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Found 107 Skills
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.
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`.
Use when working on the spec branch of sdlc-dev, when requirements are ambiguous, scope is unstable, constraints are unclear, and issues such as context drift, unfounded assumptions, multiple questions asked at once, or requests to skip the FEATURE_DIR/raw gate occur.
Use when PRFAQ, BRD, PRD, product brief, workflow, or equivalent initiative artifacts exist and you need to review them for planning gaps, unclear scope, weak priorities, missing actors, and backlog-blocking ambiguity before decomposing work. 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 planning inputs are clear enough and you need to map business goals, roles, capabilities, and outcome-oriented epics before detailed backlog decomposition. 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 evidence-backed project context and bounded task-pack workflow. Use when an AI assistant needs to onboard to a repository, detect stack and commands, map ownership and test topology, check context drift, conditionally select task sources, exclude secrets, or allocate a freshness-aware context pack within an explicit token budget. Supports `--quick-flow` for focused evidence and `--full-flow` for stricter repository coverage.
Used when you need to perform Discover (reverse engineering) on legacy projects with existing code, consolidate repository facts into `.aisdlc/project/`, and you find that AI or teams frequently guess entry points and boundaries, have duplicate writing of indexes and details, or lack evidence chains leading to repeated rework.
Use when you need to turn selected modules (P0 priority first) into single-page module SSOT at the path `.aisdlc/project/components/{module}.md`, and build authoritative entries for API/Data contracts, invariant summaries, evidence entries and structured Evidence Gaps in the same page to meet the DoD gate requirements of Discover.
Generate Testing & Quality documentation for SDLC projects. Compliant with BS ISO/IEC/IEEE 29119-3:2013 (supersedes IEEE 829:2008 and BS 7925-2:1998). Covers Software Test Plan, Test Case Specifications (with normative 29119-3 fields), V&V Plan...
AI SDLC Git-flow branching workflow. Use when an AI assistant starts implementation work, needs to create or verify a task branch, checks branch/spec alignment, or prepares to hand off a completed user-visible task to validation and commit prep. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.