Loading...
Loading...
Found 82 Skills
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.
AI SDLC versioned policy-as-code workflow. Use when an AI assistant needs to resolve layered delivery policy, evaluate an action with explainable rules and gates, protect organization minimums from weaker overrides, apply or reject an expiring waiver, or select a reusable assurance profile. Supports `--quick-flow` for deterministic evaluation and `--full-flow` for strict owner and exception review.
Use this when you need to execute R4 (generate an interactive Demo project based on requirements/prototype.md) in the sdlc-dev product requirement Spec process, and need to avoid skipping spec-context, proceeding when prototype.md is missing or the runnable Demo project root directory is missing, or creating custom pages/directories that lead to untraceability and inability to close the loop.
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...
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.
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.
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.
AI SDLC code review workflow. Use when an AI assistant is asked to review a diff, PR, branch, commit, staged changes, or completed implementation against SDD requirements, tests, API contracts, security, and scope discipline. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
AI SDLC package trust and privacy-preserving local metrics workflow. Use when an AI assistant needs to verify package origin, file integrity, harness compatibility, declared capabilities, provenance evidence, or generate reproducible aggregate run, retry, budget, coverage, and freshness metrics without collecting source, prompts, commands, or diffs. Supports `--quick-flow` and `--full-flow`.
AI SDLC reusable quality-lens workflow. Use when an AI assistant needs to challenge a requirement, design, plan, test strategy, change, or delivery artifact through pre-mortem, adversarial, edge-case, stakeholder-conflict, reversibility, abuse-case, operational-failure, or assumption lenses and finalize evidence-backed findings with ownership and traceability. Supports `--quick-flow` for selected high-value lenses and `--full-flow` for the complete applicable registry.
AI SDLC repository delivery-graph and evidence-freshness workflow. Use when an AI assistant needs to index lifecycle traceability, resolve end-to-end paths, report gaps or orphans, register evidence identity, propagate stale dependencies, or calculate fresh evidence coverage. Supports `--quick-flow` for deterministic local analysis and `--full-flow` for strict trace and evidence review.
Use this when you need to execute R2 in the sdlc-dev product requirement Spec process, transcribe requirements/solution.md into a deliverable, acceptable, and testable requirements/prd.md, while avoiding guessing file paths, continuing generation when solution.md is missing, or using "Pending Questions/Open Questions" to replace the verification checklist.