--- name: collector-hand-skill-c9a7e7114b8c 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". --- Source attribution: RightNow-AI Original project: https://github.com/RightNow-AI/openfang License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: RightNow-AI Original project: https://github.com/RightNow-AI/openfang Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Marketing, Sales Roles: Business Development, Data Analyst, Growth Marketer, Market Research Analyst, Product Marketer Specialties: API Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 02ccafd70be3ed1503a0fc7abeccde79bd645e39f57a54f3739053e046703f36