mukul975/Anthropic-Cybersecurity-Skills

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⚠️ Community Project — This is an independent, community-created project. Not affiliated with Anthropic PBC.

🔐 Authorized & lawful use only. This library includes offensive and dual-use techniques (e.g. red-team C2, phishing simulation, exploitation) intended for authorized penetration testing, security research, defense, and education. Only use them against systems you own or have explicit written permission to test, and comply with all applicable laws and rules of engagement. You are solely responsible for how you use these skills. See SECURITY.md and CODE_OF_CONDUCT.md.

Give any AI agent the security skills of a senior analyst

A junior analyst knows which Volatility3 plugin to run on a suspicious memory dump, which Sigma rules catch Kerberoasting, and how to scope a cloud breach across three providers. Your AI agent doesn't — unless you give it these skills.

This repo contains 817 structured cybersecurity skills spanning 29 security domains, each following the agentskills.io open standard. The library maps across six industry frameworks — MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, NIST AI RMF, and the MITRE Fight Fraud Framework (F3) — with each skill mapped to the frameworks relevant to its type (a forensics skill carries ATT&CK + CSF; an AI-security skill adds ATLAS and AI RMF). Clone it, point your agent at it, and your next security investigation gets expert-level guidance in seconds.

Six frameworks, one skill library

Each skill maps to the frameworks that fit its subject — ATT&CK and NIST CSF are near-universal, while ATLAS, AI RMF, D3FEND, and F3 apply where they're relevant. Framework coverage across the 817 skills: MITRE ATT&CK 805 · NIST CSF 2.0 804 · MITRE D3FEND 139 · NIST AI RMF 97 · MITRE F3 94 · MITRE ATLAS 93.

Example — each skill maps only to the frameworks relevant to it (one may hit all six, another just a couple):

🆕 MITRE Fight Fraud Framework (F3) — 94 fraud-relevant skills

The MITRE Fight Fraud Framework (F3) was released April 9, 2026 by MITRE's Center for Threat-Informed Defense (CTID), co-developed with JPMorganChase, Citigroup, Lloyds Banking Group, Standard Chartered, CrowdStrike, Verizon Business, FS-ISAC, and others. It is an ATT&CK-compatible TTP catalog for cyber-enabled financial fraud — filling the gap ATT&CK leaves after initial compromise.

F3 v1.1 adds two fraud-specific tactics that ATT&CK does not enumerate:

• Positioning (FA0001) — actions taken after access to collect/manipulate data and prepare the fraud (synthetic-identity seeding, account warming, beneficiary setup, SIM-swap pre-positioning, banking-session hijack).
• Monetization (FA0002) — converting stolen assets into usable funds (money-mule layering, APP fraud, crypto off-ramping, card cash-out, refund/chargeback abuse).

Fraud-specific techniques use F1XXX IDs (e.g. F1005.003 Add Beneficiary, F1025.003 Wire Transfer, F1007 Adversary-in-the-Browser); reused ATT&CK techniques keep their T1XXX IDs. Mappings live in each skill's mitre_f3: frontmatter block — all 123 F3 v1.1 technique IDs were verified against the upstream STIX bundle. See docs/mitre-f3-mapping.md for the schema.

MITRE ATT&CK v19.1 — 805/817 skills mapped

Every skill carries a mitre_attack frontmatter list validated against MITRE ATT&CK v19.1 (the latest release) using the official mitreattack-python library — 290 distinct techniques and sub-techniques (146 base + 144 sub) across Enterprise, ICS, and Mobile. Zero revoked or deprecated IDs. v19.1's restructured Defense Evasion (now split into Stealth and Defense Impairment) is reflected below.

Quick start

# 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

Works immediately with Claude Code, GitHub Copilot, OpenAI Codex CLI, Cursor, Gemini CLI, and any agentskills.io-compatible platform.

🌍 GARS-2026 — Global Agentic AI Readiness Survey

I'm running a global academic study measuring how ready security professionals, developers, and enterprise teams actually are for agentic AI — MCP servers, tool calling, governance, and human-in-the-loop workflows.

If you use this repo, your response would be a genuinely valuable data point.

📋 Take the survey (10 min): Survey Link

• 60 questions · Anonymous · Supervised by SRH Berlin
• You get 50 Casky Tokens for early access to casky.ai
• Results published open access under CC-BY 4.0

🚀 Try it on the Playground

Experience Casky.ai hands-on — no setup required.

→ Launch Playground on Casky.ai

The playground lets you:

• Run live cybersecurity skill exercises against real targets
• See AI agents execute structured skills in real time
• Explore MITRE ATT&CK mapped workflows interactively
• Test threat hunting, DFIR, and penetration testing scenarios

No installation. No configuration. Just open and start.

Why this exists

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, but they lack the practitioner playbooks that turn a generic LLM into a capable security analyst.

Existing security tool repos give you wordlists, payloads, or exploit code. None of them give an AI agent the structured decision-making workflow a senior analyst follows: when to use each technique, what prerequisites to check, how to execute step-by-step, and how to verify results. That is the gap this project fills.

Anthropic Cybersecurity Skills is not a collection of scripts or checklists. It is an AI-native knowledge base built from the ground up for the agentskills.io standard — YAML frontmatter for sub-second discovery, structured Markdown for step-by-step execution, and reference files for deep technical context. Every skill encodes real practitioner workflows, not generated summaries.

What's inside — 29 security domains

How AI agents use these skills

Each skill costs ~30 tokens to scan (frontmatter only) and 500–2,000 tokens to fully load (complete workflow). This progressive disclosure architecture lets agents search all 817 skills in a single pass without blowing context windows.

User prompt: "Analyze this memory dump for signs of credential theft"

Agent's internal process:

  1. Scans 817 skill frontmatters (~30 tokens each)
     → identifies 12 relevant skills by matching tags, description, domain

  2. Loads top 3 matches:
     • performing-memory-forensics-with-volatility3
     • hunting-for-credential-dumping-lsass
     • analyzing-windows-event-logs-for-credential-access

  3. Executes the structured Workflow section step-by-step
     → runs Volatility3 plugins, checks LSASS access patterns,
        correlates with event log evidence

  4. Validates results using the Verification section
     → confirms IOCs, maps findings to ATT&CK T1003 (Credential Dumping)

Without these skills, the agent guesses at tool commands and misses critical steps. With them, it follows the same playbook a senior DFIR analyst would use.

Skill anatomy

Every skill follows a consistent directory structure:

skills/performing-memory-forensics-with-volatility3/
├── SKILL.md              ← Skill definition (YAML frontmatter + Markdown body)
├── references/
│   ├── standards.md      ← MITRE ATT&CK, ATLAS, D3FEND, NIST mappings
│   └── workflows.md      ← Deep technical procedure reference
├── scripts/
│   └── process.py        ← Working helper scripts
└── assets/
    └── template.md       ← Filled-in checklists and report templates

YAML frontmatter (real example)

---
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
---

Markdown body sections

## When to Use
Trigger conditions — when should an AI agent activate this skill?

## Prerequisites
Required tools, access levels, and environment setup.

## Workflow
Step-by-step execution guide with specific commands and decision points.

## Verification
How to confirm the skill was executed successfully.

Frontmatter fields: name (kebab-case, 1–64 chars), description (keyword-rich for agent discovery), domain, subdomain, tags, atlas_techniques (MITRE ATLAS IDs), d3fend_techniques (MITRE D3FEND IDs), nist_ai_rmf (NIST AI RMF references), nist_csf (NIST CSF 2.0 categories). MITRE ATT&CK technique mappings are documented in each skill's references/standards.md file and in the ATT&CK Navigator layer included with releases.

📊 MITRE ATT&CK Enterprise coverage — all 15 tactics

 

Tactic	ID	Coverage	Key skills
Reconnaissance	TA0043	Strong	OSINT, subdomain enumeration, DNS recon
Resource Development	TA0042	Moderate	Phishing infrastructure, C2 setup detection
Initial Access	TA0001	Strong	Phishing simulation, exploit detection, forced browsing
Execution	TA0002	Strong	PowerShell analysis, fileless malware, script block logging
Persistence	TA0003	Strong	Scheduled tasks, registry, service accounts, LOTL
Privilege Escalation	TA0004	Strong	Kerberoasting, AD attacks, cloud privilege escalation
Stealth	TA0005	Strong	Obfuscation, rootkit analysis, evasion detection
Defense Impairment	TA0112	Moderate	Impair Defenses (T1562), log/indicator removal, EDR tampering
Credential Access	TA0006	Strong	Mimikatz detection, pass-the-hash, credential dumping
Discovery	TA0007	Moderate	BloodHound, AD enumeration, network scanning
Lateral Movement	TA0008	Strong	SMB exploits, lateral movement detection with Splunk
Collection	TA0009	Moderate	Email forensics, data staging detection
Command and Control	TA0011	Strong	C2 beaconing, DNS tunneling, Cobalt Strike analysis
Exfiltration	TA0010	Strong	DNS exfiltration, DLP controls, data loss detection
Impact	TA0040	Strong	Ransomware defense, encryption analysis, recovery

An ATT&CK Navigator layer file is included in the v1.0.0 release assets for visual coverage mapping.

Note: ATT&CK v19 lands April 28, 2026 — splitting Defense Evasion (TA0005) into two new tactics: Stealth and Impair Defenses. Skill mappings will be updated in a forthcoming release.

📊 NIST CSF 2.0 alignment — all 6 functions

 

Function	Skills	Examples
Govern (GV)	30+	Risk strategy, policy frameworks, roles & responsibilities
Identify (ID)	120+	Asset discovery, threat landscape assessment, risk analysis
Protect (PR)	150+	IAM hardening, WAF rules, zero trust, encryption
Detect (DE)	200+	Threat hunting, SIEM correlation, anomaly detection
Respond (RS)	160+	Incident response, forensics, breach containment
Recover (RC)	40+	Ransomware recovery, BCP, disaster recovery

NIST CSF 2.0 (February 2024) added the Govern function and expanded scope from critical infrastructure to all organizations. Skill mappings align to all 22 categories and reference 106 subcategories.

📊 Framework deep dive — ATLAS, D3FEND, AI RMF

 

MITRE ATLAS 2026.07 — AI/ML adversarial threats

ATLAS maps adversarial tactics, techniques, and case studies specific to AI and machine learning systems. Release 2026.07 covers 101 techniques and 77 sub-techniques including agentic AI attack vectors added in late 2025: AI agent context poisoning, tool invocation abuse, MCP server compromises, and malicious agent deployment. Skills mapped to ATLAS help agents identify and defend against threats to ML pipelines, model weights, inference APIs, and autonomous workflows.

MITRE D3FEND v1.4.0 — Defensive countermeasures

D3FEND is an NSA-funded knowledge graph of 270 defensive techniques organized across 7 tactical categories: Model, Harden, Detect, Isolate, Deceive, Evict, and Restore. Built on OWL 2 ontology, it uses a shared Digital Artifact layer to bidirectionally map defensive countermeasures to ATT&CK offensive techniques. Skills tagged with D3FEND identifiers let agents recommend specific countermeasures for detected threats.

NIST AI RMF 1.0 + GenAI Profile (AI 600-1)

The AI Risk Management Framework defines 4 core functions — Govern, Map, Measure, Manage — with 72 subcategories for trustworthy AI development. The GenAI Profile (AI 600-1, July 2024) adds 12 risk categories specific to generative AI, from confabulation and data privacy to prompt injection and supply chain risks. Colorado's AI Act (effective February 2026) provides a legal safe harbor for organizations complying with NIST AI RMF, making these mappings directly relevant to regulatory compliance.

Compatible platforms

AI code assistants Claude Code (Anthropic) · GitHub Copilot (Microsoft) · Cursor · Windsurf · Cline · Aider · Continue · Roo Code · Amazon Q Developer · Tabnine · Sourcegraph Cody · JetBrains AI

CLI agents OpenAI Codex CLI · Gemini CLI (Google)

Autonomous agents Devin · Replit Agent · SWE-agent · OpenHands

Agent frameworks & SDKs LangChain · CrewAI · AutoGen · Semantic Kernel · Haystack · Vercel AI SDK · Any MCP-compatible agent

All platforms that support the agentskills.io standard can load these skills with zero configuration.

What people are saying

"A database of real, organized security skills that any AI agent can plug into and use. Not tutorials. Not blog posts." — Hasan Toor (@hasantoxr), AI/tech creator

"This is not a random collection of security scripts. It's a structured operational knowledge base designed for AI-driven security workflows." — fazal-sec, Medium

Featured in

Star history

Releases

Skills have continued to grow on main since v1.0.0 — the library now contains 817 skills with 6-framework mapping (MITRE ATLAS, D3FEND, NIST AI RMF, and the MITRE Fight Fraud Framework added post-release). Check Releases for the latest tagged version.

Contributing

This project grows through community contributions. Here is how to get involved:

Add a new skill — Domains like Deception Technology (2 skills) and Compliance & Governance (5 skills) need the most help. Follow the template in CONTRIBUTING.md and submit a PR with the title Add skill: your-skill-name.

Improve existing skills — Add framework mappings, fix workflows, update tool references, or contribute scripts and templates.

Report issues — Found an inaccurate procedure or broken script? Open an issue.

Every PR is reviewed for technical accuracy and agentskills.io standard compliance within 48 hours. Check good first issues for a starting point.

This project follows the Contributor Covenant. By participating, you agree to uphold this code.

🙏 Thanks to our contributors

This library is built by the community. Thank you to everyone who has contributed:

_{Ordered by contribution count · see the full contributor graph}

Community

💬 Discussions — Questions, ideas, and roadmap conversations 🐛 Issues — Bug reports and feature requests 🔒 Security Policy — Responsible disclosure process (48-hour acknowledgment)

Citation

If you use this project in research or publications:

@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         = {817 structured cybersecurity skills for AI agents,
                  mapped to MITRE ATT\&CK, NIST CSF 2.0, MITRE ATLAS,
                  MITRE D3FEND, and NIST AI RMF}
}

License

This project is licensed under the Apache License 2.0. You are free to use, modify, and distribute these skills in both personal and commercial projects.

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