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Found 1,248 Skills
Build modern web applications with React, Vue, Angular, or Svelte, focusing on performance and accessibility. Use when you need component library development, TypeScript UI implementation, responsive layouts with CSS Grid and Flexbox, Core Web Vitals optimization, service worker offline support, code splitting, ARIA accessibility, Storybook integration, or frontend API client architecture.
Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.
A hand-drawn wireframe exploration — graph-paper background, marker / pencil tone, multiple tab labels for variants, sticky-note annotations, scribbled chart placeholders, hatched fills. Reads like a designer's whiteboard before any pixels are committed. Use when the brief asks for "wireframe", "sketch wireframe", "hand-drawn", "lo-fi", "whiteboard", "草稿", or "手绘原型".
Guides application developers in designing correct and performant transaction patterns for CockroachDB, covering transaction lifetime, implicit vs explicit transactions, retry handling with exponential backoff, pushing invariants into SQL, selective pessimistic locking, set-based operations, connection pooling, prepared statements, keyset pagination, follower reads, and separating business logic from database logic. Use when building applications on CockroachDB, designing transaction workflows, handling retries, optimizing application-layer database interactions, or configuring connection pools.
Design a professional logo with full branding package — primary logo, variations (dark/light/icon-only), color palette, and real-world application mockups.
Add a single functional spec to the ***functional specs*** section of a ***plain spec file. Use whenever exactly one new functional spec is being added — whether the user explicitly asks, or another skill/workflow (e.g. forge-plain, add-feature) needs to author a new functional spec. Every new entry under ***functional specs*** must go through either this skill or `add-functional-specs` (the bulk variant for adding multiple specs in one pass); hand-authoring functional specs without invoking one of these skills is forbidden.
Write, refine, run, and QA promptfoo evaluation suites: promptfooconfig.yaml, prompts, providers, vars, tests, assertions, model-graded rubrics, transforms, datasets, exports, and CI gates. Use for non-redteam eval coverage, regression tests, or new eval matrices. Do not use for adversarial redteam plugin or strategy setup.
Modo de explicação em camadas. Aplica SOMENTE na resposta imediatamente após a invocação — depois volta ao normal automaticamente. A resposta deve ser em manchete (1 frase, no máximo 2), no nível exato da granularidade da pergunta. Não listar itens individuais quando a pergunta foi sobre o conjunto. Não oferecer drill-down nem perguntar se quer detalhar — esperar o usuário pedir. Use SOMENTE quando o usuário invocar explicitamente com "/peel-talk", "peel-talk", "explica no peel-talk", "peel talk", "modo peel", ou variações. NÃO invocar automaticamente em outras tarefas.
One-time setup skill that builds a personalized inbox triage knowledge base via interactive interview. Interviews the user about their email patterns, business context, reply style, and priorities using grill-me discipline (one question at a time, forcing format where possible, dependency-ordered, each question explains why I'm asking), then generates the knowledge base files that power the companion 'inbox-triage' skill. Run this once before using inbox-triage for the first time. Re-run when business, pricing, or priorities change significantly. Triggers: 'set up my inbox', 'configure inbox triage', 'set up my email system', 'configure email triage', 'build my email knowledge base', 'initialize email management', 'set up inbox triage', 'onboard email triage', or any variation where someone wants to get the email triage system running for the first time.
This skill is strict implementation instruction, not advisory reference text. The skill treats the HTML as discovery-only input, forces interactive Playwright route/state capture, then moves through scored gates for source acceptance, implementation planning, authored UI reproduction, implementation integrity, visual verification, and adversarial proof before signoff.
Integrates Material UI with Next.js App and Pages routers using @mui/material-nextjs, Emotion cache providers, next/font, CSS layers with Tailwind/CSS Modules, Link component prop patterns, CSS theme variables SSR notes, and App Router useSearchParams + Suspense. Use when setting up or debugging MUI in a Next.js app.
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.**