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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@@ -150,45 +150,82 @@ site:sec.gov "[company]"
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## Change Detection Methodology
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### Snapshot Comparison
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1. Store the current state of all entities as a JSON snapshot
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2. On next collection cycle, compare new state against previous snapshot
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3. Classify changes:
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### Change Classification
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| Change Type | Significance | Example |
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|-------------|-------------|---------|
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| Entity appeared | Varies | New competitor enters market |
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| Entity disappeared | Important | Company goes quiet, product deprecated |
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| Attribute changed | Critical-Minor | CEO changed (critical), address changed (minor) |
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| New relation | Important | New partnership, acquisition, hiring |
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| Relation removed | Important | Person left company, partnership ended |
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| Sentiment shift | Important | Positive→Negative media coverage |
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Every difference between the current snapshot and the previous one falls into exactly one category:
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| Category | Definition | Examples |
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|----------|-----------|---------|
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| **Structural** | Entity appeared/disappeared, relationship added/removed | New competitor enters market, person left company, product deprecated, new partnership formed |
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| **Content** | Attribute value changed on an existing entity | CEO changed, funding amount updated, version number bumped, pricing modified |
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| **Metadata** | Supporting data changed but core fact is the same | New source confirms existing fact, confidence upgraded, last_seen timestamp refreshed |
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### Cross-Source Deduplication
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Before scoring, deduplicate overlapping data points:
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1. **Normalize** entity names: strip legal suffixes (Inc, LLC, Corp), lowercase, expand common abbreviations
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2. **Merge** when 2+ sources report the same fact about the same entity — keep highest confidence, list all source URLs
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3. **Flag conflicts** when sources disagree on a fact (e.g., different funding amounts) — record both, mark as "conflicting — requires resolution"
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### Significance Scoring Algorithm
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Compute a numeric score (0-100) for each change:
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### Significance Scoring
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```
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CRITICAL (immediate alert):
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- Leadership change (CEO, CTO, board)
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- Acquisition or merger
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- Major funding round (>$10M)
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- Product discontinuation
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- Legal action or regulatory issue
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Base score (by category):
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Structural change = 60
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Content change = 40
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Metadata change = 5
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IMPORTANT (include in next report):
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- New product launch
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- New partnership or integration
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- Hiring surge (>5 roles)
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- Pricing change
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- Competitor move
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- Major customer win/loss
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Source reliability modifier (best source tier for this data point):
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Tier 1 (official/primary) = +20
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Tier 2 (institutional) = +10
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Tier 3 (professional) = +5
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Tier 4-5 (community/anon) = +0
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MINOR (note in report):
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- Blog post or press mention
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- Minor update or patch
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- Social media activity spike
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- Conference appearance
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- Job posting (individual)
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Source freshness modifier (publication age):
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Within 24 hours = +10
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Within 7 days = +5
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Within 30 days = +0
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Older than 30 days = -10
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Corroboration modifier:
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Confirmed by 2+ independent sources = +10
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Single source only = +0
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Contradicted by another source = -15
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Focus area relevance:
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Directly matches configured focus_area = +10
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Tangentially related = +0
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Final score = clamp(base + reliability + freshness + corroboration + relevance, 0, 100)
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```
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### Alert Tier Mapping
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Map the computed significance score to an action tier using `change_significance_threshold` (configurable, default 60):
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```
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Score >= 80 → CRITICAL (immediate alert via event_publish)
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Examples: leadership change (CEO/CTO/CFO), acquisition or merger,
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major funding round (>$10M), product discontinuation,
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regulatory action, data breach
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Score >= threshold → IMPORTANT (include in next report)
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Examples: new product launch, new partnership, hiring surge (>5 roles),
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pricing change, significant competitor move, major customer win/loss
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Score < threshold → MINOR (note in report)
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Examples: blog post, minor update or patch, conference appearance,
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individual job posting, social media activity within normal range
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```
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### Source Reliability Filtering
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Apply the configured `source_reliability_threshold` (default: tier_3) to filter low-quality data:
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- **Discard** data points where ALL supporting sources fall below the threshold tier
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- **Exception**: if a below-threshold source is the ONLY source for a structural change, keep it but downgrade confidence to "low" and flag for corroboration in the next cycle
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---
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## Sentiment Analysis Heuristics
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