rightnow-ai/collector-hand-skill

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"

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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"

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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" |

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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)

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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.

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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]

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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".