2,883 件 ・ 関連度順
概要と使いどころ
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or
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。
Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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。
Compatibility skill for geo llmstxt. Use when a user or upstream workflow invokes this name; route the task to `ai-seo` while preserving the requested scope and source-specific intent.
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 for AI search, answer engines, GEO/AEO/LLMO and conventional search together. Use for citability, AI crawler access, entity/brand evidence, llms.txt evaluation, platform readiness and AI-answer 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。
Generative UI (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.
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。
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
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。
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
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 Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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。
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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。
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
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。
Karpathy's LLM Wiki: build/query interlinked markdown KB.
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。
I-A LLMs Large Language Models (LLMs) are extensive, general-purpose models that can be pre-trained and adapted for specific tasks.
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。
Architecture Matters We often conflate different AI paradigms: LLMs (Transformers): Predict token se
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。
Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation: even frontier models (Gemini 3.
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。
Model Quantization Guide: Run 70B LLMs in 4 Bits — … — 12. Okt. 2025 · Run 70B parameter LLMs on con
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。
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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。
E-commerce Specialist IA — Expert en e-commerce (Shopify, WooCommerce, Stripe, catalog, fulfillment, marketplace DZ)
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。
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
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。
LLMs, prompt engineering, RAG systems, LangChain, and AI application 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。
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
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 patterns for building LLM applications, inspired by [Dify](https://github.com/langgenius/dify) and industry best practices.
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。
Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI, Anthropic, and Google APIs.
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 improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token 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。