About stride-analysis-patterns
A threat-modeling skill for teams identifying and documenting security threats with the STRIDE methodology. It guides architecture reviews and threat analysis while emphasizing evidence, assumptions, and environment-specific validation.
Key features
- 01STRIDE-based threat identification
- 02Guidance for architecture analysis
- 03Security design review support
- 04Threat documentation and audit preparation
- 05Implementation playbook reference
- 06Explicit modeling limitations
Use cases
- 01Starting a threat-modeling session
- 02Reviewing an existing system architecture
- 03Assessing security design decisions
- 04Preparing threat records for audits
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/stride-analysis-patternsin Claude Code to confirm it loaded.
Bundled filesresources/implementation-playbook.md
Details
From the project READMESTRIDE Analysis Patterns
Systematic threat identification using the STRIDE methodology.
Use this skill when
- Starting new threat modeling sessions
- Analyzing existing system architecture
- Reviewing security design decisions
- Creating threat documentation
- Training teams on threat identification
- Compliance and audit preparation
Do not use this skill when
- The task is unrelated to stride analysis patterns
- 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.mdfor detailed patterns and examples.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Modeling and scoring limits
The Python helpers produce prompts from user-supplied model data; they do not inspect software or establish actual vulnerabilities, control effectiveness or compliance. Impact×likelihood and all severities/deadlines in examples are illustrative ordinal policy choices, not CVSS or calibrated probabilities. Record evidence, assumptions, assets, trust boundaries, owners and residual-risk acceptance for each candidate. Equal numeric trust levels can still cross tenant/identity/administrative boundaries; model explicit boundary membership rather than relying solely on inequality. Encryption does not prevent authorized-but-abusive access, and authentication lockouts can create denial-of-service risks. Missing elements or unmodeled interaction types must be surfaced, not silently treated as no threats. Templates are partial independent blocks; validate imports, duplicate IDs and structured model inputs before use. Keep secret-bearing diagrams/logs private and do not run attack simulations without separate authorization.