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Found 12 Skills
Check whether a design is complete enough to move into implementation planning. Use when a design appears mostly done and needs a final readiness review for missing branches, weak assumptions, unresolved risks, failure handling, validation gaps, or non-functional omissions. Trigger before invoking writing-plans or when the user asks whether the current design is ready to implement. Do not use as a general design-document audit for external docs or as a replacement for initial design work.
Checks if a community pull request is ready for human review. Verifies CLA signature, PR title format, description completeness, test coverage, and cubic-dev-ai issues, then triages to the right Linear team or recommends a close. Use when given a PR number or branch name to review, or when the user says /community-pr-readiness-check, or asks to check if a PR is ready for review.
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.
A Metabase AI-governance coach, covering the *controls and rollout* side of AI: who may use Metabot, what it can see, what it costs, where the model runs, and the audit trail. Use this skill whenever the user wants to roll out AI analytics safely, control who can use Metabot or what it can see, set spend or token limits on AI, restrict Metabot's system prompt, audit AI usage, evaluate bring-your-own model or self-hosting options for AI, or prep for a security review of AI features. Trigger it even if the user just says "how do we control AI access", "can we limit what Metabot sees", "we need an AI security review", or "run the AI governance checklist" in a Metabase context. **Not this skill** if the question is about whether the underlying data is modeled, documented, and trustworthy enough for AI — Transforms, metadata, the Glossary, Metrics, the Library — that's `ai-readiness-checklist`. Rough test: this skill is about who gets to point AI at the data; that one is about whether the data is good enough.
Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".
Validates all artifacts are complete and consistent for release. Checks spec kit artifacts, documentation, versioning, and API docs. Run before creating a release to ensure nothing is missing.
Orchestrate a disciplined pre-trade workflow by routing a watchlist or trade idea through the minimum set of underlying skills needed to decide whether the trade is ready, not ready, or should be resized or reworked first.
Am I set up? Machine readiness (Node, the tenant connection, the cached contract, the CLI path, the skills, the SDK, the optional replica) plus tenant readiness from the contract's ten per-team checks, in one verdict: what passes, what is blocked, what is merely waiting, and who can click what. Use when someone asks "am I set up", "what is missing", "why does nothing happen", "is the replica running", or right after connecting a new machine. Reports; never repairs.
Nuclear-grade 16-agent pre-publish release gate. Runs /get-unpublished-changes to detect all changes since last npm release, spawns up to 10 ultrabrain agents for deep per-change analysis, invokes /review-work (5 agents) for holistic review, and 1 oracle for overall release synthesis. Use before EVERY npm publish. Triggers: 'pre-publish review', 'review before publish', 'release review', 'pre-release review', 'ready to publish?', 'can I publish?', 'pre-publish', 'safe to publish', 'publishing review', 'pre-publish check'.
Create GitHub pull requests with readiness checks, auto-drafted titles and descriptions, and remote push handling. Use when: create PR, pull request, open PR, submit PR, create pull request, PR for review, push and PR, pull-request, /pull-request.
Validate readiness to advance between development phases. Produces a PASS/CONCERNS/FAIL verdict with specific blockers and required artifacts. Use when user says 'are we ready to move to X', 'can we advance to production', 'check if we can start the next phase', 'pass the gate'.
Review whether a GitHub issue is complete enough for its current stage and decide the next PR or closure action. Use before PR, before closing an issue, after merge, or when auditing a closed issue for regressions or missing scope. For pre-PR checks, use CodeRabbit CLI review; when an open PR exists and corrective review work is needed, use autofix.