PromptOperations
Define requirements for an OpenClaw integration
Document a proposed integration's data needs, actions, credentials, and review boundaries.
◈OpenClaw
By Dave AIDave AI original
MIT · FreeView prompt
Installable skillApache-2.0
Security Requirement Extraction
Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.
instructions.md
--- name: security-requirement-extraction-de2726cfb719 description: "Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases." risk: unknown source: community date_added: "2026-02-27" --- # Security Requirement Extraction Transform threat analysis into actionable security requirements. ## Use this skill when - Converting threat models to requirements - Writing security user stories - Creating security test cases - Building security acceptance criteria - Compliance requirement mapping - Security architecture documentation ## Do not use this skill when - The task is unrelated to security requirement extraction - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed patterns 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 Security Requirement Extraction" ``` ### 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 security-requirement-extraction 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: security-requirement-extraction-de2726cfb719 description: "Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases." risk: unknown source: community date_added: "2026-02-27" --- # Security Requirement Extraction Transform threat analysis into actionable security requirements. ## Use this skill when - Converting threat models to requirements - Writing security user stories - Creating security test cases - Building security acceptance criteria - Compliance requirement mapping - Security architecture documentation ## Do not use this skill when - The task is unrelated to security requirement extraction - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Resources - `resources/implementation-playbook.md` for detailed patterns 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 Security Requirement Extraction" ``` ### 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 security-requirement-extraction 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 -->
COMPATIBILITY & REFERENCES
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.