osint

name	osint
version	1.2.0
description	Open-source intelligence on people, companies, domains, and B2B accounts. Use when the user wants to investigate, vet, research, or build a dossier on a target — phrases like "OSINT", "due diligence", "background check", "research this person", "look into [company/domain]", "vet this prospect/vendor", "what does X do", "is this account worth pursuing", "find me a contact at", "who's the buyer for", or any open-source investigation task. Disambiguates identities before reporting and grades every claim by independent source count.

OSINT

Open-source intelligence gathering with disambiguation discipline, confidence grading, and tiered depth. Public sources only. No breach data. No social engineering. Scope-respecting.

This skill is the orchestrator. You do the thinking. Tools and scripts are workers — they fetch, you decide.

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How to use this skill

1. Classify the target. Person, company, domain/IP, B2B account, due-diligence subject, or threat entity. If ambiguous, ask the user.
2. Pick a depth. Quick (≤5 min, web search only) → Standard (15–30 min, multi-tool) → Deep (full dossier, may take an hour). Default to Standard unless the user signals otherwise.
3. Run the gates below — every investigation, every time.
4. Route to a workflow. See the routing table.
5. Produce the output contract — a markdown dossier plus a findings.json sidecar.

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The Four Gates

Run these in order. Skipping any of them is how OSINT goes wrong.

Gate 1 — Scope & Authorization

Before a single query, confirm:

• Lawful basis. Public information about a public-facing target (executive, company, public domain) is fine. Private individuals investigated for stalking, harassment, or unauthorized vetting are not. If the request smells like that, decline and say why.
• No prohibited methods. No paid breach databases. No social engineering. No accessing accounts you don't own. No bypassing paywalls or platform ToS. No active probing of infrastructure (port scans, vuln scans) without explicit authorization.
• Scope statement. Write one sentence: "I am researching [target] for [purpose] using [public sources]." If you can't write that sentence cleanly, stop.

See references/ethics-and-scope.md for full rules and the decline-it list.

Gate 2 — Disambiguation

The single biggest OSINT failure mode is conflating two people (or two companies) with the same name. Before reporting anything about a target:

1. Identify all plausible candidates that match the name/handle.
2. Pick a discriminator — employer, location, age range, domain, industry, photo.
3. Confirm the discriminator from a primary source (LinkedIn, corporate bio, company website, official registry).
4. State which candidate you're tracking and which ones you're explicitly not.

If you can't disambiguate, say so. Don't guess.

See references/disambiguation.md for the full protocol.

Gate 3 — Source Independence

A "fact" repeated by ten content-farm sites that all copied one LinkedIn bio is one source, not ten. Before grading a finding:

• Trace each claim to a primary source (the entity itself, an authoritative registry, a first-hand account).
• Two sources owned by the same parent, or two articles citing each other, count as one.
• Self-published claims (the target's own LinkedIn, bio, website) are useful for what they claim but not independent confirmation of the underlying fact.

Gate 4 — Confidence Grading

Every finding gets a letter grade in the dossier:

• A — Three or more independent sources, including at least one primary or authoritative.
• B — Two independent sources.
• C — One source, or multiple non-independent sources.
• D — Inferred, likely-but-unconfirmed, or single self-published source.

If a finding can only get a D, either drop it or label it as inference.

See references/confidence-grading.md for the rubric and edge cases.

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Routing table

Match the user's intent to a workflow. If unclear, ask.

User intent / phrasing	Workflow
"Research this person", "background check on", "who is X"	workflows/person.md
"Look into this company", "what does X do", "is X legit"	workflows/company.md
"B2B account intel", "is this prospect worth pursuing", "find the buyer at", "what's their stack", "who's their MSP"	workflows/b2b-account.md
"Check this domain/IP", "tech footprint", "what's running on", "subdomains of"	workflows/domain.md
"Due diligence", "investment-grade research", "vet this vendor/partner"	workflows/due-diligence.md

For multi-target investigations (e.g., a person at a company), run the relevant workflows in sequence and merge the outputs.

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Phase model

Pick a depth at the start. Tell the user the depth and the rough budget so they can adjust.

Quick (≤5 min, web_search only)

• 3–5 targeted searches.
• Read 1–2 primary sources.
• Output: 2–3 paragraph summary + 3–5 cited findings + confidence grades.
• Use for: triage, "do I care about this", before-a-meeting prep.

Standard (15–30 min, multi-tool)

• 8–15 targeted searches across general web, LinkedIn, corporate registries.
• Read 5–10 primary sources, including the target's own properties.
• Cross-check at least three claims for source independence.
• Output: full dossier per templates/dossier.md + findings.json sidecar.
• Use for: most B2B account work, prospect research, person research.

Deep (60+ min, all available tools)

• Iterative search with progressive narrowing.
• DNS/WHOIS/cert transparency for domain targets.
• Multiple platforms cross-referenced.
• Historical data (Wayback) where it changes the picture.
• Output: full dossier, findings.json, plus a one-page executive summary.
• Use for: due diligence, investment decisions, big-account work.

If you're going to exceed the budget you stated, stop and tell the user before continuing.

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Output contract

Every Standard or Deep investigation produces two artifacts:

1. <target-slug>-dossier.md — human-readable, follows templates/dossier.md. Section headers, confidence grades on every claim, source URLs collected at the bottom.
2. <target-slug>-findings.json — machine-readable, follows templates/findings.schema.json. Each finding has claim, confidence, sources[], category, extracted_at.

For Quick investigations, the markdown summary is enough. JSON is optional.

Save outputs to a folder named osint/<target-slug>/ in the working directory unless the user specifies otherwise.

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Tool usage

Required minimum: web search + ability to fetch URLs. The skill works with just these.

Strongly helpful (graceful degradation):

• LinkedIn search (via web search operators or scraper)
• WHOIS / DNS / certificate transparency for domain work
• GitHub search (free API tier is plenty)
• Wayback Machine (web.archive.org) for historical
• Apify or Bright Data actors for social platform extraction (if API keys present)

Search operator reference: references/search-operators.md — Google dorks, LinkedIn X-ray, GitHub search syntax, Wayback usage.

API key check (do this once at the start of any Standard or Deep investigation, silently):

[ -n "$GITHUB_TOKEN" ]   && echo "github: yes"   || echo "github: no"
[ -n "$BRAVE_API_KEY" ]  && echo "brave: yes"    || echo "brave: no"
[ -n "$APIFY_TOKEN" ]    && echo "apify: yes"    || echo "apify: no"
[ -n "$SHODAN_API_KEY" ] && echo "shodan: yes"   || echo "shodan: no"

If a key is missing, fall back to web search. Never tell the user "I can't do this" without first trying the fallback.

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Anti-patterns to avoid

• Don't open with the dossier. State the depth, scope, and disambiguation result first; produce the dossier at the end.
• Don't manufacture confidence. A C-grade finding labeled C is useful. A C-grade finding labeled A poisons the rest of the report.
• Don't pad with noise. Five well-sourced findings beat fifty drive-by mentions.
• Don't assume the most prominent match is the right one. The CEO is famous; the IT director you're actually trying to reach is not.
• Don't write speculative biography. If a source doesn't say it, you don't either.
• Don't keep mining after the question is answered. Quick means quick.

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Agentic OS Integration

If the current project root contains an .agent/ directory, this skill MUST participate in the Agentic OS shared-memory model.

At the end of your execution, check for .agent/state/last-run.json. If it exists, append or update the file using its required schema to log your run. Ensure you capture your runtime (agent_id), assigned_skill, a concise description, decision_log, and outcome. Use python3 ~/.agents/skills/heartbeat/scripts/heartbeat.py complete <task_id> ... if completing a task from the queue.

Optional: PAI / OpenCode integration

If running inside PAI or PAI-OpenCode, the following are honored automatically:

• User customizations at ~/.opencode/skills/PAI/USER/SKILLCUSTOMIZATIONS/OSINT/PREFERENCES.md override defaults.
• Apify actors via ~/.opencode/skills/Apify/ are used when present.
• Memory/context loading from ~/.opencode/skills/PAI/SKILL.md runs first if the file exists.

If those paths don't exist, ignore them. The skill is fully functional standalone.

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Quick-start example

User: "Look into Jason Gurash at Avenir Senior Living for me — B2B prospect, infrastructure consulting angle."

Your turn 1 (don't dossier yet):

Setting up a B2B account intel investigation. Routing to workflows/b2b-account.md. Depth: Standard (~20 min). Disambiguation target: Jason Gurash, VP Operations, Avenir Senior Living. I'll confirm that match before reporting any details about him personally. Scope: public sources for sales discovery — corporate site, LinkedIn, news, tech footprint. I'll skip personal-life angles since they're not relevant to the consulting use case. Starting now.

Then run the workflow. Then produce the dossier with grades.