⚠️ 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 818 structured cybersecurity skills spanning 34 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.
| Framework |
Version |
Framework scope |
What it maps |
| MITRE ATT&CK |
v19.1 |
15 tactics · Enterprise/Mobile/ICS |
Adversary behaviors and TTPs |
| NIST CSF 2.0 |
2.0 |
6 functions · 22 categories · 106 subcategories |
Organizational security posture |
| MITRE ATLAS |
2026.07 |
101 techniques · 77 sub-techniques |
AI/ML adversarial threats |
| MITRE D3FEND |
v1.4.0 |
270 techniques |
Defensive countermeasures |
| NIST AI RMF |
1.0 |
4 functions (Govern/Map/Measure/Manage) |
AI risk management |
| MITRE F3 (Fight Fraud Framework) |
v1.1 (2026-04-09) |
8 tactics · 123 techniques · 94 fraud-relevant skills |
Cyber-enabled financial fraud TTPs |
Example — each skill maps only to the frameworks relevant to it (one may hit all six, another just a couple):
| Skill |
ATT&CK |
NIST CSF |
ATLAS |
D3FEND |
AI RMF |
F3 |
analyzing-network-traffic-of-malware |
T1071 |
DE.CM |
AML.T0047 |
D3-NTA |
MEASURE-2.6 |
— |
detecting-business-email-compromise |
T1566 |
DE.AE |
— |
— |
— |
F1005.006 · monetization |
🆕 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.
| Tactic |
ID |
Skills |
| Reconnaissance |
TA0043 |
103 |
| Resource Development |
TA0042 |
22 |
| Initial Access |
TA0001 |
467 |
| Execution |
TA0002 |
350 |
| Persistence |
TA0003 |
444 |
| Privilege Escalation |
TA0004 |
464 |
| Stealth |
TA0005 |
442 |
| Defense Impairment |
TA0112 |
92 |
| Credential Access |
TA0006 |
202 |
| Discovery |
TA0007 |
237 |
| Lateral Movement |
TA0008 |
68 |
| Collection |
TA0009 |
172 |
| Command and Control |
TA0011 |
123 |
| Exfiltration |
TA0010 |
82 |
| Impact |
TA0040 |
50 |
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 — 34 security domains
| Domain |
Skills |
Key capabilities |
| Cloud Security |
66 |
AWS, Azure, GCP hardening · CSPM · cloud attack emulation · cloud forensics |
| SOC Operations |
63 |
Playbooks · escalation workflows · Graph-log detection · tabletop exercises |
| Threat Hunting |
58 |
Hypothesis-driven hunts · LOTL detection · EVTX hunting · fleet hunting |
| Threat Intelligence |
52 |
STIX/TAXII · MISP · OpenCTI · feed integration · actor profiling |
| Web Application Security |
46 |
OWASP Top 10 · SQLi · XSS · SSRF · deserialization |
| Network Security |
43 |
IDS/IPS · firewall rules · VLAN segmentation · traffic analysis |
| Digital Forensics |
41 |
Disk imaging · memory forensics · Hayabusa/KAPE/Plaso timelines |
| Identity & Access Management |
40 |
Entra ID/ROADtools · device-code phishing · PAM · zero trust identity |
| Malware Analysis |
39 |
Static/dynamic analysis · reverse engineering · sandboxing |
| Red Teaming |
35 |
ADCS/Certipy · BloodHound CE · Sliver/Havoc C2 · NTLM relay |
| Container Security |
33 |
K8s RBAC · image scanning · Falco · container escape |
| OT/ICS Security |
29 |
Modbus · DNP3 · IEC 62443 · historian defense · SCADA |
| API Security |
28 |
GraphQL · REST · OWASP API Top 10 · WAF bypass |
| Incident Response |
26 |
Breach containment · ransomware response · IR playbooks |
| Vulnerability Management |
25 |
Nessus · scanning workflows · patch prioritization · CVSS |
| Penetration Testing |
23 |
Network · web · cloud · mobile · NetExec lateral movement |
| DevSecOps |
18 |
CI/CD security · Trivy IaC/image scanning · code signing |
| Zero Trust Architecture |
18 |
BeyondCorp · CISA maturity model · microsegmentation |
| Endpoint Security |
17 |
EDR · LOTL detection · fileless malware · persistence hunting |
| Phishing Defense |
16 |
Email authentication · BEC detection · phishing IR |
| Cryptography |
16 |
TLS · Ed25519 · post-quantum migration · key management |
| AI Security |
14 |
LLM red-teaming (garak/PyRIT) · prompt injection · MCP/agentic security · guardrails |
| Mobile Security |
13 |
Android/iOS analysis · mobile pentesting · MDM forensics |
| Ransomware Defense |
13 |
Precursor detection · response · recovery · encryption analysis |
| Compliance & Governance |
10 |
NIST 800-30/RMF · CMMC · HIPAA · TPRM · CIS benchmarks |
| Supply Chain Security |
8 |
SBOMs · dependency confusion · malicious-package triage · SLSA/Sigstore |
| Threat Detection |
7 |
Credential dumping · golden-ticket forgery · pass-the-ticket · LOLBAS · UEBA insider signals |
| Hardware & Firmware Security |
6 |
CHIPSEC/UEFI audit · Secure Boot bypass · TPM attestation · bootkit hunting |
| Deception Technology |
6 |
Honeytokens · canarytokens · breach detection |
| Blockchain Security |
2 |
Ethereum smart-contract vulnerabilities · Foundry audit workflows |
| Wireless Security |
2 |
Bluetooth Low Energy attack detection · BLE security assessment |
| Privacy Compliance |
2 |
GDPR data-subject access requests · privacy impact assessments |
| Data Protection |
1 |
Data loss prevention with Microsoft Purview |
| Purple Team |
1 |
Atomic Red Team purple-team testing |
817 skills across 34 domains. Counts come from the subdomain field in each skill's frontmatter.
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 818 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 818 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) |
|
|
一个开源的 AI 智能体网络安全技能库，包含 818 个生产级技能，覆盖 34 个安全领域，并映射到 MITRE ATT&CK 和 NIST CSF 2.0 等六个框架。