「voc」の検索結果

Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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This skill recommends against initiating treatments aimed at normalizing cortisol or its action when only borderline biochemical abnormalities of the hypothalamic-pituitary-adrenal (HPA) axis are present without specific signs of Cushing's syndrome. Use when encountering borderline urinary free cortisol, late-night salivary cortisol, or dexamethasone suppression test results without classic Cushingoid features; triggers include borderline UFC (1-2× ULN), equivocal late-night salivary cortisol, or nondiagnostic low-dose DST.

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Contract document for the binding test verdict file at .claude/state/last_test_result. Format spec for callers (integrate, simplify, chore, the verify-tick worker) that inline the four mechanical operations. Not Skill-tool-invocable.

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Orchestrates `impeccable` for every design task inside a workflow phase. Captures intent in natural language, classifies it (design / development / copy), translates design intents into a sequence of impeccable subcommand invocations, runs them in main context with state persistence at `.claude/state/design/<slug>.json`, and returns a structured report. ALWAYS invokes `impeccable` under the hood for the underlying design move; never picks aesthetic direction itself; never writes product code directly. Per CLAUDE.md Article X.2, all design tasks in workflow phases route through this skill.

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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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Dev-only check that a drafted spec for THIS baseline repo won't ship dev-tree references to consumer installs. Catches three failure modes — shipped SKILL.md prose that references paths under `src/`, `tests/`, `scripts/`, `obj/` as runtime invocations (in ```bash fences``` OR `inline backticks`, plus shipped `.mjs`/`.js`/`.sh`/`.py` helper-file imports); new Python helpers added under `.claude/skills/<slug>/` (shipped helpers must be `.sh` or `.mjs`/`.js` going forward); and imports of modules that aren't in `obj/template/.claude/manifest.json` (consumer won't have the file). The aggregate scanner (`scan-shipped-skills.mjs`) walks only baseline-owned skill dirs (via `owner: baseline` frontmatter) at top level — `references/` and other subdirs are documentation, not runtime. BLOCKER findings hard-block implementation entry; ADVISORY surfaces but doesn't block. Read-only — surfaces a punch list; maintainer edits the spec and re-runs until CLEAN.

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Bring documentation prose down to a high-school reading level without losing precision. Runs after the content is written and before humanizer. Targets grade 9 on four measures — sentence length, vocabulary, clause load and jargon density — and carries the rewriting moves that actually shift them.

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Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

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Rewrite AI-sounding prose so it reads as considered human writing — cut the tells (em-dash pile-ups, "it's not X, it's Y" antitheses, inflated vocabulary, uniform rhythm, empty uplift) while preserving every fact, claim, and the author's own voice. Use when the user says "make this not sound like AI", "de-AI this", "humanize/humanise this", "this reads like ChatGPT", "remove the AI tells", "it sounds robotic/generic", or shares a draft (post, email, essay, docs, cover letter) to sharpen into a more human voice. Ships a stdlib scanner that flags the mechanical tells.

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Use for critical thinking, or when the user wants a claim, plan or decision checked for weak spots: "is this right?", "what am I missing?", "poke holes", "sanity check", "devil's advocate", "red team this". Skip for execution-only tasks and wording edits.

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Use for lateral thinking, or when ideas feel stuck or incremental: "we've tried everything", "need a surprising angle", "break the pattern", "give me a wild idea that could work". Uses provocations and reframes. Skip for rule lookups or strict logical proofs.

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Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.

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Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

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Implement and finalize one ready rolling-wave slice in one invocation while preserving the lifecycle boundary between implementation and independent review. Use when the user wants a slice delivered end to end without manually invoking implement-slice and finalize-slice, wants main-local implementation by default with workers only when delegation pays, followed by mandatory fresh-context finalization, or says deliver, implement and finalize, or take the slice through review.

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Summarizes uncommitted git changes in a concise machine-readable format. Use in CI pipelines, scripts, or headless invocations where the output will be piped or captured.

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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Use when extracting Voice-of-Customer themes from existing artefacts — GH issues, PR threads, Sentry patterns. Triggers on 'what are users saying', 'recurring complaints', 'top themes'.

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Adversarial critique — devil's advocate, stress-test, honest teardown ('poke holes', 'be brutal', 'was hältst du davon'); explicit request only. Routine code or design review → code-review.

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Você DEVE usar isto antes de qualquer trabalho criativo - criar features, construir componentes, adicionar funcionalidade ou modificar comportamento. Explora a intenção do usuário, requisitos e design antes da implementação.

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Kit de ferramentas para estilizar artefatos com um tema. Esses artefatos podem ser slides, documentos, relatórios, páginas de destino HTML, etc. Existem 10 temas pré-configurados com cores/fontes que você pode aplicar a qualquer artefato criado, ou pode gerar um novo tema instantaneamente.

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Fornece orientação para treinamento RL agentico nativo do PyTorch usando torchforge, biblioteca da Meta que separa infraestrutura de algoritmos. Use quando você quer abstrações limpas de RL, fácil experimentação de algoritmos, ou treinamento escalável com Monarch e TorchTitan.

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Biblioteca do Facebook para busca eficiente de similaridade e clustering de vetores densos. Suporta bilhões de vetores, aceleração GPU e vários tipos de índice (Flat, IVF, HNSW). Use para busca k-NN rápida, recuperação de vetores em larga escala ou quando você precisa de busca pura de similaridade sem metadados. Melhor para aplicações de alto desempenho.

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Todo produto será potencializado por IA. A questão é se você vai construir corretamente ou entregar uma demo que desmorona em produção. Esta skill cobre padrões de integração de LLM, arquitetura RAG, engenharia de prompt que escala, UX de IA em que usuários confiam, e otimização de custos que não quebra o orçamento. Use quando: keywords, file_patterns, code_patterns.

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Tokenizadores rápidos otimizados para pesquisa e produção. Implementação em Rust que tokeniza 1GB em menos de 20 segundos. Suporta algoritmos BPE, WordPiece e Unigram. Treine vocabulários customizados, rastreie alinhamentos, gerencie padding/truncagem. Integração perfeita com transformers. Use quando precisar de tokenização de alto desempenho ou treinamento de tokenizadores customizados.

日本語の概要は準備中です。原文の説明を表示しています。