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anthropic-cybersecurity-skills

reason-machines/security-skills

754 structured cybersecurity skills mapped to MITRE ATT&CK, NIST CSF, ATLAS, D3FEND, and NIST AI RMF frameworks.

What is anthropic-cybersecurity-skills?

A production-grade library of 754 cybersecurity skills organized across 26 security domains and mapped to five industry-standard frameworks. Use this to enable AI agents to perform incident response, threat hunting, malware analysis, forensics, and security posture assessment with expert-level guidance.

  • Access 754 production-grade cybersecurity skills spanning 26 security domains
  • Map security operations to MITRE ATT&CK techniques, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF frameworks
  • Discover skills by domain, framework mapping, or tags using provided Python utilities
  • Load skills progressively to minimize token cost (frontmatter ~30 tokens, full skill ~500-2000 tokens)
  • Access structured references including standards mappings, technical workflows, helper scripts, and templates

How to install anthropic-cybersecurity-skills

npx skills add https://github.com/reason-machines/security-skills --skill anthropic-cybersecurity-skills
Prerequisites
  • Python 3.6+ for skill discovery and loading utilities
  • Access to the skill repository (GitHub or local clone)
  • Basic familiarity with MITRE ATT&CK, NIST CSF, or other referenced frameworks (optional but helpful)
Claude Code
Cursor
Windsurf
Cline

How to use anthropic-cybersecurity-skills

  1. 1.Install the skill library using npx, git clone, or git submodule
  2. 2.Use the SkillLoader class to scan all skill frontmatter and build an index of available skills
  3. 3.Search for relevant skills by domain, ATT&CK technique ID, or tags using the provided Python functions
  4. 4.Load the full skill content (metadata, references, scripts) when you need to execute a specific skill
  5. 5.Reference the skill's standards.md for framework mappings and workflows.md for technical procedures

Use cases

Good for
  • Investigate security incidents by mapping attack patterns to relevant skills and frameworks
  • Perform malware analysis and reverse engineering with guided procedures and checklists
  • Conduct threat hunting across cloud and on-premises environments using domain-organized skills
  • Execute memory forensics and incident response workflows with step-by-step technical guidance
  • Assess and improve security posture by discovering skills relevant to NIST CSF or ATT&CK techniques
Who it's for
  • Security operations center (SOC) analysts and incident responders
  • Threat hunters and malware analysts
  • Security architects designing defense strategies
  • AI agents performing autonomous security operations
  • Organizations mapping their security practices to industry frameworks

anthropic-cybersecurity-skills FAQ

How many skills are included and how are they organized?

754 skills organized into 26 security domains. Each skill includes metadata, framework mappings, technical workflows, helper scripts, and templates.

What frameworks are the skills mapped to?

MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF. This enables alignment with multiple industry standards and compliance requirements.

How do I find skills relevant to a specific ATT&CK technique?

Use the find_by_attack_technique() function provided in the SKILL.md, which searches skill references for a specific technique ID (e.g., T1003 for Credential Dumping).

Can I use this with AI agents like Claude Code or Cursor?

Yes. The skills follow the agentskills.io standard and are designed for AI-driven security operations. Use the SkillLoader to integrate skills into your agent workflow.

How do I minimize token usage when working with 754 skills?

Use the progressive loading pattern: scan all frontmatter first (~30 tokens each) to build an index, then load only the full skill content (~500-2000 tokens) when needed.

Full instructions (SKILL.md)

Source of truth, from reason-machines/security-skills.


name: anthropic-cybersecurity-skills description: Use 754 structured cybersecurity skills mapped to MITRE ATT&CK, NIST CSF, ATLAS, D3FEND, and NIST AI RMF for AI-driven security operations triggers:

  • "help me investigate this security incident"
  • "analyze this malware sample"
  • "perform memory forensics on this dump"
  • "hunt for threats in our environment"
  • "check cloud security posture"
  • "map this attack to MITRE ATT&CK"
  • "what security skills are available"
  • "guide me through incident response"

anthropic-cybersecurity-skills

Skill by ara.so — Security Skills collection.

Overview

The Anthropic Cybersecurity Skills library provides 754 production-grade cybersecurity skills spanning 26 security domains. Each skill is structured following the agentskills.io standard and mapped to five industry frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF. This enables AI agents to perform security operations with expert-level guidance.

Installation

# Option 1: Using 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

# Option 3: Add as submodule
git submodule add https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills/cybersecurity

Directory Structure

skills/
├── {skill-name}/
│   ├── SKILL.md              # Skill definition with YAML frontmatter
│   ├── references/
│   │   ├── standards.md      # Framework mappings
│   │   └── workflows.md      # Technical procedures
│   ├── scripts/
│   │   └── *.py              # Helper scripts
│   └── assets/
│       └── *.md              # Templates and checklists

Discovering Skills

By Domain

Skills are organized into 26 domains. List all domains:

import os
import yaml

def list_domains():
    domains = {}
    for skill_dir in os.listdir('skills'):
        skill_path = f'skills/{skill_dir}/SKILL.md'
        if os.path.exists(skill_path):
            with open(skill_path, 'r') as f:
                content = f.read()
                # Extract YAML frontmatter
                if content.startswith('---'):
                    yaml_end = content.find('---', 3)
                    frontmatter = yaml.safe_load(content[3:yaml_end])
                    domain = frontmatter.get('domain', 'unknown')
                    subdomain = frontmatter.get('subdomain', 'general')
                    
                    if domain not in domains:
                        domains[domain] = {}
                    if subdomain not in domains[domain]:
                        domains[domain][subdomain] = []
                    domains[domain][subdomain].append(frontmatter['name'])
    
    return domains

# Usage
domains = list_domains()
for domain, subdomains in domains.items():
    print(f"\n{domain.upper()}")
    for subdomain, skills in subdomains.items():
        print(f"  {subdomain}: {len(skills)} skills")

By Framework Mapping

Find skills mapped to specific ATT&CK techniques:

def find_by_attack_technique(technique_id):
    """Find skills mapped to a specific ATT&CK technique"""
    matching_skills = []
    
    for skill_dir in os.listdir('skills'):
        skill_path = f'skills/{skill_dir}/SKILL.md'
        if os.path.exists(skill_path):
            with open(skill_path, 'r') as f:
                content = f.read()
                if content.startswith('---'):
                    yaml_end = content.find('---', 3)
                    frontmatter = yaml.safe_load(content[3:yaml_end])
                    
                    # Check ATT&CK mappings in references
                    refs_path = f'skills/{skill_dir}/references/standards.md'
                    if os.path.exists(refs_path):
                        with open(refs_path, 'r') as ref_file:
                            if technique_id in ref_file.read():
                                matching_skills.append({
                                    'name': frontmatter['name'],
                                    'description': frontmatter['description'],
                                    'path': skill_path
                                })
    
    return matching_skills

# Usage
skills = find_by_attack_technique('T1003')  # Credential Dumping
for skill in skills:
    print(f"{skill['name']}: {skill['description']}")

By Tags

Search skills by tags:

def search_by_tags(search_tags):
    """Find skills matching any of the provided tags"""
    results = []
    
    for skill_dir in os.listdir('skills'):
        skill_path = f'skills/{skill_dir}/SKILL.md'
        if os.path.exists(skill_path):
            with open(skill_path, 'r') as f:
                content = f.read()
                if content.startswith('---'):
                    yaml_end = content.find('---', 3)
                    frontmatter = yaml.safe_load(content[3:yaml_end])
                    
                    skill_tags = frontmatter.get('tags', [])
                    if any(tag in skill_tags for tag in search_tags):
                        results.append(frontmatter)
    
    return results

# Usage
malware_skills = search_by_tags(['malware-analysis', 'reverse-engineering'])
for skill in malware_skills:
    print(f"{skill['name']}: {', '.join(skill['tags'])}")

Loading and Executing Skills

Progressive Loading Pattern

Load only frontmatter first (low token cost), then full content when needed:

class SkillLoader:
    def __init__(self, skills_dir='skills'):
        self.skills_dir = skills_dir
    
    def scan_all_frontmatter(self):
        """Scan all skill frontmatter (~30 tokens each)"""
        skills_index = []
        
        for skill_dir in os.listdir(self.skills_dir):
            skill_path = f'{self.skills_dir}/{skill_dir}/SKILL.md'
            if os.path.exists(skill_path):
                with open(skill_path, 'r') as f:
                    content = f.read()
                    if content.startswith('---'):
                        yaml_end = content.find('---', 3)
                        frontmatter = yaml.safe_load(content[3:yaml_end])
                        frontmatter['path'] = skill_path
                        skills_index.append(frontmatter)
        
        return skills_index
    
    def load_full_skill(self, skill_name):
        """Load complete skill content (~500-2000 tokens)"""
        skill_path = f'{self.skills_dir}/{skill_name}/SKILL.md'
        
        with open(skill_path, 'r') as f:
            content = f.read()
        
        # Parse frontmatter and body
        if content.startswith('---'):
            yaml_end = content.find('---', 3)
            frontmatter = yaml.safe_load(content[3:yaml_end])
            body = content[yaml_end + 3:].strip()
            
            return {
                'metadata': frontmatter,
                'content': body,
                'references': self._load_references(skill_name),
                'scripts': self._load_scripts(skill_name)
            }
    
    def _load_references(self, skill_name):
        """Load framework mappings and workflows"""
        refs = {}
        refs_dir = f'{self.skills_dir}/{skill_name}/references'
        
        if os.path.exists(refs_dir):
            for ref_file in os.listdir(refs_dir):
                with open(f'{refs_dir}/{ref_file}', 'r') as f:
                    refs[ref_file.replace('.md', '')] = f.read()
        
        return refs
    
    def _load_scripts(self, skill_name):
        """Load helper scripts"""
        scripts = {}
        scripts_dir = f'{self.skills_dir}/{skill_name}/scripts'
        
        if os.path.exists(scripts_dir):
            for script_file in os.listdir(scripts_dir):
                with open(f'{scripts_dir}/{script_file}', 'r') as f:
                    scripts[script_file] = f.read()
        
        return scripts

# Usage
loader = SkillLoader()

# Step 1: Scan all skills (lightweight)
all_skills = loader.scan_all_frontmatter()
print(f"Found {len(all_skills)} skills")

# Step 2: Find relevant skills
memory_forensics = [s for s in all_skills if 'memory-analysis' in s.get('tags', [])]

# Step 3: Load top matches fully
for skill in memory_forensics[:3]:
    full_skill = loader.load_full_skill(skill['name'])
    print(f"\n{skill['name']}")
    print(f"Content length: {len(full_skill['content'])} chars")

Common Usage Patterns

Incident Response Workflow

def incident_response_guide(incident_type):
    """Get relevant IR skills for incident type"""
    loader = SkillLoader()
    all_skills = loader.scan_all_frontmatter()
    
    # Map incident types to skill domains
    incident_mappings = {
        'ransomware': ['malware-analysis', 'incident-response', 'forensics'],
        'data_breach': ['threat-hunting', 'forensics', 'cloud-security'],
        'phishing': ['email-security', 'threat-intelligence', 'endpoint-security'],
        'insider_threat': ['behavior-analytics', 'iam', 'forensics']
    }
    
    relevant_tags = incident_mappings.get(incident_type, [])
    relevant_skills = [
        s for s in all_skills 
        if any(tag in s.get('tags', []) for tag in relevant_tags)
    ]
    
    # Prioritize by subdomain
    prioritized = sorted(
        relevant_skills,
        key=lambda s: (
            s.get('subdomain') == 'incident-response',
            len(set(s.get('tags', [])) & set(relevant_tags))
        ),
        reverse=True
    )
    
    return prioritized[:5]

# Usage
ransomware_skills = incident_response_guide('ransomware')
for skill in ransomware_skills:
    print(f"- {skill['name']}: {skill['description']}")

ATT&CK Technique Coverage

def check_attack_coverage(technique_id):
    """Check which skills cover a specific ATT&CK technique"""
    loader = SkillLoader()
    
    coverage = []
    for skill_dir in os.listdir('skills'):
        refs_path = f'skills/{skill_dir}/references/standards.md'
        if os.path.exists(refs_path):
            with open(refs_path, 'r') as f:
                content = f.read()
                if technique_id in content:
                    skill = loader.load_full_skill(skill_dir)
                    coverage.append({
                        'name': skill['metadata']['name'],
                        'description': skill['metadata']['description'],
                        'domain': skill['metadata']['subdomain']
                    })
    
    return coverage

# Usage
t1003_coverage = check_attack_coverage('T1003')  # Credential Dumping
print(f"Skills covering T1003: {len(t1003_coverage)}")
for skill in t1003_coverage:
    print(f"  {skill['domain']}: {skill['name']}")

Multi-Framework Compliance Check

def compliance_mapper(skill_name):
    """Show all framework mappings for a skill"""
    loader = SkillLoader()
    skill = loader.load_full_skill(skill_name)
    
    frameworks = {
        'MITRE ATT&CK': skill['metadata'].get('attack_techniques', []),
        'NIST CSF 2.0': skill['metadata'].get('nist_csf', []),
        'MITRE ATLAS': skill['metadata'].get('atlas_techniques', []),
        'MITRE D3FEND': skill['metadata'].get('d3fend_techniques', []),
        'NIST AI RMF': skill['metadata'].get('nist_ai_rmf', [])
    }
    
    print(f"\nFramework mappings for: {skill_name}\n")
    for framework, mappings in frameworks.items():
        if mappings:
            print(f"{framework}:")
            for mapping in mappings:
                print(f"  - {mapping}")

# Usage
compliance_mapper('performing-memory-forensics-with-volatility3')

Working with Skill Scripts

Many skills include helper scripts in scripts/ directories:

import subprocess
import json

def execute_skill_script(skill_name, script_name, **kwargs):
    """Execute a skill's helper script with arguments"""
    script_path = f'skills/{skill_name}/scripts/{script_name}'
    
    if not os.path.exists(script_path):
        raise FileNotFoundError(f"Script not found: {script_path}")
    
    # Build command with arguments
    cmd = ['python', script_path]
    for key, value in kwargs.items():
        cmd.extend([f'--{key}', str(value)])
    
    # Execute
    result = subprocess.run(cmd, capture_output=True, text=True)
    
    return {
        'stdout': result.stdout,
        'stderr': result.stderr,
        'returncode': result.returncode
    }

# Usage example with memory forensics skill
result = execute_skill_script(
    'performing-memory-forensics-with-volatility3',
    'process.py',
    dump_file='/path/to/memory.dmp',
    plugin='windows.pslist'
)

if result['returncode'] == 0:
    print(result['stdout'])
else:
    print(f"Error: {result['stderr']}")

Environment Configuration

Skills that require API keys or credentials reference environment variables:

# .env file for skills requiring external services
export VIRUSTOTAL_API_KEY=your_vt_key_here
export SHODAN_API_KEY=your_shodan_key_here
export MISP_URL=https://your-misp-instance.com
export MISP_API_KEY=your_misp_key_here
export SPLUNK_HOST=your-splunk-host
export SPLUNK_TOKEN=your_splunk_token

Load in Python:

import os
from dotenv import load_dotenv

load_dotenv()

# Skills will reference these
vt_key = os.getenv('VIRUSTOTAL_API_KEY')
misp_url = os.getenv('MISP_URL')

Integration Examples

With Claude Code / Cursor

Place in your project's .claud/ or .cursorrules:

# Cybersecurity Skills Context

This project has access to 754 cybersecurity skills in the `skills/` directory.

When I ask security questions:
1. Scan skill frontmatter in skills/*/SKILL.md
2. Match by domain, subdomain, or tags
3. Load top 3 relevant skills fully
4. Follow the Workflow sections step-by-step
5. Verify results using Verification sections

Example: "analyze this memory dump" 
→ load performing-memory-forensics-with-volatility3/SKILL.md
→ execute Volatility3 commands from Workflow
→ validate findings using Verification checklist

With Custom AI Agent (Python)

class CybersecurityAgent:
    def __init__(self, skills_dir='skills'):
        self.loader = SkillLoader(skills_dir)
        self.skill_index = self.loader.scan_all_frontmatter()
    
    def handle_query(self, user_query):
        """Process security query using relevant skills"""
        # Step 1: Find relevant skills
        relevant = self._match_skills(user_query)
        
        # Step 2: Load top matches
        top_skills = [
            self.loader.load_full_skill(s['name']) 
            for s in relevant[:3]
        ]
        
        # Step 3: Extract workflow steps
        workflows = []
        for skill in top_skills:
            content = skill['content']
            # Extract Workflow section
            if '## Workflow' in content:
                start = content.index('## Workflow')
                end = content.index('##', start + 1) if '##' in content[start + 1:] else len(content)
                workflows.append(content[start:end])
        
        return {
            'matched_skills': [s['metadata']['name'] for s in top_skills],
            'workflows': workflows,
            'framework_mappings': self._get_mappings(top_skills)
        }
    
    def _match_skills(self, query):
        """Simple keyword matching (replace with semantic search)"""
        query_lower = query.lower()
        scores = []
        
        for skill in self.skill_index:
            score = 0
            desc = skill['description'].lower()
            tags = ' '.join(skill.get('tags', [])).lower()
            
            # Score by keyword matches
            for word in query_lower.split():
                if word in desc:
                    score += 2
                if word in tags:
                    score += 1
            
            if score > 0:
                scores.append((score, skill))
        
        return [s for _, s in sorted(scores, reverse=True)]
    
    def _get_mappings(self, skills):
        """Extract framework mappings from loaded skills"""
        mappings = {
            'attack': set(),
            'nist_csf': set(),
            'atlas': set()
        }
        
        for skill in skills:
            meta = skill['metadata']
            mappings['attack'].update(meta.get('attack_techniques', []))
            mappings['nist_csf'].update(meta.get('nist_csf', []))
            mappings['atlas'].update(meta.get('atlas_techniques', []))
        
        return {k: list(v) for k, v in mappings.items()}

# Usage
agent = CybersecurityAgent()
response = agent.handle_query("investigate credential dumping attack")

print("Matched skills:", response['matched_skills'])
print("\nATT&CK Techniques:", response['framework_mappings']['attack'])
print("\nFirst workflow:")
print(response['workflows'][0][:500])

Troubleshooting

Skill Not Found

def verify_skill_exists(skill_name):
    """Check if skill exists and is properly formatted"""
    skill_path = f'skills/{skill_name}/SKILL.md'
    
    if not os.path.exists(skill_path):
        print(f"❌ Skill not found: {skill_path}")
        return False
    
    with open(skill_path, 'r') as f:
        content = f.read()
    
    if not content.startswith('---'):
        print(f"❌ Invalid format: missing YAML frontmatter")
        return False
    
    try:
        yaml_end = content.find('---', 3)
        frontmatter = yaml.safe_load(content[3:yaml_end])
        required_fields = ['name', 'description', 'domain', 'subdomain']
        
        for field in required_fields:
            if field not in frontmatter:
                print(f"❌ Missing required field: {field}")
                return False
        
        print(f"✅ Skill valid: {skill_name}")
        return True
        
    except Exception as e:
        print(f"❌ YAML parse error: {e}")
        return False

Framework Mapping Missing

If a skill doesn't show framework mappings, check references/standards.md:

def audit_framework_mappings(skill_name):
    """Check which framework mappings exist for a skill"""
    refs_path = f'skills/{skill_name}/references/standards.md'
    
    if not os.path.exists(refs_path):
        print(f"⚠️  No references/standards.md found")
        return
    
    with open(refs_path, 'r') as f:
        content = f.read()
    
    frameworks = {
        'ATT&CK': r'T\d{4}',
        'NIST CSF': r'[A-Z]{2}\.[A-Z]{2}',
        'ATLAS': r'AML\.T\d{4}',
        'D3FEND': r'D3-[A-Z]+',
        'AI RMF': r'[A-Z]+-\d+\.\d+'
    }
    
    import re
    for name, pattern in frameworks.items():
        matches = re.findall(pattern, content)
        if matches:
            print(f"✅ {name}: {', '.join(set(matches))}")
        else:
            print(f"⚠️  {name}: No mappings found")

Key Features Summary

  • 754 skills across 26 security domains
  • 5 framework mappings: ATT&CK, NIST CSF, ATLAS, D3FEND, AI RMF
  • Progressive loading: scan frontmatter (~30 tokens), load full content only when needed
  • Structured workflows: step-by-step procedures in every skill
  • Helper scripts: working Python scripts in scripts/ directories
  • Framework standards: complete mappings in references/standards.md
  • agentskills.io compliant: works with 26+ AI coding platforms

License

Apache 2.0 — see repository for full license text.

Contributing

Contributions welcome following the project's CONTRIBUTING.md guidelines. All skills must include YAML frontmatter, structured Markdown sections, and framework mappings.