT 3/4 | Product launch Q1 2025 |
|
||||
```
|
||||
|
||||
### CRM Export Field Mappings
|
||||
|
||||
When `crm_export_format` is configured, produce an additional file with CRM-native field names:
|
||||
|
||||
**HubSpot** (JSON):
|
||||
| Lead Field | HubSpot Property |
|
||||
|------------|-----------------|
|
||||
| first_name | `firstname` |
|
||||
| last_name | `lastname` |
|
||||
| title | `jobtitle` |
|
||||
| company | `company` |
|
||||
| company_url | `website` |
|
||||
| industry | `industry` |
|
||||
| score | `hs_lead_status` (mapped: 80+ = "New", 60-79 = "Open", <60 = "In Progress") |
|
||||
|
||||
**Salesforce** (CSV):
|
||||
| Lead Field | Salesforce Field |
|
||||
|------------|-----------------|
|
||||
| first_name | `FirstName` |
|
||||
| last_name | `LastName` |
|
||||
| title | `Title` |
|
||||
| company | `Company` |
|
||||
| company_url | `Website` |
|
||||
| industry | `Industry` |
|
||||
| score | `Rating` (mapped: 80+ = "Hot", 60-79 = "Warm", <60 = "Cold") |
|
||||
| lead_source | `LeadSource` |
|
||||
|
||||
**Pipedrive** (JSON):
|
||||
| Lead Field | Pipedrive Field |
|
||||
|------------|----------------|
|
||||
| full_name | `name` |
|
||||
| title | `job_title` |
|
||||
| company | `org_name` |
|
||||
| company_url | `org_address` |
|
||||
| notes | `note` |
|
||||
|
||||
---
|
||||
|
||||
## Worked Examples
|
||||
|
||||
Reference in new issue
Block a user