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公开评测 · 综合采用结论

存在需要人工复核的风险或证据不足

查看评测依据 评测我的项目基于公开项目证据,非安全认证或安装推荐
72stars
11forks
最近更新 29天前
评测生成时间(北京时间)
本报告引擎
v3.10.0
当前引擎
v3.16.0

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重新评测此项目

进入后确认来源与额度,提交才会创建任务。

Evaluation report

综合采用结论

59
D
满分 100
谨慎采用高风险
决策摘要

存在需要人工复核的风险或证据不足

88%
高置信度
92
文档
56
安全
73
质量
100
活跃
32
采用
  • 基础评测完成+25/25确定性评分与静态安全扫描已完成
  • README 有效证据+25/2525,757 个去重后的有效字符
  • 独立证据来源+8/202 类非重复证据,重复文件不叠加
  • 仓库元数据+10/10已取得仓库状态与采用数据
  • 活跃记录+5/5已取得最近提交时间
  • AI 复核+15/15已完成结构化 AI 证据复核
How it works · 架构图

技能库架构与使用流程

README描述了技能库的组成(技能、frontmatter、框架映射)及代理使用流程,无明确时间顺序,故选architecture。

AI 提取 · 证据约束

左右滑动查看完整图示

技能库架构与使用流程README描述了技能库的组成(技能、frontmatter、框架映射)及代理使用流程,无明确时间顺序,故选architecture。扫描frontmatter包含映射AI代理使用者技能库知识库单个技能单元框架映射元数据
图示依据
  • • README中'How AI agents use these skills'描述代理扫描frontmatter并加载技能
  • • README中'Skill anatomy'展示技能目录结构
  • • README中'Framework coverage'列出各框架映射数量
五维表现
大规模技能库,覆盖广且映射多框架,价值明确;但安装与运行细节不足,错误处理缺失,需自行补充。
质量证据
  • README中'What's inside — 34 security domains'列出各领域技能数量
  • README中'Skill anatomy'展示目录结构:SKILL.md、references/、scripts/、assets/
  • README中'Quick start'提供npx skills add和git clone命令
  • README中'Authorized & lawful use only'声明使用限制
  • README中'Markdown body sections'列出When to Use、Workflow等章节
采用建议
优势
  • 问题与用途描述
  • 有效 README
  • 安装或接入步骤
  • 可执行示例
  • 818个技能覆盖34个领域,映射6个框架,解决AI代理缺乏结构化安全知识的问题
关注点
  • 发现提示词或系统信息提取意图
  • 缺少错误处理或排障
  • 缺少具体技能的最小可运行示例,仅展示目录结构
  • 未说明技能如何被代理加载或执行的具体机制
  • 错误处理或排障章节缺失
适合

需要为AI代理提供结构化网络安全知识库的开发者、希望按MITRE ATT&CK等框架映射安全流程的团队、用于授权渗透测试、DFIR、威胁狩猎等场景、作为agentskills.io标准兼容的技能集合

不建议直接用于

需要即插即用、无需额外配置的单一技能用户、对技能内部执行细节有严格审计要求的场景

也有自己的公开项目?先看完证据,再用当前规则生成独立报告。

评测我的项目 →
文档证据
92/100
问题与用途描述10 分
有效 README12 分
安装或接入步骤14 分
可执行示例16 分
输入、参数或工具说明11 分
输出或结果说明9 分
限制、权限或边界12 分
错误处理或排障8 分
许可证信息5 分
结构化章节3 分
安全证据
高风险
发现提示词或系统信息提取意图
prompt-extractionREADME.md:96high confidence
| Exfiltration | TA0010 | 82 |

修复:移除提示词提取逻辑,并增加敏感上下文不可输出的边界说明。

发现提示词或系统信息提取意图
prompt-extractionREADME.md:293high confidence
| Exfiltration | TA0010 | Strong | DNS exfiltration, DLP controls, data loss detection |

修复:移除提示词提取逻辑,并增加敏感上下文不可输出的边界说明。

优先改进清单
  1. 01移除提示词提取逻辑,并增加敏感上下文不可输出的边界说明。
  2. 02补充错误处理或排障
方法、证据与局限展开
数据来源

GitHub Repository API

扫描范围

2 个文件 · 33,287 字符

评测引擎

v3.10.0 · AI 复核已启用(deepseek-chat)

局限
  • 静态评测不会安装或执行项目代码
  • 安全扫描基于高信号文件与已知模式,不能替代人工审计
  • 流行度只反映采用程度,不代表安全或工程质量

30 天热度趋势

README

Anthropic Cybersecurity Skills

Anthropic Cybersecurity Skills

The largest open-source cybersecurity skills library for AI agents

GARS-2026 Survey License Skills Frameworks MITRE F3 Domains Platforms GitHub stars GitHub forks Last Commit agentskills.io PRs Welcome Playground Hermes Agent

818 production-grade cybersecurity skills · 34 security domains · 6 framework mappings · 26+ AI platforms

Get Started · What's Inside · Frameworks · Platforms · Contributing


⚠️ 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.

FrameworkVersionFramework scopeWhat it maps
MITRE ATT&CKv19.115 tactics · Enterprise/Mobile/ICSAdversary behaviors and TTPs
NIST CSF 2.02.06 functions · 22 categories · 106 subcategoriesOrganizational security posture
MITRE ATLAS2026.07101 techniques · 77 sub-techniquesAI/ML adversarial threats
MITRE D3FENDv1.4.0270 techniquesDefensive countermeasures
NIST AI RMF1.04 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 skillsCyber-enabled financial fraud TTPs

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

SkillATT&CKNIST CSFATLASD3FENDAI RMFF3
analyzing-network-traffic-of-malwareT1071DE.CMAML.T0047D3-NTAMEASURE-2.6—
detecting-business-email-compromiseT1566DE.AE———F1005.006 · monetization

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

MITRE F3

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.

TacticIDSkills
ReconnaissanceTA0043103
Resource DevelopmentTA004222
Initial AccessTA0001467
ExecutionTA0002350
PersistenceTA0003444
Privilege EscalationTA0004464
StealthTA0005442
Defense ImpairmentTA011292
Credential AccessTA0006202
DiscoveryTA0007237
Lateral MovementTA000868
CollectionTA0009172
Command and ControlTA0011123
ExfiltrationTA001082
ImpactTA004050

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

DomainSkillsKey capabilities
Cloud Security66AWS, Azure, GCP hardening · CSPM · cloud attack emulation · cloud forensics
SOC Operations63Playbooks · escalation workflows · Graph-log detection · tabletop exercises
Threat Hunting58Hypothesis-driven hunts · LOTL detection · EVTX hunting · fleet hunting
Threat Intelligence52STIX/TAXII · MISP · OpenCTI · feed integration · actor profiling
Web Application Security46OWASP Top 10 · SQLi · XSS · SSRF · deserialization
Network Security43IDS/IPS · firewall rules · VLAN segmentation · traffic analysis
Digital Forensics41Disk imaging · memory forensics · Hayabusa/KAPE/Plaso timelines
Identity & Access Management40Entra ID/ROADtools · device-code phishing · PAM · zero trust identity
Malware Analysis39Static/dynamic analysis · reverse engineering · sandboxing
Red Teaming35ADCS/Certipy · BloodHound CE · Sliver/Havoc C2 · NTLM relay
Container Security33K8s RBAC · image scanning · Falco · container escape
OT/ICS Security29Modbus · DNP3 · IEC 62443 · historian defense · SCADA
API Security28GraphQL · REST · OWASP API Top 10 · WAF bypass
Incident Response26Breach containment · ransomware response · IR playbooks
Vulnerability Management25Nessus · scanning workflows · patch prioritization · CVSS
Penetration Testing23Network · web · cloud · mobile · NetExec lateral movement
DevSecOps18CI/CD security · Trivy IaC/image scanning · code signing
Zero Trust Architecture18BeyondCorp · CISA maturity model · microsegmentation
Endpoint Security17EDR · LOTL detection · fileless malware · persistence hunting
Phishing Defense16Email authentication · BEC detection · phishing IR
Cryptography16TLS · Ed25519 · post-quantum migration · key management
AI Security14LLM red-teaming (garak/PyRIT) · prompt injection · MCP/agentic security · guardrails
Mobile Security13Android/iOS analysis · mobile pentesting · MDM forensics
Ransomware Defense13Precursor detection · response · recovery · encryption analysis
Compliance & Governance10NIST 800-30/RMF · CMMC · HIPAA · TPRM · CIS benchmarks
Supply Chain Security8SBOMs · dependency confusion · malicious-package triage · SLSA/Sigstore
Threat Detection7Credential dumping · golden-ticket forgery · pass-the-ticket · LOLBAS · UEBA insider signals
Hardware & Firmware Security6CHIPSEC/UEFI audit · Secure Boot bypass · TPM attestation · bootkit hunting
Deception Technology6Honeytokens · canarytokens · breach detection
Blockchain Security2Ethereum smart-contract vulnerabilities · Foundry audit workflows
Wireless Security2Bluetooth Low Energy attack detection · BLE security assessment
Privacy Compliance2GDPR data-subject access requests · privacy impact assessments
Data Protection1Data loss prevention with Microsoft Purview
Purple Team1Atomic 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

 

TacticIDCoverageKey skills
ReconnaissanceTA0043StrongOSINT, subdomain enumeration, DNS recon
Resource DevelopmentTA0042ModeratePhishing infrastructure, C2 setup detection
Initial AccessTA0001StrongPhishing simulation, exploit detection, forced browsing
ExecutionTA0002StrongPowerShell analysis, fileless malware, script block logging
PersistenceTA0003StrongScheduled tasks, registry, service accounts, LOTL
Privilege EscalationTA0004StrongKerberoasting, AD attacks, cloud privilege escalation
StealthTA0005StrongObfuscation, rootkit analysis, evasion detection
Defense ImpairmentTA0112ModerateImpair Defenses (T1562), log/indicator removal, EDR tampering
Credential AccessTA0006StrongMimikatz detection, pass-the-hash, credential dumping
DiscoveryTA0007ModerateBloodHound, AD enumeration, network scanning
Lateral MovementTA0008StrongSMB exploits, lateral movement detection with Splunk
CollectionTA0009ModerateEmail forensics, data staging detection
Command and ControlTA0011StrongC2 beaconing, DNS tunneling, Cobalt Strike analysis
ExfiltrationTA0010StrongDNS exfiltration, DLP controls, data loss detection
ImpactTA0040StrongRansomware 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

 

FunctionSkillsExamples
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

WhereTypeLink
awesome-agent-skillsAwesome List (1,000+ skills index)VoltAgent/awesome-agent-skills
awesome-ai-securityAwesome List (AI security tools)ottosulin/awesome-ai-security
awesome-codex-cliAwesome List (Codex CLI resources)RoggeOhta/awesome-codex-cli
SkillsLLMSkills directory & marketplaceskillsllm.com/skill/anthropic-cybersecurity-skills
OpenflowsSignal analysis & trackingopenflows.org
NeverSight skills_feedAutomated skills indexNeverSight/skills_feed

Star history

Star History Chart

Releases

VersionDateHighlights
v1.0.0March 11, 2026734 skills · 26 domains · MITRE ATT&CK + NIST CSF 2.0 mapping · ATT&CK Navigator layer

Skills have continued to grow on main since v1.0.0 — the library now contains 818 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 — Read SCOPE.md first, then the template in CONTRIBUTING.md. The thinnest domains are the ones most worth adding to: Data Protection and Purple Team have one skill each, and Blockchain Security, Wireless Security and Privacy Compliance have two. Submit one skill per 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. Deepening a skill that already exists is worth as much to this library as adding a new one.

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

Every PR is reviewed for technical accuracy and agentskills.io standard compliance. I do that review myself, and the queue is currently longer than I would like — some pull requests have been open for months. I am working through them, and small, focused PRs move fastest. 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:

@mukul975 @valorisa @juliosuas @Daytona39264 @kevglynn @andrewibrah @Bortlesboat @DevRedious @ioxoi @OctoBored @shanujans @farhan6667 @dakshverma23 @nyxst4ck

14 contributors, 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         = {818 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.


If this project helps your security work, consider giving it a ⭐

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Community project by @mukul975. Not affiliated with Anthropic PBC.