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Found 4 Skills
Use when the user wants to validate that implemented code matches its specifications, generate integration tests from feature files, or check if code still satisfies existing scenarios. Trigger after implementation completes a feature. Also use when the user asks "does the code do what we specified?" or "generate tests from the feature files".
Scaffold and maintain a reusable research → design → plan → orchestrate → act folder for any non-trivial work — software features, marketing campaigns, org changes. Drops a domain-agnostic spine (00-README · 01-plan · 02/03 research · 04-discussion newest-first · 05-tracking · 09-orchestration · artifact/board.html plan-board) plus stateless action-skills that augment the docs in place without clobbering hand-written prose. Composes ikenga-artifact-builder, huashu-design, frontend-design, ikenga-pkg-builder when present; degrades gracefully when not. Profile-driven: `software` (rich default, code work), `general` (lean, non-code — campaigns, org changes), and `content` (editorial/marketing with key art). TRIGGER when the user asks to start a real plan for non-trivial work ("plan a feature," "scaffold a plan folder," "set up groundwork for…"), references an existing plans/ folder by groundwork structure, or runs any of these actions: groundwork init / research / design / review / clarify / orchestrate / refresh-board / refresh-living-spec / status. DO NOT TRIGGER for one-off code changes, single-document writeups, ADRs, or content that fits in a single markdown file — those don't need a multi-doc plan folder. If the user just wants a single artifact (dashboard, mockup), route to ikenga-artifact-builder instead.
Use BEFORE brainstorming — when the user wants to capture WHAT a feature should do as Gherkin scenarios. Trigger when the user says "I need to build X", "let's spec this out", "what should this feature do?", or wants to formally capture a new edge case or bug as a BDD scenario.
A Metabase AI-readiness coach, covering the *data groundwork* that makes AI answers trustworthy: modeling with Transforms, table and field metadata, the Glossary, saved Metrics, and marking canonical content. Use this skill whenever the user wants to check if their data is ready for AI, prep the underlying data for Metabot or the Metabase MCP server, work through an AI readiness checklist, or audit their data modeling/metadata/metrics before turning on AI features. Trigger it even if the user just says "is my data AI ready?", "let's get set up for Metabot", or "run the AI readiness checklist" in a Metabase context. **Not this skill** if the question is about controlling who may use AI, capping AI spend, restricting what Metabot can see, auditing AI usage, or passing a security review — that's `ai-governance-checklist`. Rough test: this skill is about whether the data is good enough; that one is about who gets to point AI at it.