Skip to content
Back to skills

Osint

ASecurity

OSINT investigation — discover, track, and report on people, companies, and technologies

  • 96 stars
  • 0 votes
  • 0 copies
  • 2 views
  • Added September 3, 2026
ai-agentsrustgogitsecurity

Security analysis

A100/100

Scanned September 3, 2026

npx -y skills add WingedGuardian/GENesis-AGI --skill osint --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Osint?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Osint
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/wingedguardian-osint/badge)](https://www.skillsdirectory.com/skills/wingedguardian-osint)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: osint
description: OSINT investigation — discover, track, and report on people, companies, and technologies
consumer: cc_background_task
phase: 7
skill_type: uplift
---

# OSINT Investigation

## Purpose

Conduct open-source intelligence gathering on specific targets — people,
companies, technologies, or markets. Discover publicly available information,
track changes over time, assess source reliability, and produce structured
intelligence reports.

This is NOT the awareness loop (which monitors Genesis's own systems). This
is outward-facing investigation — finding information about external entities.

## When to Use

- User requests research on a person, company, or competitive entity.
- Lead generation identifies a prospect needing deeper enrichment.
- Strategic reflection flags a competitor or technology to monitor.
- A scheduled monitoring task triggers a collection cycle.
- An inbox item references an entity worth investigating.

## Investigation Pipeline

### Phase 1: Target Initialization

Define the target clearly:
- **Type:** person | company | technology | market | competitor
- **Identity:** name, aliases, known associations
- **Scope:** what specifically to find (general profile, funding history,
  team composition, technology stack, competitive positioning)
- **Depth:** surface (headlines only) | deep (full articles + sources) |
  exhaustive (multi-hop research across connected entities)

### Phase 2: Query Construction

Build 10-20 search queries tailored to target type:

**Person:**
- `"[name]" [company]` — basic association
- `"[name]" site:linkedin.com` — public LinkedIn profile
- `"[name]" [industry] interview OR podcast OR keynote`
- `"[name]" [company] announcement OR appointed OR promoted`
- `"[name]" github OR gitlab` — technical contributions

**Company:**
- `"[company]" funding OR "series A" OR "series B" OR acquisition`
- `"[company]" hiring OR careers OR "we're hiring"`
- `site:crunchbase.com "[company]"` — Crunchbase profile
- `site:stackshare.io "[company]"` OR `site:builtwith.com "[company]"` — tech stack
- `"[company]" review OR glassdoor` — employee sentiment
- `"[company]" revenue OR valuation OR growth`

**Technology:**
- `"[technology]" benchmark OR comparison OR vs`
- `"[technology]" production OR deployment OR "in production"`
- `"[technology]" github stars OR contributors OR releases`
- `"[technology]" adoption OR migration OR "switched to"`

### Phase 3: Collection Sweep

For each query:
1. Search (SearXNG / web search)
2. Fetch top 3-5 results
3. Extract structured data: names, dates, numbers, relationships
4. Tag each data point with: source URL, timestamp, confidence, relevance

**Source quality heuristics:**
- Official sources (filings, gov data, press releases) = Very High
- Institutional (Reuters, AP, established publications) = High
- Professional (industry pubs, analyst reports) = Medium-High
- Community (forums, social media, reviews) = Medium
- Anonymous/unverified = Low

### Phase 4: Entity Extraction

For each data point, extract entities and relationships:

**Entity types:** Person, Organization, Product, Event, Financial, Technology, Location

**Relationship types:** works_at, founded, invested_in, competes_with,
partnered_with, launched, acquired, uses_technology, located_in, reports_to

Store as structured records:
```yaml
entity:
  name: <canonical name>
  type: <entity type>
  attributes:
    title: <if person>
    industry: <if company>
    founded: <if company>
    headcount: <if known>
  sources:
    - url: <source URL>
      reliability: <very_high | high | medium_high | medium | low>
      date_accessed: <YYYY-MM-DD>
  first_seen: <YYYY-MM-DD>
  confidence: high | medium | low
```

### Phase 5: Change Detection (for ongoing monitoring)

If this is a follow-up cycle on a previously investigated target:
1. Compare current findings against previous snapshot
2. Classify changes:
   - **CRITICAL** (immediate attention): Leadership change, acquisition,
     major funding (>$10M), product discontinuation, legal action, security breach
   - **IMPORTANT** (include in next report): New product launch, partnership,
     hiring surge (>5 roles), pricing change, competitor move
   - **MINOR** (note in report): Blog post, minor update, conference appearance
3. Flag critical changes for immediate surfacing

### Phase 6: Report Generation

Produce a structured intelligence report:

```markdown
# OSINT Report: [Target Name]

**Date:** YYYY-MM-DD
**Depth:** surface | deep | exhaustive
**Sources consulted:** N

## Summary
<3-5 sentence overview of key findings>

## Entity Profile
<structured profile data>

## Key Findings
1. <finding with source>
2. <finding with source>

## Changes Since Last Report (if applicable)
- [CRITICAL] <change>
- [IMPORTANT] <change>

## Relationships
<entity → relationship → entity map>

## Source Quality
| Source | Reliability | Data Points |
|--------|------------|-------------|

## Confidence Assessment
<overall confidence in findings, gaps identified>

## Recommended Follow-Up
- <what to investigate next>
- <what to monitor>
```

### Phase 7: State Persistence

- Store entity data as observations via MemoryStore
- Update existing observations if entity already tracked
- Record investigation metadata for future cycles

## Source Evaluation Checklist

Before trusting any data point, check:
- [ ] **Recency** — Is this from the last 12 months?
- [ ] **Primary vs Secondary** — Is this the original source?
- [ ] **Corroboration** — Can a second independent source confirm?
- [ ] **Bias** — Does the source have an incentive to distort?
- [ ] **Specificity** — Are claims specific and verifiable?
- [ ] **Track record** — Has this source been reliable before?

If 3+ checks fail, downgrade confidence to "low."

## Compliance Rules

- Only use publicly available information
- Do NOT attempt to bypass login walls, paywalls, or CAPTCHAs
- Do NOT scrape behind authentication barriers
- LinkedIn discovery uses `site:linkedin.com` via search engines only
- Respect robots.txt and rate limits
- Label all speculation as speculation

## Output Format

```yaml
investigation_id: <OSINT-YYYY-MM-DD-NNN>
target: <name>
target_type: <person | company | technology | market | competitor>
date: <YYYY-MM-DD>
depth: <surface | deep | exhaustive>
sources_consulted: <count>
entities_extracted: <count>
key_findings:
  - finding: <description>
    confidence: high | medium | low
    sources:
      - <URL>
    significance: critical | important | minor
changes_detected:
  - change: <description>
    significance: critical | important | minor
recommended_actions:
  - <next step>
monitoring_schedule: <none | weekly | daily>
```

## References

- `src/genesis/skills/research/SKILL.md` — General research methodology
- `src/genesis/memory/` — MemoryStore for persistence
- `docs/reference/gemini-routing.md` — For video content during investigation

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…