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Found 121 Skills
Use when you need to locate the current spec pack (FEATURE_DIR) in the Spec process of sdlc-dev, avoid reading or writing requirements/*.md in the wrong directory, or encounter issues such as "misreading context/writing to the wrong file/non-compliant branch".
Use after QA strategy and test-case synthesis to build the requirements-to-test traceability matrix, identify missing coverage and test blockers, and score readiness for QA 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.
Use this when you need to execute the AI SDLC (Spec Pack) process in the sdlc-dev repository, select/chain together skills from the demand side (raw/solution/prd/prototype/demo) and implementation side (plan/execute/finishing), and use guardrails to avoid context drift, incorrect directory writes, or skipping critical steps under pressure.
Use when QA scope and strategy are defined and you need to generate detailed, executable test cases plus smoke, regression, and user acceptance suites tied to requirements, roles, workflows, and risks. 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 stories, specs, BRDs, APIs, workflows, or equivalent delivery artifacts exist and you need to review them for testability, missing business rules, unclear behavior, scope ambiguity, and QA blocking gaps before generating 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 backend validation workflow. Use when an AI assistant needs to validate Go, SQL, API, provider integration, SDD, or documentation changes in this repository and choose focused deterministic checks without running unrelated expensive tests by default. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Use when you need to perform I2 (Implementation Execution) in the Spec Pack of sdlc-dev, implement in batches with `{FEATURE_DIR}/implementation/plan.md` as the only SSOT, run minimal verification, write back audit information, and report at batch checkpoints; stop immediately when encountering blocking or clarification required items.
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 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.
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.
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...