
Give any AI agent the structured decision-making of a senior security analyst — not generic web...

Give any AI agent the structured decision-making of a senior security analyst — not generic web search, but step-by-step playbooks mapped to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF.
Based on mukul975/Anthropic-Cybersecurity-Skills (754 skills · 26 domains · Apache 2.0).
Community project — not affiliated with Anthropic PBC.
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The cybersecurity workforce gap hit 4.8 million unfilled roles globally in 2024 (ISC2). AI agents can help close that gap — but only if they have structured domain knowledge to work from.
Today’s agents can write code and search the web. They typically cannot :
Existing security repos give you wordlists, payloads, or exploit code. None give an AI agent the decision workflow a senior analyst follows: prerequisites, step order, verification, and framework mapping.
Anthropic Cybersecurity Skills fills that gap: 754 skills, each a practitioner playbook in agentskills.io format — YAML frontmatter for discovery, Markdown body for execution, optional references/scripts/assets for depth.



No other open-source skills library maps every skill to all five frameworks. One skill, five compliance checkboxes.

Skill: analyzing-network-traffic-of-malware

Works with any agentskills.io-compatible platform:
npx skills add mukul975/Anthropic-Cybersecurity-Skills
The installer registers skills in your agent’s configured skills directory.

git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
cd Anthropic-Cybersecurity-Skills
Inspect skills/ — each subdirectory is one skill with SKILL.md at the root.

cd guides/anthropic-cybersecurity-skills
chmod +x install-skills.sh verify-install.sh
./install-skills.sh
./verify-install.sh
Default clone path: ~/.cybersec-skills/Anthropic-Cybersecurity-Skills. Override:
export CYBERSEC_SKILLS_DIR=/opt/security-skills/Anthropic-Cybersecurity-Skills
./install-skills.sh

Claude Code — symlink skills to ~/.claude/skills/
Claude Code loads skills from .claude/skills/ (project) or ~/.claude/skills/ (global).
SKILLS_SRC=~/.cybersec-skills/Anthropic-Cybersecurity-Skills/skills
mkdir -p ~/.claude/skills
# Symlink entire library (754 skills — high discovery surface)
ln -sf "${SKILLS_SRC}"/* ~/.claude/skills/
# Or copy a subset — e.g. DFIR only
cp -r "${SKILLS_SRC}"/performing-memory-forensics-with-volatility3 ~/.claude/skills/
cp -r "${SKILLS_SRC}"/hunting-for-credential-dumping-lsass ~/.claude/skills/
mkdir -p .claude/skills
ln -sf ~/.cybersec-skills/Anthropic-Cybersecurity-Skills/skills/* .claude/skills/
Start a session and ask:
Use the performing-memory-forensics-with-volatility3 skill. List prerequisites and the first three Workflow steps only.
Claude should read SKILL.md and cite structured sections — not invent generic Volatility commands.
See also: Claude Code .claude/ tutorial.

Cursor — npx or manual symlink to ~/.cursor/skills/
Cursor discovers skills listed in agent configuration and from ~/.cursor/skills/ (user skills).
npx skills add mukul975/Anthropic-Cybersecurity-Skills
Follow Cursor-specific prompts if the installer detects your environment.
mkdir -p ~/.cursor/skills
ln -sf ~/.cybersec-skills/Anthropic-Cybersecurity-Skills/skills/* ~/.cursor/skills/
Add to .cursor/rules/ or project instructions:
For security investigations, prefer skills from Anthropic Cybersecurity Skills.
Scan skill frontmatter by tags (dfir, threat-hunting, cloud-security) before loading full SKILL.md.
Always complete the Verification section before closing an investigation step.
Open Agent mode and prompt:
I have a Windows memory dump. Which cybersecurity skills apply? Load the best match and show Prerequisites.

Copilot + Codex CLI — install skills and invoke by name
Both support agentskills.io when configured with a skills path.
npx skills add mukul975/Anthropic-Cybersecurity-Skills
codex # or your configured entrypoint
Codex reads frontmatter for routing; load full skills for multi-step IR workflows.

Gemini CLI — npx install and skill invocation
Compatible without custom forks:

Gemini CLI: install skills via npx skills add, then invoke by skill name in prompts.
LangChain / CrewAI: mount skills/<name>/SKILL.md as tool description or system prompt segment; use frontmatter tags for retrieval routing.
MCP agents: expose skill search as an MCP resource listing frontmatter; fetch full SKILL.md on match.

Hermes — copy skills into ~/.hermes/skills/
Hermes uses ~/.hermes/skills/ (same agentskills.io layout).
git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/cybersec-skills
cp -r /tmp/cybersec-skills/skills/* ~/.hermes/skills/
hermes skills list | head
For SOC automation, combine with Hermes cron/Curator so frequently used skills stay prioritized. See Awesome Hermes Agent tutorial.
Example Hermes prompt:
Run a hypothesis-driven hunt for Kerberoasting using the threat hunting skills. Map hits to ATT&CK T1558.003.

Every skill follows a consistent directory structure:
skills/performing-memory-forensics-with-volatility3/
├── SKILL.md ← Definition (YAML + Markdown)
├── references/
│ ├── standards.md ← Framework mappings
│ └── workflows.md ← Deep technical reference
├── scripts/
│ └── process.py ← Helper scripts
└── assets/
└── template.md ← Report templates
---
name: performing-memory-forensics-with-volatility3
description: >-
Analyze memory dumps to extract running processes, network connections,
injected code, and malware artifacts using the Volatility3 framework.
domain: cybersecurity
subdomain: digital-forensics
tags: [forensics, memory-analysis, volatility3, incident-response, dfir]
atlas_techniques: [AML.T0047]
d3fend_techniques: [D3-MA, D3-PSMD]
nist_ai_rmf: [MEASURE-2.6]
nist_csf: [DE.CM-01, RS.AN-03]
version: "1.2"
author: mukul975
license: Apache-2.0
---


User prompt: “Analyze this memory dump for signs of credential theft.”
Agent internal process:
Without skills, the agent guesses commands and skips steps. With skills, it follows the same playbook a senior DFIR analyst would use.
Scenario: IR ticket — suspected Mimikatz on a Windows server. You have a .raw memory image.
Prompt:
Authorized DFIR on image server01.raw. Find skills for memory forensics and credential dumping. List prerequisites.
Expected skills: memory forensics + LSASS hunting + Windows event logs.
Agent should verify from SKILL.md:
Typical workflow order (from skills):
Use skill assets/template.md if present; include framework mappings from references/standards.md.

Scenario: Hunt for Kerberoasting in Enterprise SIEM.
Service accounts may be targeted via Kerberoasting (T1558.003) in the last 30 days.
Tags: threat-hunting, kerberos, sigma, splunk or sentinel.
Agent loads hunting skill → Workflow:
Scenario: Credentials leaked; unknown activity in AWS, Azure, and GCP.

Agent workflow:

754/754 skills mapped. Validated with official mitreattack-python — no revoked or deprecated IDs.
v19.1 change: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112).

Map skill outputs to Govern, Identify, Protect, Detect, Respond, Recover for audit trails. Example: memory forensics → Detect (DE.CM), Respond (RS.AN).
Use when the incident involves ML models — poisoning, evasion, model theft. Frontmatter field: atlas_techniques.
Pair offensive findings with defensive countermeasures — e.g. D3-NTA for network traffic analysis skills.
For AI governance — document which agent skills were used, human-in-the-loop checkpoints, and measurement (MEASURE-* subcategories).
See Framework mappings for crosswalk tables and reporting templates.
Hands-on exercises without local install:
→ Launch Playground on Casky.ai
Global Agentic AI Readiness Survey (SRH Berlin) — measures readiness for MCP, tool calling, and governance.
Improve existing skills: framework mappings, fixed commands, new scripts/templates.
Report issues: inaccurate procedures or broken scripts → GitHub Issues.
Project follows Contributor Covenant.
These skills describe ** offensive and defensive techniques**. Use only:
AI agents can execute commands quickly — mis-scoped prompts cause real damage. Always:
Upstream Security Policy: responsible disclosure, 48-hour acknowledgment.

Run ./verify-install.sh after every pull.
@software{anthropic_cybersecurity_skills,
author = {Jangra, Mahipal},
title = {Anthropic Cybersecurity Skills},
year = {2026},
url = {https://github.com/mukul975/Anthropic-Cybersecurity-Skills},
license = {Apache-2.0},
note = {754 structured cybersecurity skills for AI agents,
mapped to MITRE ATT\&CK, NIST CSF 2.0, MITRE ATLAS,
MITRE D3FEND, and NIST AI RMF}
}
Apache License 2.0 — use, modify, and distribute in personal and commercial projects.
The cybersecurity skills gap is not going to close with generic chatbots alone. Analysts do not win investigations because an LLM can search the web — they win because they know which playbook to run, in what order, and how to verify the result before closing the ticket.
Anthropic Cybersecurity Skills (community-built, Apache 2.0) gives AI agents that same structure: 754 skills across 26 domains, each mapped to MITRE ATT&CK, NIST CSF, ATLAS, D3FEND, and NIST AI RMF. The agentskills.io format makes it practical — scan lightweight frontmatter first, load full workflows only when the incident demands it.
You do not need a custom fork or a new agent runtime. One install command works across Cursor, Claude Code, Copilot, Codex CLI, Gemini CLI, and Hermes. Point your agent at the library, name the skill in your prompt, and require the Verification step before it reports done.
Start here:
npx skills add mukul975/Anthropic-Cybersecurity-Skills
Then walk through the tutorial: inspect a real SKILL.md, run the credential-dump walkthrough, and pick skills by domain or ATT&CK tactic. Use them only on authorized systems — these are practitioner playbooks, not toys.
If this helps your SOC or red-team workflow, star the upstream repo and consider contributing a skill in an underrepresented domain like Deception Technology or Compliance & Governance. The library grows on community PRs — and the agents using it get sharper with every one.
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