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Introduction:
The global cybersecurity workforce shortage reached 4.8 million unfilled positions in 2024, and AI agents are now capable of performing vulnerability analysis, log forensics, and even portions of red-team penetration testing. The convergence of these two trends has created an urgent need: how do we give AI agents the structured, framework-aligned security knowledge that senior practitioners carry in their heads? Anthropic’s Cybersecurity Skills Library—an open-source community project containing 817 production-grade cybersecurity skills across 29 security domains—offers a compelling answer by transforming AI agents from generic chatbots into specialized security assistants that can reason across MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, NIST AI RMF, and the MITRE Fight Fraud Framework (F3).
Learning Objectives:
- Understand the architecture and progressive disclosure design of the Anthropic Cybersecurity Skills Library
- Learn how to install and configure the skills library across 26+ AI platforms including Claude Code, Cursor, and GitHub Copilot
- Master the six-framework mapping approach and apply it to real-world security workflows
1. What Is the Anthropic Cybersecurity Skills Library?
This is not a collection of random scripts or exploit payloads. It is a structured knowledge base where every skill is a self-contained directory following the agentskills.io open standard. Each skill includes YAML frontmatter for agent discovery and a structured Markdown body for execution, with references, workflows, scripts, and assets organized for progressive disclosure.
The library spans 29 security domains, including Cloud Security (66 skills), Threat Hunting (58 skills), Threat Intelligence (52 skills), Network Security (43 skills), Web Application Security (42 skills), Digital Forensics (41 skills), Malware Analysis (39 skills), Identity & Access Management (37 skills), SOC Operations (35 skills), Red Teaming (33 skills), and Container Security.
Installation & Setup:
Method 1: npx (recommended) npx skills add mukul975/Anthropic-Cybersecurity-Skills Method 2: Git clone git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
Copy the repository to your `./claude/skills` directory for Claude Code auto-loading, or point your AI agent of choice to the skills directory.
2. Six Frameworks, One Skill Library—The Technical Breakdown
No other open-source skills library maps every skill to all six frameworks simultaneously. Here is the complete framework coverage:
| Framework | Version | Coverage |
|–||-|
| MITRE ATT&CK | v19.1 | 15 tactics, 286 techniques |
| NIST CSF 2.0 | 2.0 | 6 functions, 22 categories |
| MITRE ATLAS | v5.4 | 16 tactics, 84 techniques |
| MITRE D3FEND | v1.3 | 7 categories, 267 techniques |
| NIST AI RMF | 1.0 | 4 functions, 72 subcategories |
| MITRE F3 | v1.1 | 8 tactics, 123 techniques |
What each framework answers:
- ATT&CK: What technique is the adversary using?
- NIST CSF 2.0: Which risk management function does this skill address?
- MITRE ATLAS: If the target is an AI/ML system, which adversarial technique applies?
- D3FEND: What defensive countermeasures stop the attack?
- NIST AI RMF: Where does this fit in the AI risk lifecycle?
- F3: How does this relate to cyber-enabled financial fraud?
Example skill mapping (detecting prompt injection attacks):
name: detecting-prompt-injection-attacks frameworks: mitre-attack: [T1059.001, T1078] nist-csf: [DE.CM-01, DE.AE-02] mitre-atlas: [AML.T0017, AML.T0051] mitre-d3fend: [D3-IDA, D3-ODA] nist-ai-rmf: [MEASURE-2.7, GOVERN-6.1]
3. Progressive Disclosure: The Efficiency Engine
The library is built around progressive disclosure—a design principle that balances comprehensiveness with token efficiency. Irrelevant skills cost approximately 30 tokens to scan, while relevant skills provide complete expert-level guidance at 500–2,000 tokens. This means your AI agent can rapidly scan all 817 skills without exhausting context windows, then dive deep only into the skills that match the task at hand.
Skill directory structure:
performing-memory-forensics-with-volatility3/ ├── SKILL.md YAML frontmatter + step-by-step workflow ├── references/ │ ├── standards.md Framework mappings │ └── workflows.md Deep technical procedures ├── scripts/ │ └── process.py Functional helper scripts └── assets/ └── template.md Report templates
4. Practical Walkthrough: Memory Forensics with Volatility3
The skill `performing-memory-forensics-with-volatility3` demonstrates how the library operationalizes complex security tasks. When an agent accesses this skill, it receives:
- Framework context: The skill maps to MITRE ATT&CK T1071 (command and control), NIST CSF DE.CM (detection), and D3FEND D3-1TA (network traffic analysis)
-
Step-by-step workflow: Which Volatility3 plugin to run on a suspicious memory dump
-
Verification steps: How to validate findings and eliminate false positives
4. Reporting guidance: Templates for documenting forensic findings
Linux command example for memory acquisition:
Acquire memory image using LiME sudo insmod lime.ko "path=/tmp/memory.lime format=lime" Analyze with Volatility3 vol -f /tmp/memory.lime windows.info vol -f /tmp/memory.lime windows.pslist vol -f /tmp/memory.lime windows.malfind vol -f /tmp/memory.lime windows.cmdline
Windows PowerShell example for memory acquisition:
Using Magnet RAM Capture .\MagnetRAMCapture.exe /accepteula /quiet /output C:\forensics\ Using DumpIt .\DumpIt.exe /accepteula /output C:\forensics\memory.raw
5. Practical Walkthrough: Hypothesis-Driven Threat Hunting
With 58 skills dedicated to threat hunting, the library enables AI agents to conduct assumption-driven hunting, Living Off The Land (LOTL) detection, and EVTX log hunting.
Sigma rule example for detecting Kerberoasting:
title: Kerberoasting Activity id: 12345678-1234-1234-1234-123456789012 status: experimental description: Detects Kerberoasting attacks using event ID 4769 references: - https://attack.mitre.org/techniques/T1558/003/ logsource: product: windows service: security detection: selection: EventID: 4769 TicketOptions: '0x40810000' TicketEncryptionType: '0x17' condition: selection level: high tags: - attack.t1558.003
KQL query for Azure Sentinel threat hunting:
// Detect anomalous Kerberos ticket requests SecurityEvent | where EventID == 4769 | where TicketOptions == "0x40810000" | summarize count() by AccountName, bin(TimeGenerated, 1h) | where count_ > 10
6. Cloud Security: 66 Skills for Multi-Cloud Defense
Cloud Security is the largest domain with 66 skills covering AWS, Azure, and GCP security hardening, CSPM, cloud attack simulation, and cloud forensics. The skills structure CIS Benchmark checks so AI agents can batch-scan, generate reports, and automatically remediate misconfigurations.
AWS CLI command for CIS benchmark scanning:
Install Scout Suite pip install scoutsuite Run Scout Suite against AWS account scout aws --profile default Generate HTML report scout aws --profile default --report-dir ./scout-report
Azure CLI command for security posture assessment:
Run Azure Security Center assessment az security assessment-metadata list Check secure score az security secure-score-controls list Export compliance data az security compliance list --export
GCP command for security health scan:
Run Forseti Security scanner forseti scanner run Export findings forseti model export --model_id <model-id> --output_path ./findings.json
7. Red Teaming: 33 Skills for Offensive Security
With 33 skills covering ADCS/Certipy, BloodHound CE, Sliver/Havoc C2, and NTLM relay, the library enables AI agents to automatically select attack paths based on target environments.
BloodHound CE collection command:
Collect Active Directory data
SharpHound.exe -c All --DomainController <DC-IP> --OutputDirectory ./output
Import to BloodHound
bloodhound --import ./output/.json
Run custom Cypher query for high-privilege paths
MATCH p=(n:User)-[r:MemberOf|HasSession|AdminTo1..5]->(m:Group {name:'DOMAIN ADMINS'})
RETURN p
Sliver C2 deployment:
Start Sliver server sliver-server Generate implant generate --mtls <C2-IP> --os windows --save ./implant.exe Start listener mtls Interact with session sessions -i <session-id>
What Undercode Say:
- Knowledge is the differentiator: AI agents don’t need more compute power—they need structured, framework-aligned knowledge. The 817 skills library proves that the future of AI in cybersecurity is about reasoning across established security practices, not just answering questions faster.
-
Democratization of expertise: By mapping every skill to six frameworks simultaneously, the library gives junior analysts and AI agents the same structured decision-making that senior security practitioners carry in their heads. This has profound implications for the 4.8 million unfilled cybersecurity positions.
The library’s progressive disclosure design—scanning all skills at ~30 tokens each while loading only relevant playbooks—represents a paradigm shift in how AI agents interact with security knowledge. The multi-framework mapping ensures that whether an organization is governed by NIST CSF 2.0, tracking adversary TTPs via MITRE ATT&CK, securing AI systems against ATLAS techniques, or complying with the EU AI Act’s NIST AI RMF safe harbor provisions, the AI agent speaks the same language as the security team.
Prediction:
- +1 The skills library model will become the de facto standard for AI security automation within 18 months, with major vendors building proprietary skills libraries while open-source communities maintain federated registries.
- +1 Organizations that adopt AI skills libraries will achieve 40–60% faster mean time to detect (MTTD) and respond (MTTR) as AI agents autonomously execute framework-aligned investigation playbooks.
- -1 The proliferation of AI agents with offensive security skills (red teaming, penetration testing) will lower the barrier to entry for threat actors, requiring enhanced guardrails, authorization controls, and continuous monitoring of AI agent actions.
- +1 The NIST AI RMF mapping—covering 85 skills—will become increasingly critical as the EU AI Act’s full requirements go live and organizations seek legal safe harbor through framework compliance.
- -1 Security teams must invest in AI agent governance frameworks to prevent unauthorized skill execution, as agents with 817 production-grade skills could inadvertently cause system disruptions if not properly constrained.
- +1 The agentskills.io open standardwill emerge as the industry specification for AI security skills, enabling cross-platform interoperability and a vibrant ecosystem of community-contributed skills.
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