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Found 46 Skills
Optional AI SDLC research workflow. Use when an AI assistant needs to investigate a customer, market, domain, technology, regulation, competitor, operational question, or implementation uncertainty and produce a routed source inventory plus synthesized findings with confidence, limitations, open questions, and delivery trace targets. Supports `--quick-flow` for focused evidence and `--full-flow` for multi-source and source-diversity gates.
Use when goals, capabilities, and epics are defined and you need to decompose them into features, user stories, acceptance summaries, and cross-functional delivery tasks. 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 resumable task-runtime workflow. Use when an AI assistant needs to start or resume a versioned delivery run, select dependency-ready work, enforce step, failure, and token budgets, retry safely, persist exact stop reasons, recover state from an append-only journal, or require commit evidence at task boundaries. Supports `--quick-flow` for deterministic local runs and `--full-flow` for strict transition review.
AI SDLC declarative workflow planning. Use when an AI assistant needs to validate a versioned workflow, plan typed dependency steps, evaluate bounded conditions, enforce approval gates, attach deterministic hooks, detect cycles, or create safe dependency waves with sequential fallback when host concurrency or isolation is unavailable. Supports `--quick-flow` and `--full-flow`.
Use after backlog decomposition to define prioritization, MVP and release slices, sequencing, readiness, traceability, and JIRA-ready outputs, then score backlog quality for planning and estimation. 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 the delivery gap review is complete and you need to convert a clarified initiative package into epics, user stories, acceptance criteria, scenario coverage, and priority signals tied to business value. 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 a PRFAQ, BRD, or equivalent discovery package exists and you need to review it for delivery gaps, contradictions, missing business rules, and insufficient implementation handoff detail before writing user stories or specs. 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 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.
AI SDLC change-impact and lifecycle recovery workflow. Use when a requirement, acceptance criterion, decision, API contract, risk assumption, or other traced source changed after downstream artifacts were created and an AI assistant must identify stale artifacts, affected lifecycle stages, and evidence-backed reopen or revalidation actions without silently rewriting authoritative state. Supports `--quick-flow` for focused trace scanning and `--full-flow` for strict state and source-evidence gates.
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 when stories and clarified delivery context are ready and you need to produce a structured delivery specification that engineering and cross-functional teams can use for implementation planning and handoff. 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 host adapter and capability negotiation workflow. Use when an AI assistant needs to validate a host adapter manifest, map portable workflow operations to host-native operations, negotiate capabilities and limits, select deterministic semantic-preserving fallbacks, or explain why a host cannot run a plan. Supports `--quick-flow` and `--full-flow`.