「code generation」の検索結果

OpenAPI 3.1 specification, schema design, code generation

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。

Create Faust DSP plugins in Pulp using Pulp-owned reference DSPs, optional external code generation, and the FaustProcessor template wrapper.

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。

Generate and maintain browser demos of Pulp audio plugins (both web ABIs — WAM and WCLAP) from one declarative config, so every demo mounts the SAME shared player and the two ABIs stay in lockstep. Invoke it whenever you publish/update a browser demo of a Pulp plugin, add a plugin to a demo gallery, stand up a new gallery, or touch demo pages/hosting. It runs a deterministic generator (config → stamped WAM+WCLAP site + owned-files manifest) and a blocking validator (config schema, cross-origin-isolation coverage, OG metadata, regeneration ownership). Customization is declarative config in <repo>/.pulp-web-demo/config.json (schema bundled) plus at most two typed module seams; the shared player (pinned @danielraffel/web-player) holds all UX behavior and is never vendored. Works in Claude and Codex.

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。

Hreflang and international SEO audit, validation, and generation. Detects common mistakes, validates language/region codes, and generates correct hreflang implementations. Use when user says "hreflang", "i18n SEO", "international SEO", "multi-language", "multi-region", "language tags", "language targeting", "country targeting", "x-default", "locale", or "multilingual SEO".

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。

Hreflang and international SEO audit, validation, and generation. Detects common mistakes, validates language/region codes, and generates correct hreflang implementations. Use when user says "hreflang", "i18n SEO", "international SEO", "multi-language", "multi-region", or "language tags".

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。

Maintain Yuzu's protobuf wire contracts, code generation, and gateway proto mirrors. Use when editing `.proto` files, `proto/gen_proto.py`, `proto/meson.build`, `proto/buf.yaml`, or any file under `gateway/priv/proto`.

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。

Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.

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。

Analysis of Competing Hypotheses. Operationalizes Richards Heuer's CIA-tradition technique for systematically narrowing among multiple hypotheses against evidence. Builds an explicit hypothesis-vs-evidence matrix and focuses on disconfirmation rather than confirmation — the central insight: hypotheses cannot be proven, only disconfirmed; the surviving hypothesis is the one with the least disconfirming evidence. Spawns parallel hypothesizers (in isolation, across hypothesis-generation angles) and parallel evidence-gatherers (across evidence classes), then synthesizes into a matrix, diagnosticity analysis, sensitivity analysis, and falsification milestones. Produces feedback only — no code, no tickets, no artifacts.

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。

Production-ready Dart and Flutter patterns covering null safety, immutable state, async composition, widget architecture, popular state management frameworks (BLoC, Riverpod, Provider), GoRouter navigation, Dio networking, Freezed code generation, and clean architecture.

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。

Use when committing or pushing changes and the repository needs a version bump. Analyzes commits using conventional commit prefixes to determine whether the next release is a major, minor, or patch increment. Also use when the user asks about versioning, release planning, or changelog generation.

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。

Launch Marcus multi-agent experiments with independent CLI agents in tmux panes. Use when the user says "build X with Marcus", "run a Marcus experiment", "spawn N agents with Marcus", "/marcus", or references the Marcus multi-agent framework. This skill handles experiment setup, config generation, and spawning independent agent CLI processes that coordinate via the Marcus MCP server. Each agent runs in its own tmux pane with full autonomy. NOT for ClawTeam — this is specifically for the Marcus MCP-based multi-agent system. NOTE: "Marcus" is also the name of the MCP server that agents use for coordination. The /marcus skill launches experiments that USE the Marcus MCP server — they are complementary, not competing. The MCP server must be running before invoking this skill. The Marcus MCP server is agent-agnostic; this skill supports both Anthropic's claude CLI and OpenAI's codex CLI as agent harnesses (selected via --harness).

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。

Launch Marcus multi-agent experiments with independent CLI agents in tmux panes. Use when the user says "build X with Marcus", "run a Marcus experiment", "spawn N agents with Marcus", "/marcus", or references the Marcus multi-agent framework. This skill handles experiment setup, config generation, and spawning independent agent CLI processes that coordinate via the Marcus MCP server. Each agent runs in its own tmux pane with full autonomy. NOT for ClawTeam — this is specifically for the Marcus MCP-based multi-agent system. NOTE: "Marcus" is also the name of the MCP server that agents use for coordination. The /marcus skill launches experiments that USE the Marcus MCP server — they are complementary, not competing. The MCP server must be running before invoking this skill. The Marcus MCP server is agent-agnostic; this skill supports Anthropic's claude CLI, OpenAI's codex CLI, and Google's gemini CLI as agent harnesses (selected via --harness).

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。

Use Angular CLI and development tools effectively in Angular v20+ projects. Use for project setup, code generation, building, testing, and configuration. Triggers on creating new projects, generating components/services/modules, configuring builds, running tests, or optimizing production builds. Don't use for Nx workspace commands, custom Webpack configurations, or non-Angular CLI build systems like Vite standalone or esbuild direct usage.

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。

Use when a plan directory (plan.md + phase-*.md) needs a visual review. By DEFAULT this drives the STRUCTURED visual-plan artifact (visual-plan/plan.json) via the mewkit visual-plan CLI — generate if absent, validate, open the local studio (view/edit), and export plan.html from the approved artifact. Pass --static for the legacy prompt-only single-file plan.html template render. Triggers on "render this plan", "visualize the plan", "open the plan studio", "make a shareable plan page", "plan.html". This is the canonical owner of plan-as-visual-review. NOT for plan critique or scope review (see mk:plan-ceo-review / mk:validate-plan), NOT for generic code/diagram/diff visuals (see mk:preview), NOT for live media generation (see mk:multimodal).

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。

Render an HTML/CSS/JS template to a local MP4 video by capturing frames with headless Chromium (via Playwright) and encoding them with ffmpeg. Use when "render my animation as a video", "export HTML to MP4", "make a video from my web template", or "capture CSS animation as MP4". NOT for recording live browser interactions (use `agent-browser record start`); NOT for screen recording; NOT for AI video generation (use mk:multimodal).

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。

Synthesize code harnesses for agent action validation — AutoHarness-inspired verifier/filter/policy generation

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。

Use when writing C# / VB.NET / F# code that reads, writes, manipulates, or exports Excel (.xlsx / .xls) files, generates PDF or HTML from Excel, or produces data-driven reports with FlexCel Studio for .NET (TMS Software). Triggers include Excel/xlsx from C#, XlsFile, FlexCelReport, FlexCelPdfExport, FlexCelHtmlExport, FlexCelImgExport, ExcelFile, PdfWriter, .NET Excel export, ASP.NET Excel generation, .NET MAUI Excel, Native AOT + Excel, and Excel reporting from .NET.

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。

Use when taking a repo open source, or hardening one that already is. Run /oss-launch and it scans the repo (stack, existing files, git remote, secrets), asks only what it cannot infer, then generates a tailored OSS file collection (README, LICENSE, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, CHANGELOG, .gitignore, .github/ issue + PR templates, CI/CD). Also covers launch (GitHub metadata, badges, Show HN, Reddit, YouTube), release cadence (SemVer + CHANGELOG), and demo-GIF generation. Triggers on "open source this", "OSS checklist", "prep for launch", "make this repo public", "add a license / contributing / security policy", "Show HN", "release workflow", "star growth", "get contributors".

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。

Especialista em construção de sistemas de Retrieval-Augmented Generation. Domina modelos de embedding, bancos de dados vetoriais, estratégias de chunking e otimização de recuperação para aplicações LLM. Use quando: construir RAG, busca vetorial, embeddings, busca semântica, recuperação de documentos.

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。

Controle saída de LLM com regex e gramáticas, garanta geração válida de JSON/XML/código, aplique formatos estruturados e construa fluxos de trabalho multi-etapas com Guidance - framework de geração restrita da Microsoft Research

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。

Estratégias de cache para prompts de LLM incluindo cache de prompt da Anthropic, cache de resposta e CAG (Cache Augmented Generation) Use quando: prompt caching, cache prompt, response cache, cag, cache augmented.

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。

Padrões de Retrieval-Augmented Generation incluindo chunking, embeddings, vector stores e otimização de recuperação. Use quando: rag, retrieval augmented, vector search, embeddings, semantic search.

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。

Geração estruturada rápida e serving para LLMs com RadixAttention para cache de prefixos. Use para saídas JSON/regex, decodificação restrita, workflows com agentes que fazem chamadas de ferramentas, ou quando você precisa de 5× mais rápido que vLLM com compartilhamento de prefixos. Alimenta 300.000+ GPUs na xAI, AMD, NVIDIA e LinkedIn.

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。

Acelere a inferência de LLMs usando especulative decoding, múltiplas cabeças Medusa e técnicas de lookahead decoding. Use ao otimizar velocidade de inferência (aceleração de 1,5-3,6×), reduzir latência em aplicações em tempo real ou fazer deploy de modelos com recursos computacionais limitados. Cobre modelos draft, atenção em árvore, iteração de Jacobi, geração paralela de tokens e estratégias de deploy em produção.

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。