agentskills.io cybersecurity skills taxonomy for AI agents

I came across a GitHub project that ships a large, structured cybersecurity skills dataset intended for AI agents: 817 discrete skills mapped onto 6 frameworks (MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF, and MITRE F3).

What’s useful here is not the raw count, but the normalization: each skill is positioned inside established security taxonomies so an agent can (a) classify what it’s trying to do, and (b) justify actions in framework terms. For a production RAG setup, this is a clean way to turn “security know-how” into retrieval targets with consistent metadata.

A concrete situation I hit in agent work: you want an agent to triage a prompt like “write a detection for suspicious logins” and then choose appropriate next actions. Without a shared skill schema, the model can drift between unrelated concepts (threat hunting, secure config, incident response) with no guardrails. With a mapped taxonomy, you can constrain routing (e.g., which domains to retrieve first) and log outputs against framework categories.

What to look at first: - the landing page / entry point at agentskills.io - the mapping sections showing how each skill aligns to the 6 frameworks - the “security domains” breakdown (29 domains) - the compatibility list indicating it works with Claude Code and multiple coding/agent CLIs (e.g., GitHub Copilot, Codex CLI, Cursor, Gemini CLI)

The repo/license is Apache 2.0, which is a good sign if you’re planning to integrate the taxonomy into your own agent prompts or retrieval pipeline.