# Anthropic Cybersecurity Skills: 754 Structured Skills for AI Agents

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This repository offers the largest open-source library of 754 structured cybersecurity skills designed for AI agents. It maps these skills across five industry frameworks, including MITRE ATT&CK and NIST CSF 2.0, enabling AI agents to perform expert-level security analysis and operations. The project aims to empower AI with practitioner playbooks to address the global cybersecurity workforce gap.

GitHub: https://github.com/mukul975/Anthropic-Cybersecurity-Skills
OSRepos URL: https://osrepos.com/repo/mukul975-anthropic-cybersecurity-skills

## Summary

This repository offers the largest open-source library of 754 structured cybersecurity skills designed for AI agents. It maps these skills across five industry frameworks, including MITRE ATT&CK and NIST CSF 2.0, enabling AI agents to perform expert-level security analysis and operations. The project aims to empower AI with practitioner playbooks to address the global cybersecurity workforce gap.

## Topics

- ai-agents
- cybersecurity
- infosec
- mitre-attack
- nist-csf
- devsecops
- python
- security-automation

## Repository Information

Last analyzed by OSRepos: Sun May 24 2026 00:26:46 GMT+0100 (Western European Summer Time)
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## Safety Notice

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## Content

## 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:

bash
# 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:

1.  **Scanning skill frontmatters:** The agent scans all 754 skill frontmatters, identifying relevant skills by matching tags, descriptions, and domains.
2.  **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`.
3.  **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.
4.  **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.

## Links

*   **GitHub Repository:** [https://github.com/mukul975/Anthropic-Cybersecurity-Skills](https://github.com/mukul975/Anthropic-Cybersecurity-Skills){:target="_blank"}
*   **agentskills.io Standard:** [https://agentskills.io](https://agentskills.io){:target="_blank"}
*   **Casky.ai Playground:** [https://casky.ai/?utm_source=github&utm_medium=readme&utm_campaign=cohort_launch#waitlist](https://casky.ai/?utm_source=github&utm_medium=readme&utm_campaign=cohort_launch#waitlist){:target="_blank"}
*   **GARS-2026 Survey:** [https://mahipal.engineer/survey?utm_source=github_repo&utm_medium=readme&utm_campaign=gars2026](https://mahipal.engineer/survey?utm_source=github_repo&utm_medium=readme&utm_campaign=gars2026){:target="_blank"}
*   **License:** [https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/LICENSE](https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/LICENSE){:target="_blank"}