# 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.

- Skill: `techwavedev/attack-tree-construction` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add techwavedev/attack-tree-construction`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/attack-tree-construction/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/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-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/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.

```bash
# 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:

```bash
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.

```bash
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.

<!-- AGI-INTEGRATION-END -->