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Found 107 Skills
Use after story and spec synthesis to perform a strict delivery handoff review, identify remaining gaps or contradictions, and score readiness for engineering and cross-functional execution. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Use when working-backwards discovery is complete and you need to synthesize a PRFAQ, FAQ package, and business requirements document tied to business value, scenarios, and testable acceptance logic. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
AI SDLC test-case-driven testing workflow. Use when an AI assistant is asked to derive test cases, create a test plan, expand coverage, or write tests from explicit scenarios before implementing unit, service, transport, or integration tests. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Use after QA strategy and test-case synthesis to build the requirements-to-test traceability matrix, identify missing coverage and test blockers, and score readiness for QA execution. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
AI SDLC repository spec-driven development workflow. Use when an AI assistant receives a medium or large feature, refactor, API change, architecture change, provider integration change, or any request that must follow requirements, design, test cases, QA planning, tasks, implementation, and validation. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Optional AI SDLC user-experience workflow. Use when an AI assistant needs to define actors, goals, user journeys, interaction steps, loading/empty/error/success states, recovery behavior, content intent, accessibility requirements, or UX acceptance evidence and route them into traceable human and machine artifacts. Supports `--quick-flow` for a focused journey slice and `--full-flow` for strict state, accessibility, and acceptance coverage.
Use when you have confirmed the scope of Discover (P0/P1/P2), and now need to quickly build the Level-0 North Star (memory) and Level-1 map layer index skeleton (components/products) under `.aisdlc/project/`, so that you can supplement evidence by module later without double writing and drift.
Generate Planning & Management documentation for SDLC projects. Covers Project Vision & Scope, SDP, SCMP, QA Plan, Risk Plan, SRS, and Feasibility Study. Use when starting a new project, conducting project governance, or establishing the planning...
AI SDLC security testing workflow. Use when an AI assistant is asked for OWASP review, security testing, abuse-case analysis, authz/authn review, input validation review, secret exposure review, or security-focused validation of a diff, endpoint, workflow, or subsystem. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Optional AI SDLC evidence-council workflow. Use when an AI assistant needs to review a high-impact topic through several explicit perspectives, orchestrate simulated lenses or truly independent reviewer executions, and synthesize evidence-backed agreements, conflicts, proposals, owners, and unresolved questions without allowing panel members to rewrite authoritative artifacts. Supports `--quick-flow` for labeled simulated review and `--full-flow` for stricter panel and evidence coverage.
AI SDLC commit preparation workflow. Use when an AI assistant is asked to commit repository changes, prepare an auditable commit message, stage files safely, include SDD traceability, verify branch/spec alignment, or verify the working tree before committing. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Use when you need to execute I1 (Implementation Plan) in the Spec Pack of sdlc-dev, convert requirements/design into `{FEATURE_DIR}/implementation/plan.md` (the single source of truth for execution checklist and status, SSOT), and provide an unambiguous task list for subsequent I2 execution.