468 件 ・ 関連度順
概要と使いどころ
Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.
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。
Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.
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。
Build MCP servers in Python with FastMCP. Define tools / resources / prompts, build the server, test locally, deploy to FastMCP Cloud or Docker. Use whenever the user mentions building an MCP server, exposing tools to LLMs, FastMCP, building a Claude integration, or troubleshooting FastMCP module-level server, storage, lifespan, middleware, OAuth, or deployment errors.
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。
Writes, refactors, and evaluates prompts for LLMs generating optimized templates, structured output schemas, evaluation rubrics, and test suites.
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。
Fine-tunes LLMs and trains custom models using LoRA/QLoRA adapters, JSONL training datasets, hyperparameter configuration, RLHF, DPO, and model quantization.
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。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
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。
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.
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 the user wants to compare or benchmark multiple LLMs/agents arena-style but it's unclear which specific workflow fits — a general-purpose win-rate comparison on a custom task, or a benchmark specifically about reference/citation hallucination rate. Also use when the user mentions model arena, agent arena, pairwise model comparison, win-rate ranking, or comparing models on a task and hasn't specified whether that task is generic or about citation accuracy. This skill is the entry router for the arena-eval suite: it asks one diagnostic question when needed, then recommends the workflow or workflows needed to cover the request.
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 the user wants help with academic papers or citations but it's unclear which specific workflow fits — reviewing a paper, checking a BibTeX file for fake references, or benchmarking multiple LLMs on reference-recommendation accuracy. Also use when the user mentions paper review, peer review, BibTeX verification, citation checking, reference hallucination, or academic literature accuracy and hasn't specified which of those three tasks they mean. This skill is the entry router for the academic-eval suite: it asks one diagnostic question then routes to the right sub-skill.
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 and read this skill immediately if the user request is in any way related to SEO or a site's organic search or AI search presence. That includes site audits, rankings, keyword research, competitors, backlinks, click or traffic changes, indexing problems, crawling, redirects, sitemaps, metadata, structured data, Core Web Vitals, internal links, content opportunities, programmatic SEO, local search, Search Console or Google Analytics questions, Google update impact, llms.txt, AI search visibility in ChatGPT, Claude, Perplexity, or Google AI Overviews, and client SEO reporting. Routes to evidence-backed local reports through the SEO CLI and MCP server.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", or "AI visibility".
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。
When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' or 'zero-click search.' This skill covers content optimization for AI answer engines, monitoring AI visibility, and getting cited as a source. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema-markup.
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 building AI agents in Dart, implementing Genkit flows or tools, integrating LLMs into Dart or Flutter applications, or using Genkit Dart plugins.
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。
Khi nguoi dung muon tang traffic tu SEO, AI SEO, GEO/AEO, Google, ChatGPT, Perplexity, schema, structured data, llms.txt, programmatic SEO, local SEO, directory submissions, Product Hunt, G2/Capterra, backlinks, hoac audit website khong len top. Dung cho 'SEO audit', 'AI search visibility', 'duoc AI trich dan', 'schema markup', 'pSEO', 'local SEO', 'submit directory', 'backlink plan'. Khong dung cho setup Google Business Profile lan dau — dung skill 11-thiet-lap-kenh; khong dung cho viet noi dung bai — dung 36-content-brief.
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 the user wants SEO growth, AI SEO, GEO/AEO, Google visibility, ChatGPT or Perplexity citations, schema, structured data, llms.txt, programmatic SEO, local SEO, comparison and alternative pages, directory submissions, Product Hunt, G2/Capterra, backlinks, or a site audit. Trigger on 'SEO audit', 'AI search visibility', 'AI citations', 'schema markup', 'pSEO', 'alternative page', 'submit to directories', or 'backlink plan'. Not for first-time Google Business Profile setup, see `11-channel-setup-global`; not for writing the articles themselves, see `36-content-brief-global`.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.
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。
Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Comprehensive guide for writing modern Neo4j Cypher queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, set-based writes, and Quantified Path Patterns (QPP) for efficient graph traversal.
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。
Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.
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。
大模型安全测评全流程管理工具。支持创建/更新模型和智能体测评资产、查询可用资源、发起合规测评任务、发起安全测评任务、分析测评结果等全流程操作。
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。
Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation 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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。