collector-hand-skill

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

{
  "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

{
  "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)

# 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".