Prompt
Create an operating checklist
Turn a recurring process into a clear checklist with ownership and exception handling.
ClaudeChatGPTGeminiOperations
Dave AI
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.
instructions.md
--- name: attack-tree-construction-300b1b368fd9 description: "Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders." risk: offensive source: community date_added: "2026-02-27" --- > 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 -->--- name: attack-tree-construction-300b1b368fd9 description: "Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders." risk: offensive source: community date_added: "2026-02-27" --- > 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 -->
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.