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Found 107 Skills
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.
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.
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 business analysis workflow. Use when an AI assistant needs to frame a feature or change before implementation, derive actors, workflows, business rules, assumptions, acceptance criteria, and richer spec context for requirements and design. 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 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 approvals, sandbox, and command rule workflow. Use when an AI assistant needs to decide whether to request escalated permissions, explain sandbox failures, propose prefix_rule approvals, avoid unsafe command patterns, or document why a command was or was not rerun outside the sandbox. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
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.
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 (SSOT) for execution checklist and status), and provide an unambiguous task list for subsequent I2 execution.
Used when the current Spec Pack needs to be discarded/revoked due to major requirement issues, and the corresponding `.aisdlc/specs/{branch}` directory as well as local and remote branches must be deleted. Meanwhile, you need to output the deletion list and ask the user for double confirmation before performing the deletion.
Use after PRFAQ and BRD creation to run a strict final quality review, identify gaps or contradictions, and assign a readiness score before design or development starts. 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.