Attack Tree Construction

AUTHORIZED USE ONLY: Use this skill only for authorized security assessments, defensive validation, or controlled educational environments.

Attack Tree Construction

Systematic attack path visualization and analysis.

Use this skill when

• Visualizing complex attack scenarios
• Identifying defense gaps and priorities
• Communicating risks to stakeholders
• Planning defensive investments or test scopes

Do not use this skill when

• You lack authorization or a defined scope to model the system
• The task is a general risk review without attack-path modeling
• The request is unrelated to security assessment or design

Instructions

• Confirm scope, assets, and the attacker goal for the root node.
• Decompose into sub-goals with AND/OR structure.
• Annotate leaves with cost, skill, time, and detectability.
• Map mitigations per branch and prioritize high-impact paths.
• If detailed templates are required, open resources/implementation-playbook.md.

Safety

• Share attack trees only with authorized stakeholders.
• Avoid including sensitive exploit details unless required.

Resources

• resources/implementation-playbook.md for detailed patterns, templates, and examples.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache compliance check results to avoid re-running expensive AWS API calls. Retrieve prior audit findings to track remediation progress across sessions.

# Check for prior security context before starting
python3 execution/memory_manager.py auto --query "prior security audit results for Attack Tree Construction"

Storing Results

After completing work, store security decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Audit findings: 3 critical IAM misconfigurations found and remediated" \
  --type technical --project <project> \
  --tags attack-tree-construction security

Multi-Agent Collaboration

Share security findings with other agents so they avoid introducing vulnerabilities in their code changes.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Completed security audit — 3 critical findings fixed, compliance score 94%" \
  --project <project>

Signed Audit Trail

All security findings are cryptographically signed with the agent's Ed25519 identity, providing tamper-proof audit logs for compliance reporting.

Semantic Cache for Compliance

Cache compliance check results (semantic_cache.py) to avoid redundant AWS API calls. Cache hit at similarity >0.92 returns prior results instantly.