Skip to content

Anthropic Cybersecurity Skills: A Structured Library for AI‑Agent Security Workflows

Original URL: Anthropic Cybersecurity Skills

The repository Anthropic Cybersecurity Skills presents a comprehensive, open‑source collection of 754 cybersecurity skills designed explicitly for AI agents that need to act as security analysts. By adhering to the agentskills.io standard, each skill is packaged with YAML front‑matter, step‑by‑step workflows, and reference material, enabling seamless integration with popular AI coding assistants such as Claude Code, GitHub Copilot, Codex CLI, Cursor, and Gemini CLI. The library spans 26 security domains and maps every skill to five major industry frameworks—MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF—providing unified cross‑framework coverage that no other open‑source skill set currently offers.

Key Features of the Library

  • 754 production‑grade skills organized across 26 domains, ranging from cloud security to ransomware defense.
  • Five‑framework mapping ensures each skill can be referenced against relevant ATT&CK tactics, NIST CSF functions, ATLAS adversarial techniques, D3FEND countermeasures, and AI‑specific risk categories.
  • Agentskills.io compliance with YAML front‑matter for rapid discovery, structured Markdown for execution, and reference files for deep technical context.
  • Multi‑platform support allowing direct consumption by a wide array of AI agents without additional configuration.

Framework Mappings and Coverage

  • MITRE ATT&CK (v18): 14 tactics, >200 techniques covering adversary behavior and TTPs.
  • NIST CSF 2.0: 6 functions (Govern, Identify, Protect, Detect, Respond, Recover) with alignment to 22 categories and 106 subcategories.
  • MITRE ATLAS (v5.4): 16 tactics, 84 techniques focused on AI/ML adversarial threats such as model poisoning and tool‑calling abuse.
  • MITRE D3FEND (v1.3): 7 tactical categories (e.g., Detect, Harden, Deceive) with 267 defensive counter‑measures. - NIST AI RMF (1.0): 4 functions and 72 subcategories that address trustworthy AI development and regulatory compliance, including the GenAI Profile (AI 600‑1).

These mappings allow an AI agent to instantly locate a skill that matches a given threat scenario, follow a vetted procedural workflow, and produce outputs that satisfy multiple compliance checklists simultaneously.

Skill Structure and Anatomy

Each skill resides in a dedicated directory (e.g., skills/performing-memory-forensics-with-volatility3/) and consists of: 1. SKILL.md – Contains YAML front‑matter (name, description, domain, tags, framework identifiers) and a Markdown body that outlines the skill’s purpose and usage.
2. references/ – Holds detailed mappings (standards.md) and deep technical procedures (workflows.md).
3. scripts/ – Optional helper scripts written in Python or PowerShell that automate command execution.
4. assets/ – Template files for checklists, reports, or sample inputs.

The consistent directory layout facilitates automated discovery by AI agents: a quick scan of front‑matter tags identifies relevant skills, while deeper loading of workflow files enables step‑by‑step execution and verification.

Practical Usage Scenarios

  • Memory Forensics – An agent tasked with “Analyze this memory dump for signs of credential theft” can automatically locate skills such as performing-memory-forensics-with-volatility3, hunting-for-credential-dumping-lsass, and analyzing-windows-event-logs-for-credential-access.
  • Threat Hunting – Skills tagged with hypothesis‑driven‑hunt provide hypothesis generation, target selection, query execution, and result validation steps.
  • Incident Response – Playbooks under incident-response guide agents through containment, eradication, and recovery phases, including specific commands for cloud breach scoping across AWS, Azure, and GCP.

Because each skill references the appropriate framework IDs, an AI agent can also output compliance evidence (e.g., “Mapped to NIST CSF Detect‑DE.CM‑01”) automatically for audit trails.

Community, Contribution, and SupportThe project is maintained as a community initiative and is not officially affiliated with Anthropic PBC. Contributions are welcomed through:

  • Adding new skills in under‑represented domains (e.g., deception technology, compliance).
  • Enhancing existing skills with additional framework mappings or updated scripts. - Reporting inaccuracies or broken references via issues.

All pull requests are reviewed for technical correctness and adherence to the agentskills.io standard within 48 hours. A contributor covenant governs community interaction, ensuring a professional and inclusive environment.

The repository also offers a Playground on Casky.ai where users can experiment with live skill execution against real targets, observe AI agents performing threat‑hunting workflows, and test MITRE‑mapped processes without any local setup.

Conclusion

Anthropic Cybersecurity Skills fills a critical gap by transforming unstructured security knowledge into a machine‑readable, modular skill library. Its rigorous mapping to multiple industry frameworks, standardized YAML‑driven architecture, and compatibility with leading AI code assistants empower AI agents to emulate the disciplined workflow of senior security analysts. For organizations seeking to augment their cybersecurity workforce with AI‑driven expertise, this repository provides a scalable, compliant, and openly contributed foundation for building trustworthy, agent‑based security operations.