Anthropic Just Dropped 817 Cybersecurity Skills For AI Agents — Here’s How To Use Them + Video

Introduction:

The cybersecurity industry is facing a staggering talent shortage — ISC2 estimated a global gap of 4.8 million unfilled positions in 2024. At the same time, AI agents have grown capable enough to perform vulnerability analysis, log forensics, and even portions of red-team penetration testing. Bridging these two realities is Anthropic Cybersecurity Skills, an open-source library that gives any AI agent instant access to 817 production-grade cybersecurity skills across 29 security domains, all mapped to six industry frameworks including MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, NIST AI RMF, and MITRE F3 (Fight Fraud Framework). This isn’t a collection of generic prompts or script payloads — it’s a structured knowledge base that transforms AI agents into security practitioners capable of supporting analysts, automating repetitive tasks, and accelerating decision-making.

Learning Objectives:

Understand the architecture and scope of the Anthropic Cybersecurity Skills library, including its 29 security domains and six-framework mapping
Learn how to install and configure the skills library across 26+ AI platforms including Claude Code, Cursor, GitHub Copilot, and Gemini CLI
Master the skill anatomy — YAML frontmatter, Markdown workflows, reference files, and executable scripts
Apply practical skills for memory forensics, threat hunting, cloud incident response, and malware analysis
Implement framework-aligned compliance workflows using MITRE ATT&CK, NIST CSF 2.0, and MITRE D3FEND mappings

You Should Know:

What’s Inside the Library — 817 Skills Across 29 Security Domains

The library organizes 817 structured cybersecurity skills across 29 specialized domains, each following the `agentskills.io` open standard. Here’s a breakdown of the most prominent domains:

| Domain | Skills | Core Capabilities |

|–|–|-|

What makes this library unique is that every single skill is simultaneously mapped to six industry frameworks:

MITRE ATT&CK v19.1 — 15 tactics, 286 techniques covering adversary behavior and TTPs
NIST CSF 2.0 — 6 functions, 22 categories for organizational security posture
MITRE ATLAS v5.4 — 16 tactics, 84 techniques for AI/ML adversarial threats including prompt injection and model extraction
MITRE D3FEND v1.3 — 7 categories, 267 defensive countermeasure techniques
NIST AI RMF 1.0 — 4 functions, 72 subcategories for AI risk management
MITRE F3 (Fight Fraud Framework) v1.1 — 8 tactics, 123 techniques, 94 fraud-specific skills covering cyber-enabled financial fraud TTPs

For example, the skill `analyzing-1etwork-traffic-of-malware` maps to ATT&CK T1071, NIST CSF DE.CM, ATLAS AML.T0047, D3FEND D3-1TA, and NIST AI RMF MEASURE-2.6 simultaneously.

How to Install — One Command, 26+ Platforms

The library works with 26+ AI platforms including Claude Code, GitHub Copilot, OpenAI Codex CLI, Cursor, Gemini CLI, LangChain, CrewAI, and AutoGen. Installation takes under 30 seconds:

Option 1 — npx (recommended):

npx skills add mukul975/Anthropic-Cybersecurity-Skills

Option 2 — Claude Code plugin marketplace:

/plugin marketplace add mukul975/Anthropic-Cybersecurity-Skills

Option 3 — Manual clone:

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

Option 4 — x-cmd (Linux/macOS):

sh x install Anthropic-Cybersecurity-Skills

Once installed, your AI agent immediately gains the ability to perform memory forensics, hunt for C2 beaconing, audit Kubernetes RBAC, reverse .NET malware, and hundreds more tasks.

Skill Anatomy — How Each Skill Is Structured

Every skill in the library follows the `agentskills.io` open standard with a progressive disclosure architecture:

YAML Frontmatter — Enables sub-second agent discovery at approximately 30 tokens per skill to scan. Agents can search all 817 skills without blowing context windows
Structured Markdown Body — Step-by-step execution workflows that load at 500–2,000 tokens when a skill is matched
Reference Files — Deep technical context including API references, standards documentation, and workflow guides
Executable Scripts — Python or shell scripts that perform actual checks,采集, or analysis actions (e.g., skills//scripts/agent.py)

Skill directory structure:

skill-1ame/
├── SKILL.md  YAML frontmatter + Markdown workflow
├── references/
│ ├── standards.md  Framework mappings
│ └── workflows.md  Step-by-step procedures
├── scripts/
│ └── agent.py  Executable actions
└── assets/
└── templates/  Config templates

This architecture ensures AI agents can efficiently search the entire knowledge base while only loading the detailed instructions for skills they actually need to execute.

4. Practical Walkthrough — Memory Forensics with Volatility3

Let’s walk through a real-world skill: memory forensics on a suspicious memory dump. The library includes skills that tell the AI agent exactly which Volatility3 plugin to run, what commands to execute, and how to interpret results.

Step 1 — Agent discovers the skill

The agent scans YAML frontmatter across all 817 skills (~30 tokens each) and identifies the memory forensics skill matching the user’s request.

Step 2 — Agent loads the full skill

The agent loads the Markdown workflow (~500–2,000 tokens) containing:


name: memory-forensics-volatility
description: Analyze memory dumps using Volatility3 for malicious artifacts
frameworks:
- MITRE ATT&CK: T1003 (Credential Dumping)
- NIST CSF: DE.CM (Security Continuous Monitoring)
- MITRE D3FEND: D3-MA (Memory Analysis)

Step 3 — Agent executes the workflow

The skill provides structured commands:

 Identify the memory image profile
vol -f /path/to/memory.dump windows.info

Dump processes and identify suspicious entries
vol -f /path/to/memory.dump windows.pslist

Extract network connections
vol -f /path/to/memory.dump windows.netscan

Dump malicious process memory for further analysis
vol -f /path/to/memory.dump windows.dumpfiles --pid <suspicious_pid>

Step 4 — Verification

The skill includes verification steps to confirm findings and map them back to MITRE ATT&CK techniques.

5. Framework-Aligned Compliance — One Skill, Six Checkboxes

No other open-source skills library maps every skill to all six frameworks simultaneously. This is critical for compliance workflows:

SOC operations can use NIST CSF 2.0 mappings to demonstrate security controls
Threat hunting teams leverage MITRE ATT&CK to understand adversary TTPs
AI security teams use MITRE ATLAS and NIST AI RMF for AI-specific threat modeling
Fraud investigators apply MITRE F3 for cyber-enabled financial fraud detection
Defensive teams implement MITRE D3FEND countermeasures

Example — Cloud breach scoping across AWS, Azure, and GCP:
The library provides consistent playbooks for scoping cloud breaches across all three major cloud providers. The AI agent can:
1. Map findings to ATT&CK techniques without hallucinating IDs

2. Automatically generate NIST CSF-aligned incident reports

3. Recommend D3FEND countermeasures for containment

Identify AI-specific risks using ATLAS and AI RMF mappings

6. Linux and Windows Commands Integrated into Skills

The skills library contains executable commands and scripts for both Linux and Windows environments. Here are examples of what’s included:

Linux security commands:

 Network traffic analysis
tcpdump -i eth0 -w capture.pcap
tshark -r capture.pcap -Y "http.request"

Log analysis
grep -E "Failed password|Invalid user" /var/log/auth.log
journalctl -u sshd --since "1 hour ago"

Malware detection
clamscan -r /home/user/
rkhunter --check

Windows security commands (PowerShell):

 Event log hunting
Get-WinEvent -LogName Security -FilterXPath "[System[EventID=4625]]"

Process analysis
Get-Process | Where-Object {$<em>.CPU -gt 50}
Get-Service | Where-Object {$</em>.Status -eq 'Running'}

Registry analysis for persistence
Get-ChildItem -Path "HKLM:\Software\Microsoft\Windows\CurrentVersion\Run"

Forensic tool integration:

Hayabusa — Windows event log analysis
KAPE — Kroll Artifact Parser and Extractor
Plaso — Timeline analysis for disk images

What Undercode Say:

This is the missing link between AI capability and security domain expertise. The library doesn’t just make AI agents smarter — it gives them the structured judgment of a senior security analyst. A junior analyst knows which Volatility3 plugin to run and which Sigma rules catch Kerberoasting. Now your AI agent knows too.
Framework alignment is the game-changer. Most AI security tools operate in isolation from industry standards. This library embeds MITRE ATT&CK, NIST CSF, D3FEND, ATLAS, AI RMF, and F3 into every single skill. That means AI-generated recommendations are immediately audit-ready and compliance-aligned.

Analysis:

The Anthropic Cybersecurity Skills library represents a fundamental shift in how AI agents interact with security operations. Rather than treating AI as a generic assistant that searches the web for answers, this approach positions it as a structured knowledge worker capable of executing practitioner-grade security workflows.

The progressive disclosure architecture is particularly clever — agents can scan all 817 skills at minimal token cost (~30 tokens each) and only load the detailed playbooks when needed (500–2,000 tokens). This makes the library practical for real-world deployment where context window limits are a constraint.

The inclusion of MITRE F3 (Fight Fraud Framework) is noteworthy — it’s the only open-source skills library to include this framework, covering 94 fraud-specific skills that address the “monetization” phase of cybercrime that ATT&CK doesn’t cover.

However, there are important caveats. This is a community project maintained by Mahipal Jangra (@mukul975) and is not affiliated with Anthropic PBC. The use of “Anthropic” in the name reflects that the skills were initially designed for the Claude ecosystem but have since expanded to 26+ platforms.

The library has gained significant traction — over 21,000 GitHub stars and 2,467 forks as of June 2026. It has been featured in multiple curated lists including VoltAgent/awesome-agent-skills and ottosulin/awesome-ai-security.

For organizations, the implications are clear: combining AI + structured cybersecurity expertise + governance frameworks will be the winning formula for cyber resilience. The library provides the “expertise” layer that most AI security tools lack.

Prediction:

+1 Organizations that adopt structured AI security skills libraries will reduce mean time to detect (MTTD) and mean time to respond (MTTR) by 40–60% within 18 months, as AI agents automate the repetitive, time-consuming tasks that currently bog down SOC analysts.

+1 The agentskills.io standard will become the de facto format for AI agent knowledge bases, similar to how YAML became standard for configuration management. Expect major vendors to build native support into their AI platforms.

-1 The cybersecurity skills gap will continue widening in the short term (2–3 years) as organizations struggle to integrate AI agents into existing workflows without proper governance and training. The library is a tool, not a replacement for human expertise.

+1 MITRE F3 integration will drive a new wave of AI-powered fraud detection capabilities, particularly in financial services, where the “monetization” phase of cybercrime has been historically difficult to detect and attribute.

-1 Over-reliance on AI agents for security decisions without human verification could lead to catastrophic failures in complex, novel attack scenarios that fall outside the 817 pre-defined skills. Human-in-the-loop workflows will remain essential.

+1 The open-source nature of the library (Apache 2.0) will accelerate innovation as security researchers contribute new skills, creating a virtuous cycle where the library becomes more comprehensive and valuable over time.

+1 Framework-aligned AI security will become a regulatory expectation within 3–5 years, as auditors and regulators demand evidence that AI-assisted security decisions are mapped to recognized standards like NIST CSF and MITRE ATT&CK.

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