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Found 107 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 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 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.
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.
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.
Use when you need to execute I2 (Implementation Execution) in the Spec Pack of sdlc-dev, using `{FEATURE_DIR}/implementation/plan.md` as the sole SSOT to implement in batches, run minimal validation, write back audit information, and report at batch checkpoints; stop immediately when encountering blocks or clarification items.
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 this when you need to initialize a new Spec Pack in the AI SDLC workflow of this repository (create a three-digit numbered branch and the `.aisdlc/specs/{num}-{short-name}` directory), or when you are unsure about input parsing, short name rules, UTF-8 BOM file path parameter passing, script invocation methods, or output artifacts when executing `spec-init`.
Portable AI SDLC shared-helper runtime. Use when an AI assistant installs, verifies, diagnoses, or repairs project-scoped AI SDLC skills whose deterministic scripts depend on shared state, artifact, context, path, TOON, migration, or index modules. This is an installation dependency, not a lifecycle entry point.
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 context-aware navigation workflow. Use when an AI assistant needs to determine what to do next, select the right installed skill, start or resume a feature, explain blockers, inspect available capabilities, or provide evidence-backed required and optional next actions from repository state. Supports `--quick-flow` for compact guidance and `--full-flow` for stricter context verification.
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.