2,353 件 ・ 関連度順
概要と使いどころ
When picking a secrets store, designing rotation, or wiring scanning gates — multi-cloud (Vault, AWS, Azure, GCP), CI and Kubernetes — decision framework, provider deep-dives externalized.
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Use for deep Laravel project analysis: boot flow, request lifecycle, container usage, Eloquent/data flow, async systems, and Laravel-specific failure patterns.
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Single-pass tech-stack detection with an agents/evidence/analysis/ write-up; explicit request only. Deep multi-pass audit → universal-project-analysis. Raw primitives → project-analysis-core.
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 for deep Symfony project analysis: kernel/bootstrap, container wiring, routing/request flow, Doctrine, security, Messenger, and Symfony-specific failure patterns.
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 for deep Node.js / Express project analysis: boot flow, middleware order, async behavior, data layer, auth/security, and Node-specific runtime failure patterns.
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Autonomous multi-step investigation — deep research carried end-to-end without per-step approval; explicit request only, never for normal feature work.
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Framework RLHF de alta performance com aceleração Ray+vLLM. Use para treinamento PPO, GRPO, RLOO, DPO de modelos grandes (7B-70B+). Construído em Ray, vLLM, ZeRO-3. 2× mais rápido que DeepSpeedChat com arquitetura distribuída e compartilhamento de recursos GPU.
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Especialista em construir aplicações de voz em tempo real - de agentes de voz em tempo real a apps habilitados para voz. Abrange OpenAI Realtime API, Vapi para agentes de voz, Deepgram para transcrição, ElevenLabs para síntese, LiveKit para infraestrutura em tempo real e fundamentos de WebRTC. Sabe como construir experiências de voz com baixa latência e prontas para produção. Use quando: voice ai, voice agent, speech to text, text to speech, realtime voice.
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。
Fornece treinamento distribuído de LLM nativo do PyTorch usando torchtitan com paralelismo 4D (FSDP2, TP, PP, CP). Use para fazer pré-treinamento de Llama 3.1, DeepSeek V3 ou modelos customizados em escala de 8 a 512+ GPUs com Float8, torch.compile e checkpoint distribuído.
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Execute tarefas de pesquisa autônoma que planejam, buscam, leem e sintetizam informações em relatórios abrangentes.
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。
Orientação especializada para fine-tuning de LLMs com Axolotl - configs YAML, 100+ modelos, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, suporte multimodal
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Skill de pesquisa profunda alimentado por NotebookLM MCP. Conduz pesquisa estruturada em múltiplas fontes (análise de mercado, inteligência competitiva, análise de tendências, pesquisa de prospects) usando Google NotebookLM como mecanismo de pesquisa, depois entrega briefs formatados e artefatos opcionais de estúdio (slides, podcasts de áudio, vídeos, infográficos, relatórios, mapas mentais).
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。
Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for", "read the source of", "how does X work internally", "get the implementation of", "opensrc path", or any task requiring access to dependency source code beyond types and docs.
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。
[Workflow] Use when activating the Idea to PBI workflow for turning an idea — or a raw product vision/problem — into prioritized PBIs and stories (single-PBI deep mode, or multi-opportunity discovery mode).
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[Code Quality] Use to validate a spec artifact's decisions with the user across three contexts — a freshly-authored Feature Spec (idea-to-spec), an existing canonical spec before PBI decomposition (spec-to-pbi), or a refined idea + §8 test-specs (idea-to-pbi deep mode). Detects the context, walks every applicable validation category, and runs an exhaustive but budget-bounded blocking clarification gate so every non-obvious or conflicting decision is confirmed before the artifact drives downstream work.
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。
[Research] Use when starting a web research task — discover, gather, and triage candidate sources on a topic to feed deeper investigation.
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[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.
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 macOS app needs a TCC permission or touches private data — the map of which API, Info.plist key, and entitlement each permission needs, pre-prompt explanation patterns, denied-state recovery with System Settings deep links, and honest privacy copy.
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Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.
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Use Lingzao creator-content tools for Xiaohongshu/XHS, Douyin, and WeChat official-account public content. Lingzao supports XHS SEO, Xiaohongshu account analysis, benchmark account discovery, viral note breakdown, keyword research, one-stop content packages from keyword/link/image/inspiration material, title and cover optimization, hand-drawn route-map cards, city food maps, travel itinerary maps, life checklist maps, workflow manual maps, creator content workflow, note search, creator search, profile lookup, recent posts, deep profile copy/subtitle analysis, note/article detail, post comments, article stats, related articles, short-video copy extraction, and prompt-based creator image 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。
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
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Use when working in the deepseek-harness repo to find non-obvious simplification candidates, write proposed Agent Notes or inline TODO/FIXME/XXX notes, audit or coalesce superseded Agent Notes, or fold worthwhile simplification ideas from another PR; especially for dead, duplicated, speculative, over-built, added-then-removed, or hand-rolled-where-a-dependency-exists surfaces.
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, moving, reviewing, or auditing documentation in the deepseek-harness repo — choosing hierarchy and detail, separating tutorials from references, checking tutorial progression, trimming doc slop, responding to a verify-doc-budgets failure, or requests like "improve the docs", "audit the docs", "where should this be documented", or "this doc is too long".
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。