mukul975/Anthropic-Cybersecurity-Skills

What it solves

AI agents often lack the structured, practitioner-level domain knowledge required to perform complex cybersecurity tasks. While they can write code or search the web, they typically lack the specific playbooks, decision-making workflows, and verification steps that a senior security analyst uses. This project provides a structured knowledge base to bridge that gap, turning generic LLMs into capable security analysts.

How it works

The project is a library of 818 structured cybersecurity skills following the agentskills.io open standard. Each skill is designed for "progressive disclosure," allowing an agent to efficiently discover and execute tasks:

1. Scanning: The agent scans lightweight YAML frontmatter (~30 tokens) to identify relevant skills based on tags, descriptions, and domains.
2. Loading: The agent loads the full Markdown body (500-2,000 tokens) for the top matches.
3. Execution: The agent follows a step-by-step "Workflow" section containing specific commands and decision points.
4. Verification: The agent uses a dedicated "Verification" section to confirm the task was completed successfully.

Who it’s for

Developers building AI agents for security operations, penetration testers, incident responders, and security researchers who want to equip their agents with expert-level guidance across 34 security domains.

Highlights

• Massive Scope: 818 production-grade skills across 34 domains, including Cloud Security, SOC Operations, Digital Forensics, and AI Security.
• Industry Alignment: Mapped to six major frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, NIST AI RMF, and the MITRE Fight Fraud Framework (F3).
• Agent-Native Design: Built specifically for AI discovery and execution rather than human reading, using a structured directory system (SKILL.md, references, scripts, and assets).
• Broad Compatibility: Works with Claude Code, GitHub Copilot, Cursor, Gemini CLI, and other agentskills.io-compatible platforms.