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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@@ -25,6 +25,16 @@ A good ICP answers these questions:
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| SMB | 50-500 | $25K-$250K/yr | 1-3 months |
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| Enterprise | 500+ | $250K+/yr | 3-12 months |
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### ICP Refinement Loop
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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?
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2. **Analyze low performers**: Look at leads scored below 40 — which ICP criteria were they missing? Were there false positives from overly broad keywords?
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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.
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4. **Track revisions**: Log each ICP revision with date, changes made, and rationale. This creates an audit trail showing how targeting improved over time.
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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.
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---
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## Web Research Techniques for Lead Discovery
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@@ -171,6 +181,17 @@ site:thomasnet.com "[product category]"
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- Company blog/content activity (engagement level)
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- Executive team changes
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### Enrichment Depth Escalation Strategy
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Not all leads deserve the same enrichment investment. Use a two-pass approach:
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1. **First pass (Standard depth)**: Enrich all discovered leads at Standard depth. This is cost-effective and provides enough data for initial scoring.
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2. **Score checkpoint**: After the first pass, score all leads. Any lead scoring 70+ at Standard depth is a strong candidate.
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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.
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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.
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This approach typically reduces total enrichment cost by 40-60% while maintaining the same output quality for top-tier leads.
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### Email Pattern Discovery
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Common corporate email formats (try in order):
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1. `firstname@company.com` (most common for small companies)
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@@ -275,6 +296,9 @@ For each enterprise lead, attempt to discover:
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```
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### Choosing Between BANT and MEDDIC
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The `qualification_framework` setting controls which framework is applied. When set to "auto", use this decision table:
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| Scenario | Recommended Framework |
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|----------|----------------------|
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| SMB / startup targets, short sales cycle | BANT |
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@@ -343,12 +367,48 @@ Name,Title,Company,Company URL,LinkedIn,Industry,Size,Score,Discovered,Notes
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### Markdown Table Format
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```markdown
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| # | Name | Title | Company | Score | Key Signal |
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|---|------|-------|---------|-------|------------|
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| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | Series B funded, hiring |
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| 2 | John Doe | CTO | Beta Inc | 72 | Product launch Q1 2025 |
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| # | Name | Title | Company | Score | Grade | Qualification | Key Signal |
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|---|------|-------|---------|-------|-------|---------------|------------|
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| 1 | Jane Smith | VP Engineering | Acme Corp | 85 | A | BANT 4/4 | Series B funded, hiring |
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| 2 | John Doe | CTO | Beta Inc | 72 | B | BANT 3/4 | Product launch Q1 2025 |
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```
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### CRM Export Field Mappings
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When `crm_export_format` is configured, produce an additional file with CRM-native field names:
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**HubSpot** (JSON):
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| Lead Field | HubSpot Property |
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|------------|-----------------|
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| first_name | `firstname` |
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| last_name | `lastname` |
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| title | `jobtitle` |
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| company | `company` |
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| company_url | `website` |
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| industry | `industry` |
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| score | `hs_lead_status` (mapped: 80+ = "New", 60-79 = "Open", <60 = "In Progress") |
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**Salesforce** (CSV):
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| Lead Field | Salesforce Field |
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|------------|-----------------|
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| first_name | `FirstName` |
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| last_name | `LastName` |
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| title | `Title` |
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| company | `Company` |
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| company_url | `Website` |
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| industry | `Industry` |
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| score | `Rating` (mapped: 80+ = "Hot", 60-79 = "Warm", <60 = "Cold") |
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| lead_source | `LeadSource` |
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**Pipedrive** (JSON):
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| Lead Field | Pipedrive Field |
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|------------|----------------|
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| full_name | `name` |
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| title | `job_title` |
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| company | `org_name` |
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| company_url | `org_address` |
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| notes | `note` |
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---
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## Worked Examples
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