How to use Anthropic Cybersecurity Skills: add a security analysis skill library to AI agents

mukul975/Anthropic-Cybersecurity-Skills is a cybersecurity skill library for AI agents. The README says it includes 754 structured cybersecurity skills and maps them to frameworks such as MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND, and NIST AI RMF.

Project repository:

https://github.com/mukul975/Anthropic-Cybersecurity-Skills

Installation

The recommended method is to add it with npx:

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npx skills add mukul975/Anthropic-Cybersecurity-Skills

You can also clone the repository directly:

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git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
cd Anthropic-Cybersecurity-Skills

What the Skill Directory Looks Like

The README gives this structure example:

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skills/performing-memory-forensics-with-volatility3/
├── SKILL.md
├── references/
│   ├── standards.md
│   └── workflows.md
├── scripts/
│   └── process.py
└── assets/
    └── template.md

A skill usually includes:

YAML frontmatter.
Usage conditions.
Prerequisites.
Step-by-step workflow.
Validation methods.
References and scripts.

Suitable Tasks

It is suitable for defensive and analytical tasks, such as:

Memory forensics.
Windows event log analysis.
Investigation of credential access behavior.
Security alert triage.
Threat modeling and framework mapping.

The README example asks an agent to analyze a memory dump. The agent first scans skill frontmatter, then loads the most relevant skills and follows the workflow.

Usage Boundaries

Security skill libraries like this should only be used in authorized environments. Good use cases include:

Your own lab environment.
Authorized internal enterprise assessments.
Blue-team analysis, forensics, and compliance work.
Learning security frameworks such as MITRE and NIST.

Do not use it against unauthorized targets. After an AI agent gains a skill library, its actions become more systematic. That makes permission boundaries, logging, and human review even more important.