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概要と使いどころ
Serve LLMs com alta throughput usando PagedAttention do vLLM e continuous batching. Use ao fazer deploy de APIs LLM em produção, otimizar latência/throughput de inferência, ou servir modelos com memória GPU limitada. Suporta endpoints compatíveis com OpenAI, quantização (GPTQ/AWQ/FP8) e tensor parallelism.
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。
Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.
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。
Otimiza inferência de LLM com NVIDIA TensorRT para máxima vazão e latência mínima. Use para implantação em produção em GPUs NVIDIA (A100/H100), quando você precisa de inferência 10-100x mais rápida que PyTorch, ou para servir modelos com quantização (FP8/INT4), batching em voo e escalabilidade multi-GPU.
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。
Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.
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。
Framework para construir aplicações com LLM usando agentes, chains e RAG. Suporta múltiplos provedores (OpenAI, Anthropic, Google), 500+ integrações, agentes ReAct, tool calling, gerenciamento de memória e recuperação de vector stores. Use para construir chatbots, sistemas de perguntas e respostas, agentes autônomos ou aplicações RAG. Ideal para prototipagem rápida e deployments 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。
Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.
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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
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。
Applies Canvas brand colors, typography, and visual polish to artifacts that benefit from a consistent product look-and-feel. Use it when brand colors, style guidelines, visual formatting, or company design standards apply.
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。
Publish a new Canvas Notebook release. Use this skill whenever the user asks to bump a version, prepare release notes, update CHANGELOG.md, create or push a git tag, publish a GitHub Release, deploy, or automate the release workflow. This skill covers the full release sequence for this repository: inspect commits/tags, push completed product-code commits first when authorized, confirm the pushed code has built on GitHub, choose the next calendar version, update package and CLI versions, update the changelog, run the required local build, commit, push the release commit, wait for the remote Build and Push worker on the pushed branch, tag, push the tag, and publish the release.
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。
Load this skill when configuring or reviewing CI/CD pipelines, GitHub Actions workflows, or automated testing setups. Ensures accessibility regressions are caught before code reaches production by enforcing quality gates, structured reporting, and layered automated + manual testing across all pages and user preferences.
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 adding logging, metrics, tracing, or alerting to production code, or when a past incident lacks diagnostic data.
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。
Compares a group of companies by products, positioning, market focus, and differentiators. Use when the user provides several company records and wants a structured competitive comparison.
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 creating or editing raster images, visual concepts, product graphics, launch assets, diagrams, posters, thumbnails, reference sheets, UI mock images, or image prompts that need source-grounded claims, readable text, brand direction, and post-generation verification.
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。
Critically analyze whether a product, repo, skill, plugin, harness, or architecture vision still fits the real application, user intent, implementation, evidence, and likely future direction. Use when asked to assess vision vs application, predict future fit, critique roadmap direction, analyze strategic intent, or decide whether to keep, refine, pivot, or kill a plan.
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。
Designs public or private Agent Skill and plugin marketplaces for Cursor, Claude Code, Codex, Zed, Open Plugin, and npx skills—manifest layout, install matrix, and Skill Steward vs product boundaries. Use when setting up a marketplace, distributing skills/plugins to a team, private registry, .cursor-plugin, .codex-plugin, .plugin, .claude-plugin, or skills.sh publishing.
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。
Maintains repo-local action contracts and harness repositories where product CLI and MCP adapters stay thin over core libraries. Use when adopting or improving steward.yaml actions, capability-level adoption evidence, cold-start proof loops, probes, benchmarks, CLI/MCP/core parity, adapter refactors, packages/core boundaries, or sibling harness layout; use repo-quality-system-lifecycle first for general app/library/tool stewardship baselines.
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。
Chooses ecosystem-native release and changelog tooling (Changesets, Melos, release-plz) plus binary distribution (GitHub Release tarballs, install.sh) when the product is an executable. Use for release CI, install.sh, versioning, CHANGELOGs, shipping MCP/CLI without clone, or meta repos that only ship skills via npx skills—not domain app tutorials.
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。
Telegram CLI for AI agents. Use when the user needs to read or search Telegram, send or edit a message, download media, organise Saved Messages, monitor conversations, or automate a Telegram task. Triggers on “check my messages”, “send a message”, “search Telegram”, “read unread”, “listen to chat”, “download from Telegram”, тэг сохранёнок, чаты, каналы, and @peer names.
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 after PROJECT_GUIDE.md exists, when the user asks if a project is worth their time, should they build vs buy, or invokes project-worth-my-time. Follow-up to explain-new-project—weighs project context (files, git history, chat) to recommend whether to invest in a repo, cherry-pick a feature, park the idea, pass, or use an existing polished product instead. Optionally enriched by a personal-memory MCP (e.g. Pieces) when one is available.
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。
辅导中国中小学生作业的家长助手。当用户要"辅导作业""孩子这题不会""怎么给孩子讲""检查作业""孩子不想写作业"时使用。重点是教家长怎么引导，而不是直接给答案。
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。
用 AI 系统学习任何东西的教练。当用户要"学 XX""怎么入门""帮我制定学习计划""我想搞懂 XX""考证/备考""复习"时使用。不是直接讲答案，而是带着走完整的学习循环：定目标→输入→主动回忆→输出→纠错→间隔复习→项目交付。
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。
生成中文职场汇报材料。当用户要"写周报""做月度总结""述职报告""项目汇报""向老板汇报""做汇报PPT大纲""写工作计划"时使用。按结论先行、数据支撑的中文职场表达习惯组织。
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。
中文记账与账目整理助手。当用户要"记账""整理流水""做收支表""对账""分类消费""做预算""看看钱花哪了"时使用。只做数据整理与呈现，不提供税务筹划或投资建议。
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。
Project-level WeChat article drafting workflow for turning source materials, repo intakes, product notes, screenshots, or existing markdown into a publishable Chinese公众号 draft. Use for writing, rewriting, evidence-gating, title options, platform adaptation, and image planning before optional publishing.
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。