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Found 62 Skills
Transform user stories and specifications into precise, verifiable Gherkin acceptance criteria using Given/When/Then syntax with Happy Path, Sad Path, and edge case scenarios. Use when asking for acceptance criteria, Gherkin scenarios, BDD criteria, test scenarios, or AC generation.
Use when the user wants to implement a development plan from docs/plans/<FR-N>.md against the target codebase. Drives the task loop — reads the plan, implements each `[ ]` task as a vertical slice (code + test + typecheck + lint), commits per task with conventional commits, marks `[x]` in the same commit, then finalizes by proposing a PR. Triggers on "execute the plan", "implement docs/plans/FR-001.md", "run the dev loop on FR-001", "ship FR-001", "/execute FR-N".
Design state machines, orchestration workflows, saga patterns, and resilience strategies for distributed systems, AI agents, and complex async processes. Use when asking for a workflow, state machine, orchestration design, saga, HITL checkpoint, or process resilience strategy.
Write, rewrite, or normalize structured `*.spec.md` specification files for agent-driven development. Use this whenever the user asks for a spec, requirements, acceptance criteria, implementation-ready documentation, feature definition before coding, or wants an existing idea/codebase turned into an actionable spec, even if they do not explicitly say "spec".
Use when the user wants to bring UI designs into a project for a PRD requirement. Identifies the screens/states a requirement needs, helps the user generate them via Stitch or Claude Design (or import existing exports), and places HTML + screenshot pairs under docs/designs/<FR-N>-<slug>.{html,png} so implementation can reference them. Triggers on "import these designs", "add screens for FR-001", "set up the designs for this requirement", "vibe design this screen", "/designs FR-N".
Use when the user wants to author, refine, or audit a Product Requirements Document for AI coding agents. Walks through an 8-phase pipeline (Socratic discovery → PRD draft → acceptance criteria → adversarial review → task decomposition → AI-readiness gate → test generation → handoff). Triggers on "write a PRD", "spec this feature", "draft requirements", "prepare X for Claude/Cursor/Copilot/Windsurf/Aider to build", "audit my PRD", "is this PRD AI-ready", "score this spec".
Disciplined spec-driven test-driven development workflow for building software with AI coding agents. Transforms ambiguous requests into verified implementations through structured specification, test derivation, and strict TDD. Handles greenfield projects, brownfield enhancements (with or without existing tests), refactors, and complex bug fixes with workflow-specific guidance for each. Use when the user requests a new feature, module, enhancement, refactor, API, data pipeline, CLI tool, or system with multiple requirements, edge cases, or unclear specifications. Also use for complex bug fixes requiring root cause analysis. Triggers on phrases like "add a feature", "implement", "build a new module", "build an API", "build a CLI", "build a data pipeline", "refactor", "fix this bug", "write tests for", "TDD", "test-first", "the requirements are unclear", "characterization tests", or "spec this out". Triggers when modifying code with adjacent test files (`tests/`, `*_test.py`, `*.test.ts`, `*.spec.ts`, `spec/`, `__tests__/`) or test framework config (pytest.ini, jest.config.*, go.mod with testing imports, Cargo.toml with [dev-dependencies], package.json with a test script). Triggers when the user mentions edge cases, invariants, acceptance criteria, EARS notation, or red-green-refactor. Do NOT use for simple one-line fixes, cosmetic changes, formatting, renames, dependency bumps, or tasks where requirements are already fully specified with tests provided.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
Design complete API contracts in OpenAPI 3.0/3.1 YAML with endpoints, schemas, security, pagination, error handling, and RFC 7807 problem details. Use when asking to design an API, create an OpenAPI spec, define API endpoints, write API contracts, or generate a Swagger specification.
Use when the user wants to bootstrap a target codebase for AI-driven development with Claude Code. Generates a concise CLAUDE.md grounded in the actual stack (build tools, test runner, code style), creates a docs/ folder skeleton (designs/, prd/, plans/), and seeds conventions (conventional commits, plan-checkbox format, where designs and PRDs live). Triggers on "init Claude in this repo", "set up CLAUDE.md", "bootstrap docs folder", "prepare this project for Claude Code", "scaffold AI dev workflow", "/init this project".