🔥 mukul975/Anthropic-Cybersecurity-Skills：为 AI Agent 提供高级安全分析师技能库

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⚠️ 社区项目——这是一个独立创建的社区项目，与 Anthropic PBC 没有隶属关系。

🔐 仅限经授权且合法的使用。 本库包含进攻性和双重用途技术（例如红队 C2、钓鱼模拟和漏洞利用），仅面向经授权的渗透测试、安全研究、防御与教育。仅可将其用于您拥有或获得明确书面授权进行测试的系统，并遵守所有适用法律和交战规则。您须对这些技能的使用方式承担全部责任。请参阅 SECURITY.md 和 CODE_OF_CONDUCT.md。

为任何 AI Agent 赋予高级安全分析师的技能

初级分析师知道该对可疑内存转储运行哪个 Volatility3 插件，哪些 Sigma 规则能够捕获 Kerberoasting，以及如何界定横跨三个云服务商的云端入侵范围。而您的 AI Agent 并不知道——除非您向它提供这些技能。

该仓库包含覆盖 29 个安全领域的 817 项结构化网络安全技能，每项均遵循 agentskills.io 开放标准。该库映射至六大行业框架——MITRE ATT&CK、NIST CSF 2.0、MITRE ATLAS、MITRE D3FEND、NIST AI RMF，以及 MITRE Fight Fraud Framework（F3）；每项技能均映射到与其类型相关的框架（例如，一项取证技能会包含 ATT&CK + CSF 映射；一项 AI 安全技能则会额外添加 ATLAS 和 AI RMF）。克隆该仓库，将您的 Agent 指向它，下一次安全调查即可在数秒内获得专家级指引。

六大框架，一个技能库

每项技能会映射至符合其主题的框架——ATT&CK 和 NIST CSF 几乎普遍适用，ATLAS、AI RMF、D3FEND 和 F3 则在相关场景中适用。**817 项技能的框架覆盖情况：**MITRE ATT&CK 805 项 · NIST CSF 2.0 804 项 · MITRE D3FEND 139 项 · NIST AI RMF 97 项 · MITRE F3 94 项 · MITRE ATLAS 93 项。

框架	版本	框架范围	映射内容
MITRE ATT&CK	v19.1	15 项战术 · Enterprise/Mobile/ICS	对手行为和 TTP
NIST CSF 2.0	2.0	6 项功能 · 22 个类别 · 106 个子类别	组织安全态势
MITRE ATLAS	2026.07	101 项技术 · 77 项子技术	AI/ML 对抗性威胁
MITRE D3FEND	v1.4.0	270 项技术	防御性对策
NIST AI RMF	1.0	4 项功能（Govern/Map/Measure/Manage）	AI 风险管理
MITRE F3（Fight Fraud Framework）	v1.1（2026-04-09）	8 项战术 · 123 项技术 · 94 项与欺诈相关的技能	网络赋能型金融欺诈 TTP

示例——每项技能只映射到与其相关的框架（一项可能覆盖全部六个框架，另一项可能仅覆盖其中几个）：

技能	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 项与欺诈相关的技能

MITRE Fight Fraud Framework（F3）由 MITRE 的威胁知情防御中心（CTID）于 2026 年 4 月 9 日发布，并与 JPMorganChase、Citigroup、Lloyds Banking Group、Standard Chartered、CrowdStrike、Verizon Business、FS-ISAC 等机构共同开发。它是一套与 ATT&CK 兼容、面向网络赋能型金融欺诈的 TTP 目录，填补了 ATT&CK 在初始入侵之后留下的空白。

F3 v1.1 新增了 ATT&CK 未枚举的两项欺诈专属战术：

• Positioning（FA0001）——在获得访问权限后，为收集或操纵数据并准备实施欺诈所采取的行动（合成身份预置、账户养号、收款人设置、SIM 卡交换预部署、银行会话劫持）。
• Monetization（FA0002）——将被盗资产转化为可用资金（钱骡分层、APP 欺诈、加密货币出金、银行卡套现、退款/拒付滥用）。

欺诈专属技术使用 F1XXX ID（例如 F1005.003 Add Beneficiary、F1025.003 Wire Transfer、F1007 Adversary-in-the-Browser）；复用的 ATT&CK 技术保留其 T1XXX ID。映射位于每项技能的 mitre_f3: frontmatter 块中——全部 123 个 F3 v1.1 技术 ID 均已根据上游 STIX bundle 验证。有关 schema，请参阅 docs/mitre-f3-mapping.md。

MITRE ATT&CK v19.1——已映射 805/817 项技能

每项技能都包含一个 mitre_attack frontmatter 列表，并通过官方 mitreattack-python 库依据MITRE ATT&CK v19.1（最新版本）完成验证——涵盖 Enterprise、ICS 和 Mobile 中的 290 项不同技术及子技术（146 项基础技术 + 144 项子技术）。不存在已撤销或已弃用的 ID。v19.1 对 Defense Evasion 的重构（现拆分为 Stealth 和 Defense Impairment）已在下表体现。

战术	ID	技能数
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

# 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

可立即配合 Claude Code、GitHub Copilot、OpenAI Codex CLI、Cursor、Gemini CLI，以及任何兼容 agentskills.io 的平台使用。

🌍 GARS-2026——全球 Agentic AI 就绪度调查

我正在开展一项全球学术研究，衡量安全专业人员、开发者和企业团队对 Agentic AI 的实际准备程度——包括 MCP 服务器、工具调用、治理和人在回路（human-in-the-loop）工作流。

如果您使用该仓库，您的回答将成为真正有价值的数据点。

📋 参与调查（10 分钟）：调查链接

• 60 个问题 · 匿名 · 由 SRH Berlin 指导
• 可获得 50 枚 Casky Tokens，用于抢先体验 casky.ai
• 结果将以 CC-BY 4.0 协议开放获取发布

🚀 在 Playground 中试用

亲身体验 Casky.ai——无需任何设置。

→ 在 Casky.ai 启动 Playground

Playground 支持您：

• 针对真实目标运行实时网络安全技能演练
• 查看 AI Agent 实时执行结构化技能
• 以交互方式探索映射至 MITRE ATT&CK 的工作流
• 测试威胁狩猎、DFIR 和渗透测试场景

无需安装，无需配置。打开即可开始。

2024 年，全球网络安全劳动力缺口达到 480 万个未填补职位（ISC2）。AI Agent 能够帮助缩小这一缺口——但前提是它们拥有可供使用的结构化领域知识。如今的 Agent 可以编写代码和搜索网页，但缺乏将通用 LLM 转变为胜任安全分析师的实务操作手册。

现有安全工具仓库提供字典、载荷或漏洞利用代码，但没有任何一个能为 AI Agent 提供高级分析师所遵循的结构化决策工作流：何时使用每项技术、需要检查哪些前置条件、如何分步执行，以及如何验证结果。这正是该项目填补的空白。

Anthropic Cybersecurity Skills 不是脚本或检查清单的集合，而是一个专为 agentskills.io 标准从零构建的 AI 原生知识库——使用 YAML frontmatter 实现亚秒级发现，使用结构化 Markdown 支持分步执行，并通过参考文件提供深入技术背景。每项技能编码的都是真实从业者工作流，而非自动生成的摘要。

包含内容——29 个安全领域

领域	技能数	关键能力
云安全	66	AWS、Azure、GCP 加固 · CSPM · 云攻击模拟 · 云取证
威胁狩猎	58	假设驱动狩猎 · LOTL 检测 · EVTX 狩猎 · 大规模终端狩猎
威胁情报	52	STIX/TAXII · MISP · OpenCTI · 情报源集成 · 攻击者画像
网络安全	43	IDS/IPS · 防火墙规则 · VLAN 分段 · 流量分析
Web 应用安全	42	OWASP Top 10 · SQLi · XSS · SSRF · 反序列化
数字取证	41	磁盘镜像 · 内存取证 · Hayabusa/KAPE/Plaso 时间线
恶意软件分析	39	静态/动态分析 · 逆向工程 · 沙箱分析
身份与访问管理	37	Entra ID/ROADtools · 设备代码钓鱼 · PAM · 零信任身份
SOC 运营	35	Playbook · 升级工作流 · Graph-log 检测 · 桌面推演
红队测试	33	ADCS/Certipy · BloodHound CE · Sliver/Havoc C2 · NTLM relay
容器安全	33	K8s RBAC · 镜像扫描 · Falco · 容器逃逸
安全运营	28	SIEM 关联分析 · 日志分析 · 告警分流
OT/ICS 安全	28	Modbus · DNP3 · IEC 62443 · historian 防御 · SCADA
API 安全	28	GraphQL · REST · OWASP API Top 10 · WAF 绕过
事件响应	26	入侵遏制 · 勒索软件响应 · IR Playbook
漏洞管理	25	Nessus · 扫描工作流 · 补丁优先级排序 · CVSS
渗透测试	21	网络 · Web · 云 · 移动端 · NetExec 横向移动
DevSecOps	18	CI/CD 安全 · Trivy IaC/镜像扫描 · 代码签名
零信任架构	17	BeyondCorp · CISA 成熟度模型 · 微分段
终端安全	17	EDR · LOTL 检测 · 无文件恶意软件 · 持久化狩猎
密码学	16	TLS · Ed25519 · 后量子迁移 · 密钥管理
钓鱼防御	15	电子邮件认证 · BEC 检测 · 钓鱼事件响应
AI 安全	14	LLM 红队测试（garak/PyRIT）· 提示词注入 · MCP/Agentic 安全 · 防护机制
移动安全	13	Android/iOS 分析 · 移动端渗透测试 · MDM 取证
勒索软件防御	13	前兆检测 · 响应 · 恢复 · 加密分析
合规与治理	9	NIST 800-30/RMF · CMMC · HIPAA · TPRM · CIS benchmarks
供应链安全	8	SBOM · 依赖混淆 · 恶意包分流 · SLSA/Sigstore
欺骗技术	6	Honeytokens · canarytokens · 入侵检测
硬件与固件安全	4	CHIPSEC/UEFI 审计 · Secure Boot 绕过 · TPM 证明 · bootkit 狩猎

AI Agent 如何使用这些技能

每项技能的扫描成本约为 30 tokens（仅 frontmatter），完整加载则需要 500–2,000 tokens（完整工作流）。这种渐进式披露架构使 Agent 能够在不耗尽上下文窗口的情况下，单次遍历搜索全部 817 项技能。

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)

没有这些技能时，Agent 会猜测工具命令，并遗漏关键步骤。有了这些技能后，它会遵循与高级 DFIR 分析师相同的操作手册。

每项技能遵循一致的目录结构：

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（真实示例）

---
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
---


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

## Prerequisites
R

---

⚠️ 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.

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

# 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.

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

Domain	Skills	Key capabilities
Cloud Security	66	AWS, Azure, GCP hardening · CSPM · cloud attack emulation · cloud forensics
Threat Hunting	58	Hypothesis-driven hunts · LOTL detection · EVTX hunting · fleet hunting
Threat Intelligence	52	STIX/TAXII · MISP · OpenCTI · feed integration · actor profiling
Network Security	43	IDS/IPS · firewall rules · VLAN segmentation · traffic analysis
Web Application Security	42	OWASP Top 10 · SQLi · XSS · SSRF · deserialization
Digital Forensics	41	Disk imaging · memory forensics · Hayabusa/KAPE/Plaso timelines
Malware Analysis	39	Static/dynamic analysis · reverse engineering · sandboxing
Identity & Access Management	37	Entra ID/ROADtools · device-code phishing · PAM · zero trust identity
SOC Operations	35	Playbooks · escalation workflows · Graph-log detection · tabletop exercises
Red Teaming	33	ADCS/Certipy · BloodHound CE · Sliver/Havoc C2 · NTLM relay
Container Security	33	K8s RBAC · image scanning · Falco · container escape
Security Operations	28	SIEM correlation · log analysis · alert triage
OT/ICS Security	28	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	21	Network · web · cloud · mobile · NetExec lateral movement
DevSecOps	18	CI/CD security · Trivy IaC/image scanning · code signing
Zero Trust Architecture	17	BeyondCorp · CISA maturity model · microsegmentation
Endpoint Security	17	EDR · LOTL detection · fileless malware · persistence hunting
Cryptography	16	TLS · Ed25519 · post-quantum migration · key management
Phishing Defense	15	Email authentication · BEC detection · phishing IR
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	9	NIST 800-30/RMF · CMMC · HIPAA · TPRM · CIS benchmarks
Supply Chain Security	8	SBOMs · dependency confusion · malicious-package triage · SLSA/Sigstore
Deception Technology	6	Honeytokens · canarytokens · breach detection
Hardware & Firmware Security	4	CHIPSEC/UEFI audit · Secure Boot bypass · TPM attestation · bootkit hunting

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.

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
---


## 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.

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.

"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

Version	Date	Highlights
v1.0.0	March 11, 2026	734 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 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.

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)

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}
}

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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