Anthropic Cybersecurity Skills
817 structured cybersecurity skills for AI agents, spanning 29 domains and 6 frameworks.
Evidence shows a security policy (SECURITY.md) and responsible disclosure process, but no specific implementation details for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Thus, these criteria are only partially met, scoring 1.
Repository structure is consistent, and README and index generation script (update-index.yml) show automatic skill count sync, but no details on dependency availability guarantees or failure messages. Thus, self-consistency scores 2, dependency availability and failure messages score 1.
README details target audience (security analysts, AI agents) and multiple use scenarios, and provides skill trigger conditions (When to Use) and prerequisites, but lacks explicit capability boundaries or environment fit specifics. Thus, audience and scenarios, capability boundaries, trigger precision, environment fit all score 2.
Information architecture is clear (skill directory structure, frontmatter spec), install notes (npx, git clone) are explicit, naming is stable (kebab-case), examples and FAQ sections exist, but known limitations are not explicitly listed. License is Apache-2.0, versioning and changelog are recorded, maintenance responsibility is clear (community contributions). Thus, information architecture, install notes, naming stability, examples and FAQ, license, versioning and changelog, maintenance responsibility score 2, known limitations score 1.
Output usability is high (structured skills for AI agents), marginal value is clear (fills security knowledge gap), cost-benefit is reasonable (~30 tokens to scan, 500-2000 tokens to load). Thus, output usability, marginal value, cost-benefit all score 2.
Claims are traceable (skills mapped to MITRE frameworks, with validation claims), but cross-source corroboration is limited (only mentions external lists and quotes), and fact-inference separation is unclear (README mixes facts and inferences). Thus, claim traceability scores 2, cross-source corroboration and fact-inference separation score 1.
- Repository contains offensive security skills; users must ensure authorized use only.
- Publisher identity is unverified; assess source credibility with caution.
- Skill content may contain outdated or inaccurate information; validate in real environments.
What does this agent do, and when should you use it?
This is a large open-source cybersecurity skills library designed for AI agents, containing 817 structured skills across 29 security domains such as cloud security, threat hunting, digital forensics, and malware analysis. Each skill follows the agentskills.io standard, using YAML frontmatter and Markdown bodies for agent discovery and execution. Skills are mapped to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF, and MITRE F3 frameworks, and are compatible with 26+ AI platforms. The project is community-maintained and not affiliated with Anthropic.
The library provides AI agents with structured security operation skills. Agents can scan skill frontmatter (~30 tokens each) for fast filtering, load full workflows (500-2000 tokens) and execute step-by-step. For example, for memory forensics, the skill instructs to run Volatility3 plugins, check LSASS, and correlate event logs. Each skill includes sections like When to Use, Prerequisites, Workflow, and Verification, and references framework mappings. Installation is via npx skills add or git clone, compatible with Claude Code, GitHub Copilot, Codex CLI, and others.
- A security analyst uses an AI agent to analyze a suspicious memory dump; the skill guides running Volatility3 plugins to identify signs of credential theft.
- An incident response team automates a ransomware response playbook, including containment, analysis, and recovery steps.
- A red teamer uses the agent for penetration testing, with skills covering ADCS attacks, BloodHound enumeration, and C2 deployment.
- A threat hunter uses the agent to detect Kerberoasting, with skills providing Sigma rules and log analysis workflows.
- A cloud security engineer audits AWS/Azure/GCP configurations, performing cloud attack emulation and forensics.
- A fraud investigator uses the agent to map MITRE F3 framework, identifying financial fraud TTPs like credential stuffing and money laundering.
What are this agent's strengths and limitations?
- Largest open-source skills library with 817 skills across 29 domains.
- Mapped to 6 frameworks, including the latest MITRE F3 and ATT&CK v19.1, aiding compliance and threat modeling.
- adheres to agentskills.io standard, compatible with 26+ platforms, with progressive loading to save context window.
- Active community with many contributors and continuous updates.
- Includes offensive techniques, restricted to authorized use; legal and ethical risks.
- Skill quality depends on community maintenance; some domains (e.g., hardware security) have few skills.
- Requires agent to support agentskills.io standard; otherwise extra adaptation needed.
- Some skills may require specific tools or environments, such as Volatility3 or specific cloud environments.
How do you install or deploy this agent?
Recommended installation via npx:
npx skills add mukul975/Anthropic-Cybersecurity-SkillsOr via git clone:
git clone https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
cd Anthropic-Cybersecurity-SkillsAfter installation, it works immediately with platforms supporting the agentskills.io standard, requiring no additional configuration.
How do you use this agent?
After installation, point your agent to the skills directory. For example, in Claude Code or Codex CLI, the agent will automatically load skills. Users can give security tasks like 'analyze this memory dump for signs of credential theft', and the agent scans frontmatter, selects relevant skills (e.g., memory forensics), and executes the workflow. Skills include detailed commands and verification steps for the agent to follow.