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Found 31 Skills
Turn ambiguous coding requests into verified Codex implementation specs. Use when the user wants requirements, an implementation plan, or a spec before coding; include source checks, needed ADRs, and agreed delivery. Do not use for already specified direct implementation or memory cleanup.
Validate that a branch or pull request implementation matches introduced product, technical, security, and related specs. Use when reviewing or finishing a spec-driven change and resolving mismatches between checked-in specs and implementation.
Create or update a feature specification from a natural language feature description.
Step-by-step process for adopting Cavekit on an existing codebase. Covers the 6-step brownfield process, bootstrap prompt design, spec validation against existing behavior, and the decision between brownfield adoption vs deliberate rewrite. Trigger phrases: "brownfield", "existing codebase", "add Cavekit to existing project", "adopt Cavekit", "layer kits on code", "retrofit kits"
Automated TSPEC generation from SPEC - generates test specifications for UTEST, ITEST, STEST, FTEST with TASKS-Ready scoring
Analyze a functional spec to determine if it is too complex for the renderer. A spec is too complex if it would produce more than 200 lines of code changes. Use after drafting a new functional spec (during `add-functional-spec`, or per spec during `add-functional-specs`) to verify it fits within the complexity limit before inserting it.
Validate Test Specification (TSPEC) documents against Layer 10 schema standards
Bring existing tracker issues up to standard without implementing anything — applies missing SDLC labels, clarifies laconic issues (analyzing attached screenshots), posts a read-only implementation-prep analysis, and flags feature issues lacking a covering spec (optionally authoring one with --write-missing-specs). Single issue by id, or a batch (default — last ~25 open, worst-described first). Idempotent and claim-aware. Use for "triage the backlog", "clean up issue 123".
Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical documentation, statistics, and general factual statements.
Use when extracting or generating an OpenAPI spec from existing API code. Triggers on "extract OpenAPI", "code first", "generate spec from code", "FastAPI OpenAPI", "Spring Boot OpenAPI", "NestJS swagger", "Django OpenAPI", "Flask OpenAPI", "Rails swagger", "Laravel OpenAPI", "existing API code"
Validate specifications, implementations, constitution compliance, or understanding. Includes spec quality checks, drift detection, and constitution enforcement.
Resolve a conflict between two functional specs in a ***plain spec file. Use when conformance tests for a previously passing spec start failing after a new spec is rendered, or when a potential conflict is detected while adding a new functional spec (via `add-functional-spec` or `add-functional-specs`).