Anthropic Cybersecurity Skills: 754 Structured Skills for AI Agents

Introduction

The Anthropic-Cybersecurity-Skills repository provides an extensive, open-source library of 754 structured cybersecurity skills specifically designed for AI agents. This project equips AI with the practical knowledge of a senior security analyst, covering 26 security domains and mapping to five critical industry frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF. Built on the agentskills.io open standard, these skills are compatible with over 20 AI platforms, including Claude Code, GitHub Copilot, and Gemini CLI, transforming generic LLMs into capable security professionals.

Installation

Getting started with Anthropic-Cybersecurity-Skills is straightforward, offering both npx and git clone options:

# Option 1: npx (recommended)
npx skills add mukul975/Anthropic-Cybersecurity-Skills

# Option 2: Git clone
git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
cd Anthropic-Cybersecurity-Skills

These skills integrate immediately with agentskills.io-compatible platforms, allowing AI agents to leverage expert-level guidance for security investigations.

Examples

AI agents utilize these skills through a progressive disclosure architecture. Each skill's frontmatter, costing approximately 30 tokens to scan, allows agents to quickly identify relevant skills without exceeding context windows. Once a skill is selected, the full workflow, costing 500-2,000 tokens, is loaded for detailed execution. This enables efficient and targeted responses to complex security prompts.

Consider a user prompt like: "Analyze this memory dump for signs of credential theft."

An agent's internal process would involve:

Scanning skill frontmatters: The agent scans all 754 skill frontmatters, identifying relevant skills by matching tags, descriptions, and domains.
Loading top matches: It then loads the top 3 matches, such as performing-memory-forensics-with-volatility3, hunting-for-credential-dumping-lsass, and analyzing-windows-event-logs-for-credential-access.
Executing the workflow: The agent executes the structured workflow step-by-step, running Volatility3 plugins, checking LSASS access patterns, and correlating with event log evidence.
Validating results: Finally, it validates the results using the verification section, confirming Indicators of Compromise (IOCs) and mapping findings to MITRE ATT&CK T1003 (Credential Dumping).

This structured approach ensures AI agents follow the same comprehensive playbook a senior DFIR analyst would use, preventing guesswork and missed critical steps.

Why Use This Project

The cybersecurity workforce gap reached 4.8 million unfilled roles globally in 2024. AI agents offer a powerful solution, but they require structured domain knowledge to be effective. While existing security tool repositories provide scripts or payloads, they lack the structured decision-making workflows that define a senior analyst's approach.

Anthropic-Cybersecurity-Skills fills this gap by providing an AI-native knowledge base built on the agentskills.io standard. Each skill includes YAML frontmatter for rapid discovery, structured Markdown for step-by-step execution, and reference files for deep technical context. This project is not a collection of scripts, but an operational knowledge base encoding real practitioner workflows, ensuring AI agents can perform expert-level security tasks with precision and accuracy. Its unique cross-framework mapping further enhances its value, providing unified coverage across MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF.