collector-hand-skill — Автоматизированный сбор и анализ разведывательных данных

--- name: collector-hand-skill version: "1.0.0" description: "Expert knowledge for AI intelligence collection — OSINT methodology, entity extraction, knowledge graphs, change detection, and sentiment analysis" runtime: prompt_only --- # Intelligence Collection Expert Knowledge ## OSINT Methodology ### Collection Cycle 1. **Planning**: Define target, scope, and collection requirements 2. **Collection**: Gather raw data from open sources 3. **Processing**: Extract entities, relationships, and data points 4. **Analysis**: Synthesize findings, identify patterns, detect changes 5. **Dissemination**: Generate reports, alerts, and updates 6. **Feedback**: Refine queries based on what worked and what didn't ### Source Categories (by reliability) | Tier | Source Type | Reliability | Examples | |------|-----------|-------------|---------| | 1 | Official/Primary | Very High | Company filings, government data, press releases | | 2 | Institutional | High | News agencies (Reuters, AP), research institutions | | 3 | Professional | Medium-High | Industry publications, analyst reports, expert blogs | | 4 | Community | Medium | Forums, social media, review sites | | 5 | Anonymous/Unverified | Low | Anonymous posts, rumors, unattributed claims | ### Search Query Construction by Focus Area **Market Intelligence**: ``` "[target] market share" "[target] industry report [year]" "[target] TAM SAM SOM" "[target] growth rate" "[target] market analysis" "[target industry] trends [year]" ``` **Business Intelligence**: ``` "[company] revenue" OR "[company] earnings" "[company] CEO" OR "[company] leadership team" "[company] strategy" OR "[company] roadmap" "[company] partnerships" OR "[company] acquisition" "[company] annual report" OR "[company] 10-K" site:sec.gov "[company]" ``` **Competitor Analysis**: ``` "[company] vs [competitor]" "[company] alternative" "[company] review" OR "[company] comparison" "[company] pricing" site:g2.com OR site:capterra.com "[company] customer reviews" site:trustpilot.com "switch from [company] to" ``` **Person Tracking**: ``` "[person name]" "[company]" "[person name]" interview OR podcast OR keynote "[person name]" site:linkedin.com "[person name]" publication OR paper "[person name]" conference OR summit ``` **Technology Monitoring**: ``` "[technology] release" OR "[technology] update" "[technology] benchmark [year]" "[technology] adoption" OR "[technology] usage statistics" "[technology] vs [alternative]" "[technology]" site:github.com "[technology] roadmap" OR "[technology] changelog" ``` --- ## Entity Extraction Patterns ### Named Entity Types 1. **Person**: Name, title, organization, role 2. **Organization**: Company name, type, industry, location, size 3. **Product**: Product name, company, category, version 4. **Event**: Type, date, participants, location, significance 5. **Financial**: Amount, currency, type (funding, revenue, valuation) 6. **Technology**: Name, version, category, vendor 7. **Location**: City, state, country, region 8. **Date/Time**: Specific dates, time ranges, deadlines ### Extraction Heuristics - **Person detection**: Title + Name pattern ("CEO John Smith"), bylines, quoted speakers - **Organization detection**: Legal suffixes (Inc, LLC), "at [Company]", domain names - **Financial detection**: Currency symbols, "raised $X", "valued at", "revenue of" - **Event detection**: Date + verb ("launched on", "announced at", "acquired") - **Technology detection**: CamelCase names, version numbers, "built with", "powered by" --- ## Knowledge Graph Best Practices ### Entity Schema ```json { "entity_id": "unique_id", "name": "Entity Name", "type": "person|company|product|event|technology", "attributes": { "key": "value" }, "sources": ["url1", "url2"], "first_seen": "timestamp", "last_seen": "timestamp", "confidence": "high|medium|low" } ``` ### Relation Schema ```json { "source_entity": "entity_id_1", "relation": "works_at|founded|competes_with|...", "target_entity": "entity_id_2", "attributes": { "since": "date", "context": "description" }, "source": "url", "confidence": "high|medium|low" } ``` ### Common Relations | Relation | Between | Example | |----------|---------|---------| | works_at | Person → Company | "Jane Smith works at Acme" | | founded | Person → Company | "John Doe founded StartupX" | | invested_in | Company → Company | "VC Fund invested in StartupX" | | competes_with | Company → Company | "Acme competes with BetaCo" | | partnered_with | Company → Company | "Acme partnered with CloudY" | | launched | Company → Product | "Acme launched ProductZ" | | acquired | Company → Company | "BigCorp acquired StartupX" | | uses | Company → Technology | "Acme uses Kubernetes" | | mentioned_in | Entity → Source | "Acme mentioned in TechCrunch" | --- ## Change Detection Methodology ### Snapshot Comparison 1. Store the current state of all entities as a JSON snapshot 2. On next collection cycle, compare new state against previous snapshot 3. Classify changes: | Change Type | Significance | Example | |-------------|-------------|---------| | Entity appeared | Varies | New competitor enters market | | Entity disappeared | Important | Company goes quiet, product deprecated | | Attribute changed | Critical-Minor | CEO changed (critical), address changed (minor) | | New relation | Important | New partnership, acquisition, hiring | | Relation removed | Important | Person left company, partnership ended | | Sentiment shift | Important | Positive→Negative media coverage | ### Significance Scoring ``` CRITICAL (immediate alert): - Leadership change (CEO, CTO, board) - Acquisition or merger - Major funding round (>$10M) - Product discontinuation - Legal action or regulatory issue IMPORTANT (include in next report): - New product launch - New partnership or integration - Hiring surge (>5 roles) - Pricing change - Competitor move - Major customer win/loss MINOR (note in report): - Blog post or press mention - Minor update or patch - Social media activity spike - Conference appearance - Job posting (individual) ``` --- ## Sentiment Analysis Heuristics When `track_sentiment` is enabled, classify each source's tone: ### Classification Rules - **Positive indicators**: "growth", "innovation", "breakthrough", "success", "award", "expansion", "praise", "recommend" - **Negative indicators**: "lawsuit", "layoffs", "decline", "controversy", "failure", "breach", "criticism", "warning" - **Neutral indicators**: factual reporting without strong adjectives, data-only articles, announcements ### Sentiment Scoring ``` Strong positive: +2 (e.g., "Company wins major award") Mild positive: +1 (e.g., "Steady growth continues") Neutral: 0 (e.g., "Company releases Q3 report") Mild negative: -1 (e.g., "Faces increased competition") Strong negative: -2 (e.g., "Major data breach disclosed") ``` Track rolling average over last 5 collection cycles to detect trends. --- ## Report Templates ### Intelligence Brief (Markdown) ```markdown # Intelligence Report: [Target] **Date**: YYYY-MM-DD HH:MM UTC **Collection Cycle**: #N **Sources Processed**: X **New Data Points**: Y ## Priority Changes 1. [CRITICAL] [Description + source] 2. [IMPORTANT] [Description + source] ## Executive Summary [2-3 paragraph synthesis of new intelligence] ## Detailed Findings ### [Category 1] - Finding with [source](url) - Data point with confidence: high/medium/low ### [Category 2] - ... ## Entity Updates | Entity | Change | Previous | Current | Source | |--------|--------|----------|---------|--------| ## Sentiment Trend | Period | Score | Direction | Notable | |--------|-------|-----------|---------| ## Collection Metadata - Queries executed: N - Sources fetched: N - New entities: N - Updated entities: N - Next scheduled collection: [datetime] ``` --- ## Source Evaluation Checklist Before including data in the knowledge graph, evaluate: 1. **Recency**: Published within relevant timeframe? Stale data can mislead. 2. **Primary vs Secondary**: Is this the original source, or citing someone else? 3. **Corroboration**: Do other independent sources confirm this? 4. **Bias check**: Does the source have a financial or political interest in this claim? 5. **Specificity**: Does it provide concrete data, or vague assertions? 6. **Track record**: Has this source been reliable in the past? If a claim fails 3+ checks, downgrade its confidence to "low".