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Found 46 Skills
AI SDLC QA workflow. Use when an AI assistant is asked for QA planning, acceptance validation, regression scope, exploratory checks, smoke tests, release verification, or change-focused manual validation evidence. 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 context-aware navigation workflow. Use when an AI assistant needs to determine what to do next, select the right installed skill, start or resume a feature, explain blockers, inspect available capabilities, or provide evidence-backed required and optional next actions from repository state. Supports `--quick-flow` for compact guidance and `--full-flow` for stricter context verification.
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.
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 requirements are testable enough and you need to define QA scope, coverage priorities, test strategy, suite intent, test data needs, environment dependencies, and risk-based execution focus. 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 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 architecture workflow. Use when an AI assistant needs to define system boundaries, components, interfaces, architectural constraints, alternatives, decisions, tradeoffs, risks, or validation for a feature and produce routed human and machine artifacts linked to requirements and durable decisions. Supports `--quick-flow` for focused design and `--full-flow` for strict decision, risk, and validation coverage.
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.
AI SDLC controlled change-workspace and specification-delta workflow. Use when an AI assistant needs to create or validate an isolated proposal workspace, author and validate requirement deltas, preview canonical changes, or apply and archive an explicitly approved change with rollback evidence. Supports `--quick-flow` for assumption-driven drafts and `--full-flow` for strict owner, target, evidence, and authority checks.
Use when a user needs a staged working-backwards interview to clarify the customer problem, audience, value proposition, business case, MVP, requirements, risks, and success metrics before any PRFAQ is written. 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.