agentleFS
Liberty91LTDLiberty91LTD/cti-skills/AGENTS.md

cti-skills

AGENTS.md35 starsChanged 44 days agoMITView on GitHub

You're an agent loading the cti-skills pack. This file tells you what's here, how to use it, and what conventions to follow. It's platform-neutral — the same rules apply in Claude Code, Cursor, Codex, Windsurf, and any other agentic IDE that supports the Agent Skills spec. Cyber Threat Intelligence skills: threat actor profiling, IOC investigation, OSINT methodology, detection engineering (SIGMA/YARA/KQL), intelligence writing, and self-updating knowledge cells on nation-state and cybercrime threats. Not an internal ops tool. A public distribution artifact.…

The file

We split this AGENTS.md into the rules it gives an agent

12 rules. About 0 are common, 4 are distinctive, and 8 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 8

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Not an internal ops tool. A public distribution artifact.

    line 9
  2. Very specific:

    When the user's request doesn't name a specific skill, route it through /cti-orchestrator.

    line 40
  3. Distinctive:

    When you work with sources:

    line 65
  4. Very specific:

    Resolve provenance from data: Use /source-provenance. Do not state where a report originated from memory.

    line 67
  5. Very specific:

    Grade claims, not events, and not outlets: Use /quality-of-information-check and its fixed rubric.

    line 68
  6. Distinctive:

    Do not assert what was not established: No primary the script or platform did not resolve, no corroboration without data, no access type …

    line 69
  7. Very specific:

    Update the row in VERSIONS.md

    line 89
  8. Very specific:

    Run ./validate-skills.sh and python3 tests/provenance/run_tests.py

    line 90
  9. Very specific:

    Don't invent new skills without adding them to VERSIONS.md and passing the validator

    line 95
  10. Very specific:

    Don't hardcode API keys — always read from environment variables (see tools/integrations/<api>.md for names)

    line 96
  11. Very specific:

    Don't write content that depends on Claude-Code-specific subagent syntax if the skill could be useful cross-agent.

    line 97
  12. Distinctive:

    Don't drop TLP / source-rating / confidence from intelligence outputs if the orchestrator is active — it's auto-applied for a reason.

    line 98

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

AGENTS.md — Orientation for AI agents

You're an agent loading the cti-skills pack. This file tells you what's here, how to use it, and what conventions to follow. It's platform-neutral — the same rules apply in Claude Code, Cursor, Codex, Windsurf, and any other agentic IDE that supports the Agent Skills spec.

What this pack is

Cyber Threat Intelligence skills: threat actor profiling, IOC investigation, OSINT methodology, detection engineering (SIGMA/YARA/KQL), intelligence writing, and self-updating knowledge cells on nation-state and cybercrime threats.

Not an internal ops tool. A public distribution artifact. Optimize for adoption and clarity over enforced rigor.

Shape

skills/                 # flat — 78 composable skills
.claude/                # Claude Code specific (agents, settings). Other platforms ignore.
.claude-plugin/         # plugin manifest for Claude Code marketplace install
tools/                  # REGISTRY.md + per-API integration guides + zero-dep CLIs
data/                   # sample IOCs, reports, PIRs — example content, not required
mitre-attack/           # local MITRE ATT&CK Enterprise dataset
VERSIONS.md             # per-skill semver + changelog
tests/                  # offline tests for provenance resolution and grading caps
validate-skills.sh      # frontmatter linter — run before committing

Finding and invoking skills

Skills are self-describing via YAML frontmatter. The description field includes trigger phrases — match user intent against descriptions to decide which skill to use.

---
name: threat-actor-profile
description: Use when the user asks to profile a threat actor, build an actor card, or characterize an adversary group. Produces an actor profile with aliases, TTPs, targeting, attribution confidence, and observed infrastructure.
---

Skills can invoke other skills. An investigation skill like /ip-investigation will chain /lookup-virustotal, /lookup-otx, /lookup-shodan, and then apply rigor skills like /score-source and /apply-tlp. When you're a skill composing others, state the composition explicitly at the top of the skill body: "This skill invokes: X, Y, Z."

The orchestrator pattern

When the user's request doesn't name a specific skill, route it through /cti-orchestrator. That skill:

  1. Parses the request
  2. Routes to the right investigation/analysis skill
  3. Auto-applies rigor skills on the output (/score-source, /apply-tlp, /confidence-language, /likelihood-language)
  4. Returns a formatted, source-rated, confidence-marked product

Direct user invocations like /ach or /iran-cyber-espionage bypass the orchestrator — that's fine.

Conventions — opt-in, not gates

This pack offers tradecraft vocabularies. It does not enforce them. Skills that declare metadata.tradecraft: true in frontmatter will be validated against these conventions by validate-skills.sh; others are free-form.

  • TLP (Traffic Light Protocol) — CLEAR / GREEN / AMBER / AMBER+STRICT / RED. Applied to intelligence products.
  • Admiralty Scale — source reliability A-F + information credibility 1-6. Applied to collected intelligence.
  • MISP confidence — 0-100. Applied to analytical judgments.
  • Probability yardstick — Remote / Unlikely / Even Chance / Likely / Almost Certain with percentage bands. Applied to forward-looking statements.

Each has a dedicated skill (/apply-tlp, /score-source, /confidence-language, /likelihood-language) that the orchestrator auto-invokes.

The one default: graded evidence

Since 2.0, /ach, /threat-assessment and /writing-assessments prefer graded evidence items and run /quality-of-information-check first when handed raw URLs or text. The user can skip it. A run on ungraded evidence is labelled Evidence basis: ungraded, carries no invented grades, and is capped at Moderate confidence, so the reader always knows which kind of product they are holding.

The principle behind it: confidence in a judgment is bounded above by the weakest load-bearing claim, and first-party observation is the top of the scale, not outside it.

When you work with sources:

  • Resolve provenance from data. Use /source-provenance. Do not state where a report originated from memory.
  • Grade claims, not events, and not outlets. Use /quality-of-information-check and its fixed rubric.
  • Do not assert what was not established. No primary the script or platform did not resolve, no corroboration without data, no access type inferred from reputation, no invented confidence language.
  • Carry provenance_basis through (platform-resolved, script-resolved, model-judged) into every product built on the evidence.
  • Enter hits in the user's own telemetry as observations graded direct and established. CVSS and EPSS are reference data, not evidence.
  • Write the evidence grade in words. Access level and claim support, for example "direct, firm". It is not an Admiralty rating: never write it as a letter and a number, and never convert one into the other. Admiralty ratings apply to lookup results and single items through /source-assessment.
  • Never add ratings of organisations to source-types.md. It records what an organisation is, not how much to trust it.

External APIs

Each external threat-intel API has three artifacts:

  1. skills/lookup-<api>/SKILL.md — the invokable skill (what an agent calls)
  2. tools/integrations/<api>.md — auth setup, rate limits, default source-reliability rating
  3. tools/clis/<api>.js — zero-dependency Node CLI that the skill shells out to

This separation means: skills are platform-neutral (just SKILL.md), integration docs are for humans setting up keys, CLIs do the actual HTTP work. No MCP server required.

When you update a skill

  1. Edit the skill body
  2. Bump version in its frontmatter metadata.version
  3. Update the row in VERSIONS.md
  4. Run ./validate-skills.sh and python3 tests/provenance/run_tests.py
  5. Open a PR (see CONTRIBUTING.md)

What not to do

  • Don't invent new skills without adding them to VERSIONS.md and passing the validator
  • Don't hardcode API keys — always read from environment variables (see tools/integrations/<api>.md for names)
  • Don't write content that depends on Claude-Code-specific subagent syntax if the skill could be useful cross-agent. Keep skill bodies neutral; use .claude/ for platform-specific extensions only.
  • Don't drop TLP / source-rating / confidence from intelligence outputs if the orchestrator is active — it's auto-applied for a reason.

Where to read next

  • README.md — install methods and quick-start examples
  • CLAUDE.md — Claude Code-specific orientation
  • CONTRIBUTING.md — how to propose changes
  • tools/REGISTRY.md — catalog of external APIs the pack integrates with
  • VERSIONS.md — what's shipped and what changed

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.