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概要と使いどころ
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).
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Implement distributed cron jobs with Redis-based locking and BullMQ offloading in NestJS. Use when adding @Cron scheduled tasks, preventing duplicate runs across pods, or delegating heavy work to queue workers.
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。
Quick execution for small tasks — minimal run lifecycle (start + done) with evidence recording. Full LLM capability, scoped to mechanically clear 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。
Swarm intelligence team skill — ACO-driven multi-agent exploration with hybrid LLM coordinator + Python optimization controller. Coordinator generates swarm-config from user task, then runs K iterations of N parallel ants guided by pheromone state. Universal task space via config (nodes + scoring rule). Triggers on "team swarm", "swarm intelligence", "蚁群".
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。
Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.
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。
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.
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。
Message queue patterns with BullMQ, Kafka, RabbitMQ - saga, outbox, dead letter queue, exactly-once semantics.
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。
Write reliable prompts for Agentica/REPL agents that avoid LLM instruction ambiguity
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。
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing 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。
Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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 bounded, durable AI project memory in a repository's docs/ai/ pack. Use when Codex or Claude Code needs to create, read, update, or audit project memory (project-card, architecture, diagrams, runbook, handoff, gotchas, ADRs), install AGENTS.md memory rules, or sync a lightweight central LLM Wiki project entity for user-owned projects.
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。
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
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。
Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.
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。
Seederive 非结构化数据打标平台，使用 LLM 对文本、语音、图片数据进行批量分析处理。 当用户提到以下任何场景时必须使用此 Skill：数据打标、标注、情感分析、标签分类、 观点提取、翻译、评论分析、水军识别、内容评分、标签库管理、提示词优化。 即使用户没有直接提到「Seederive」，只要涉及对一批文本做分类/打标/分析/翻译/评分， 或者提到「帮我分析这些评论」「这些数据的情感是什么」「识别水军」「提取观点」 「翻译这批内容」「建个标签体系」「效果不好帮我优化」等表述，都应触发此 Skill。 也适用于用户提供 CSV/Excel 文件要求批量处理的场景。
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。
Use when reviewing, designing, or modifying Java enterprise systems that use GenAI, LLMs, AI-assisted coding, RAG, AI agents, generated code, generated dependencies, prompt workflows, external model providers, or AI-enabled business logic and need ISO/IEC 42001 AI management system-aware engineering guidance. Part of Plinth Toolkit
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。
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
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。
Manage golden dataset regression tests for LLM prompts using Promptfoo. Commands — init, add, run, report. Use when user says "regression", "golden dataset", "prompt regression", "eval run", "test my prompts", or after shipping prompt changes.
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。