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Overview

Collector Hand is an AI-powered intelligence collection engine that monitors any target (company, person, technology, market) 24/7, building a living knowledge graph and detecting changes over time. Category: Data
Icon: 🔍

What It Does

1

Define Target

Specify what to monitor (company, person, technology, market, or topic)
2

Continuous Collection

Run scheduled sweeps across web sources, news, social media, and documents
3

Build Knowledge Graph

Extract entities and relationships, building a structured intelligence database
4

Detect Changes

Compare each cycle against previous state, identifying new developments
5

Generate Reports

Deliver intelligence reports highlighting key changes and trends

Configuration

Target Settings

SettingDescriptionExample
Target SubjectWhat to monitorTesla, Elon Musk, Rust language, EV market
Focus AreaAnalysis lensmarket, business, competitor, person, technology
Collection DepthHow thoroughsurface, deep, exhaustive

Collection Schedule

SettingOptionsUse Case
Update Frequencyhourly, every_6h, daily, weeklyHow often to collect
Max Sources Per Cycle10, 30, 50, 100Budget vs thoroughness

Intelligence Features

SettingDescription
Alert on ChangesPublish event when significant changes detected
Track SentimentAnalyze and track sentiment trends over time
Report Formatmarkdown, json, html

Activation

Basic Setup

openfang hand activate collector
Configure your target:
openfang hand config collector \
  --set target_subject="Tesla" \
  --set focus_area="business" \
  --set collection_depth="deep" \
  --set update_frequency="daily" \
  --set alert_on_changes="true"

Example Workflow

Monitor: Anthropic (company)
Focus: Business intelligence
Frequency: Daily updates
Depth: Deep (full articles + sources)

> Monitor Anthropic for product launches, partnerships, and leadership changes
Collector Hand will:
  1. Build initial target profile and query set
  2. Run daily collection sweeps
  3. Extract entities (people, products, events, partnerships)
  4. Store in knowledge graph
  5. Compare against yesterday’s snapshot
  6. Generate collector_report_2026-03-06.md with changes
  7. Alert you if critical changes detected

How It Works

1. Target Initialization

Parses the target subject to determine type:
Target TypeCollection Strategy
CompanyProducts, leadership, funding, partnerships, news
PersonPublications, talks, job changes, social activity
TechnologyReleases, adoption, benchmarks, competitors
MarketTrends, players, reports, regulations
CompetitorProduct launches, pricing, reviews, hiring
Builds initial query set (10-20 queries tailored to target type):
// Example for company target "Anthropic"
const queries = [
  "Anthropic news",
  "Anthropic product launch",
  "Anthropic partnership",
  "Anthropic funding",
  "Anthropic leadership changes",
  "Anthropic Claude API",
  "Anthropic vs OpenAI",
  "Anthropic latest 2026"
]

2. Collection Sweep

For each query (up to max_sources_per_cycle):
1

Search

web_search(query) to find recent results
2

Fetch

web_fetch(url) promising articles, press releases, reports
3

Extract

Extract entities: people, companies, products, dates, numbers, events
4

Tag

Attach source URL, timestamp, confidence level, relevance score
Source quality heuristics:
Source TypeConfidenceExamples
Official sourcesHighCompany website, SEC filings, press releases
Established mediaMedium-HighReuters, Bloomberg, TechCrunch
Blog posts, socialMediumPersonal blogs, Twitter, LinkedIn
Forums, anonymousLowReddit, HN comments (without verification)

3. Knowledge Graph Construction

For each data point:
// Add entities
knowledge_add_entity({
  type: "person",
  name: "Dario Amodei",
  role: "CEO",
  company: "Anthropic",
  last_seen: "2026-03-06"
})

knowledge_add_entity({
  type: "product",
  name: "Claude 3.5",
  company: "Anthropic",
  category: "LLM",
  launch_date: "2024-06-20"
})

knowledge_add_entity({
  type: "event",
  description: "Series C funding round",
  date: "2025-01-15",
  amount: "$750M",
  participants: ["Google", "Spark Capital"]
})

// Add relationships
knowledge_add_relation({
  from: "Dario Amodei",
  to: "Anthropic",
  type: "founded"
})

knowledge_add_relation({
  from: "Anthropic",
  to: "Claude 3.5",
  type: "launched"
})

knowledge_add_relation({
  from: "Google",
  to: "Anthropic",
  type: "invested_in",
  metadata: {amount: "$300M", date: "2025-01-15"}
})

4. Change Detection

Compare current snapshot vs collector_knowledge_base.json: Identified changes:
Change TypeSignificanceExample
New entitiesCritical/ImportantNew executive hire, new product launch
Changed attributesCriticalPerson changed company, new funding round
New relationshipsImportantNew partnership, acquisition
Disappeared entitiesMinorNo longer mentioned in sources
Scoring changes:
function scoreChange(change) {
  // Critical changes (trigger alerts)
  if (change.type === "leadership_change") return "critical"
  if (change.type === "acquisition") return "critical"
  if (change.type === "major_funding") return "critical"
  if (change.type === "product_launch") return "critical"
  
  // Important changes
  if (change.type === "new_partnership") return "important"
  if (change.type === "hiring_surge") return "important"
  if (change.type === "pricing_change") return "important"
  
  // Minor changes
  return "minor"
}
If alert_on_changes = true and critical changes found:
event_publish({
  type: "collector_alert",
  target: "Anthropic",
  changes: [
    "New partnership with Google Cloud announced",
    "Dario Amodei interviewed on AI safety"
  ]
})

5. Sentiment Tracking (Optional)

If track_sentiment = true: For each source, classify:
  • Positive — Praise, success stories, growth
  • Negative — Criticism, failures, controversy
  • Neutral — Factual reporting
Track trend vs previous cycle:
const sentiment = {
  current: {positive: 60%, neutral: 30%, negative: 10%},
  previous: {positive: 40%, neutral: 50%, negative: 10%},
  trend: "sentiment_improving"
}

6. Report Generation

Markdown format:
# Intelligence Report: Anthropic
**Date**: 2026-03-06 | **Cycle**: 42 | **Sources Processed**: 28

## Key Changes Since Last Report

### Critical
- **Partnership announced**: Google Cloud strategic partnership for enterprise Claude API
- **Product launch**: Claude 3.5 Sonnet now supports 200K context window

### Important
- Dario Amodei interviewed on Lex Fridman podcast about AI safety
- Hiring surge: 15 new ML research positions opened

## Intelligence Summary

Anthropic continues aggressive expansion in enterprise market with Google Cloud
partnership announced March 5. The deal includes preferential pricing for GCP
customers and integration with Vertex AI. CEO Dario Amodei emphasized safety
and reliability in podcast appearance, positioning against OpenAI's rapid
deployment strategy.

Product development remains focused on context length and safety. Claude 3.5
Sonnet update brings 200K context (vs 100K previously), maintaining quality
at extended lengths. Internal benchmarks show 15% improvement on long-document
tasks.

## Entity Map

| Entity | Type | Status | Confidence |
|--------|------|--------|------------|
| Dario Amodei | Person | CEO, active | High |
| Claude 3.5 Sonnet | Product | Updated | High |
| Google Cloud | Company | New partner | High |
| Vertex AI | Product | Integration | Medium |

## Sources

1. [Anthropic announces Google Cloud partnership](https://anthropic.com/news) — confidence: high — Official press release, March 5
2. [Lex Fridman podcast #412](https://youtube.com/...) — confidence: high — Dario Amodei interview
3. [TechCrunch: Anthropic expands context window](https://techcrunch.com/...) — confidence: medium-high — March 6

## Sentiment Trend
Positive: 60% | Neutral: 30% | Negative: 10%  
Trend: ↑ improving (up from 40% positive last cycle)
Saved as: collector_report_2026-03-06.md

Output

FileDescription
collector_report_YYYY-MM-DD.*Intelligence report for each cycle
collector_knowledge_base.jsonCumulative knowledge graph

Dashboard Metrics

  • Data Points — Total intelligence items collected
  • Entities Tracked — Unique entities in knowledge graph
  • Reports Generated — Number of reports delivered
  • Last Update — Timestamp of most recent collection

Use Cases

Competitive Intelligence

openfang hand config collector \
  --set target_subject="Competitor Inc" \
  --set focus_area="competitor" \
  --set update_frequency="daily" \
  --set alert_on_changes="true"
Monitor:
  • Product launches
  • Pricing changes
  • Customer reviews and sentiment
  • Hiring patterns
  • Partnerships

Person Tracking

openfang hand config collector \
  --set target_subject="Sam Altman" \
  --set focus_area="person" \
  --set collection_depth="deep"
Track:
  • Public appearances
  • Interviews and talks
  • Social media activity
  • Company changes
  • Investments

Market Intelligence

openfang hand config collector \
  --set target_subject="AI agent market" \
  --set focus_area="market" \
  --set collection_depth="exhaustive"
Analyze:
  • Market size and trends
  • New players
  • Regulatory changes
  • Technology shifts
  • Investment flows

Tips & Best Practices

For best results:
  • Start with Daily frequency — Hourly can be noisy for most targets
  • Use Deep collection depth for active targets, Surface for monitoring
  • Enable sentiment tracking for brand monitoring
  • Review knowledge graph monthly to prune outdated entities
  • Combine with Predictor Hand to forecast based on collected trends
Collector Hand only uses publicly available information. Never attempt to collect data behind login walls or from restricted sources.

Next Steps

Predictor Hand

Make predictions based on collected intelligence

Researcher Hand

Deep-dive research on discovered topics