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Found 3,041 Skills
Integrates Sanity Live with Next.js Cache Components in next-sanity v13+ apps. Sets up sanityFetch, <SanityLive>, Visual Editing, Presentation Tool, draft mode handling, and the three-layer (Page/Dynamic/Cached) component pattern with explicit perspective/stega prop-drilling. Use when configuring or migrating a Next.js app to cacheComponents with Sanity, when adding sanityFetch, when wiring <SanityLive>/<VisualEditing>, or when refactoring components that hardcode perspective/stega.
Use when the user wants to convert a video between horizontal and vertical orientations while preserving the inverted aspect ratio (16:9 ↔ 9:16, 4:3 ↔ 3:4, 21:9 ↔ 9:21). The skill crops a narrow band from the source and tracks the active speaker — the person whose mouth is moving — via MediaPipe face landmarks and mouth-aspect-ratio variance, so the talker stays in frame even when other people are visible. Triggers — "横转竖", "竖转横", "做成竖屏发抖音/视频号/小红书", "16:9 to 9:16", "make this vertical for Reels / TikTok / YouTube Shorts", "crop to portrait", "convert to landscape".
MUI Base UI unstyled React components with Floating UI. Use for accessible components, Radix UI migration, render props API, or encountering positioning, popup, v1.0 beta issues.
Reconstruct a reference slide image into an editable PowerPoint using DeckKit, route-aware bbox JSON, optional browser Workbench review, lucide/icon semantic reconstruction, source crops, and image-generation prompts for hard bitmap assets.
Guardião da arquitetura de software no SynkOS. Use esta skill quando o usuário pedir para propor ou revisar a arquitetura de um sistema, avaliar tradeoffs entre tecnologias ou abordagens, criar um ADR (Architecture Decision Record), desenhar um modelo de dados ou contrato de API, ou fazer perguntas como "qual stack usar para X?", "como estruturar esse serviço?", "quais são os tradeoffs de Y vs Z?", "documente as decisões técnicas", "revise essa arquitetura". Ative também para discovery brownfield (entender o que já existe antes de propor mudanças), para cross-cutting concerns como segurança e performance, e para revisar designs propostos pelas equipes de implementação.
Diagnose why a GAIA question failed — extract trace, classify failure mode, and propose a fix
The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Cross-modal eval is the recommended Phase 3 quality gate: 3 frontier models from different providers critique the output, you iterate to quality, THEN write tests that lock in the proven-good behavior.
Comprehensive Rust code review across four lenses — source code (ownership, borrowing, lifetimes, errors, trait design, unsafe, common mistakes), tests (unit, integration, async testing, mocking, property-based), tokio async (task management, sync primitives, channels), and FFI (extern blocks,
Use before any implementation — understands the request, discovers project context, and proposes a concise plan for user approval before writing any code.
Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
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.**
Build React chat interfaces with Vercel AI SDK v6. Covers useChat/useCompletion/useObject hooks, message parts structure, tool approval workflows, and 18 UI error solutions. Prevents documented issues with React Strict Mode, concurrent requests, stale closures, and tool approval edge cases. Use when: implementing AI chat UIs, migrating v5→v6, troubleshooting "useChat failed to parse stream", "stale body values", "React maximum update depth", "Cannot read properties of undefined (reading 'state')", or tool approval workflow errors.