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Found 1,210 Skills
Access Slack through the global `slack` CLI for read-only workflows. Use when asked to list chats or DMs, read message history, inspect threads, fetch exact messages, or summarize recent Slack activity.
Bulk-import many components from an existing codebase to Subframe in one CLI batch. Use only when the user explicitly asks to use this exact skill. Available for select teams.
Drive a remote chrome-devtools-mcp server (typically on a tailnet) over HTTPS using the chrome-devtools CLI. Use this when the user wants to navigate, screenshot, inspect, or evaluate JavaScript on a browser running on another host (e.g. a Tailscale-connected Mac mini or a CI runner) — and you don't have a local Chrome to control. Examples of triggers ("open <url> on the lab mac", "take a screenshot of the browser on host X", "evaluate this on the remote browser").
Upgrade Prisma Next in your extension. Bumps every `@prisma-next/*` dependency to the requested target (or npm `latest`), runs the per-transition upgrade instructions for the extension SPI (middleware lifecycle, codec / migration-tools / framework-components churn, seed-migration on-disk shape), verifies the pins are correctly exact via `prisma-next-check-pins`, runs the extension's own typecheck and tests, and commits each minor step on its own. Use when the user asks to "upgrade Prisma Next" in an extension package, or to update an extension's `@prisma-next/*` deps to a new minor.
Builds React Native Nitro Modules from scratch in a monorepo. Scaffolds with Nitrogen, authors HybridObject TypeScript specs, generates native boilerplate, implements in C++/Swift/Kotlin, wires an example app, and prepares for npm publishing. Use when creating a new Nitro Module, implementing native functionality via HybridObjects, or setting up the nitrogen codegen pipeline.
Designs or reviews CLIs so coding agents can run them reliably: non-interactive flags, layered --help with examples, stdin/pipelines, fast actionable errors, idempotency, dry-run, and predictable structure. Use when building a CLI, adding commands, writing --help, or when the user mentions agents, terminals, or automation-friendly CLIs.
Explain core Contentful concepts and route users to the right implementation skill or documentation. Use when users ask conceptual questions, need terminology clarified, want help choosing between APIs (CDA/CMA/CPA/GraphQL), or need guidance on the Contentful MCP server. Also triggers on "Contentful 101", "which Contentful API", "how do I get started", "which skill should I use", "what does X mean in Contentful", "Contentful glossary", "CDA vs CPA", "CDA vs GraphQL", "how does Contentful work", "Contentful architecture", "explain environments", "what are aliases", "content model design", "headless CMS", "Contentful MCP", "MCP server", "set up MCP", "Remix Contentful", "Astro Contentful", "Gatsby Contentful", "SvelteKit Contentful", "Nuxt Contentful". Not for framework-specific implementation (contentful-nextjs), migrations (contentful-migration), personalization (contentful-personalization), or hands-on REST/GraphQL request examples (contentful-api).
Backseat gaming mode for coding — you can see exactly what's wrong and tell the user precisely what to do, but you never touch the code yourself. The user implements everything. Persistent, no exit. Activate with /backseat. Use when the user says /backseat, "coach me but don't code for me", "guide me while I implement", or wants to do the coding themselves with guidance.
Literature Scout — Responsible for multi-source literature retrieval, screening, and classification, and constructing literature matrices. Activated when assigned by research supervisors to collect literature. Conduct systematic literature retrieval using tools such as Exa, ArXiv API, Semantic Scholar, etc.
Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.
Handle Chainlink ACE (Automated Compliance Engine) work using the public smartcontractkit/chainlink-ace repository and official docs.chain.link ACE Platform docs. Use for audited ACE core contracts, managed Platform/Beta scope, Coordinator API, Reporting API, Policy Management, PolicyEngine, PolicyProtected, policy chains, custom policies, extractors, mappers, Cross-Chain Identity (CCIDs), credential registries, KYC/AML credentials, sanctions screening, regulated tokens, ERC-20 and ERC-3643 compliance token examples, upgrade guidance, and BUSL licensing. Trigger on any mention of ACE, Automated Compliance Engine, chainlink-ace, Chainlink compliance, policy enforcement, ERC-3643, or onchain compliance rules, even if the user does not explicitly say 'ACE'.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**