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attack-tree-construction

Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

techwavedev/agi-agent-kit0installs4stars

SKILL.md

Full skill instructions

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.
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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.

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