3,275 件 ・ 関連度順
概要と使いどころ
Choose which SkillNote skill collection is active for this project. Use when user says "change collection", "switch skills", "use frontend skills", "show collections", or at first session in a new project when recommended.
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。
A diagnostic tool for OpenClaw agents -- checks skill registry connectivity, AGENTS.md setup, config file validity, and installed skill health. Use when your setup seems broken, skills aren't loading, or you want to audit your agent's configuration.
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 and push reusable skills to SkillNote when repeated instructions are detected or user says "create a skill", "save this pattern", "push a skill". Guides drafting, review, collection selection, and 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。
Rate a SkillNote skill after using it in Codex. Called after applying a skill to record a 1-5 rating and outcome.
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。
Choose which SkillNote skill collection is active for this Codex project. Use when the user says "change collection", "switch skills", "use frontend skills", or "show collections".
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 and push reusable skills to SkillNote from Codex when repeated instructions are detected or the user says "create a skill", "save this pattern", "push a skill". Guides drafting, review, collection selection, and 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。
Rate a SkillNote skill after using it. Called after applying a skill to provide 1-5 rating and outcome.
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。
设计 AI 架构，编写 Prompt，构建 RAG 系统和 LangChain 应用
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 AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and 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。
Design, review, and improve agent workflows & agent using SSOT, SRP, Fail Fast principles. Supports Prompt Chaining, Parallelization, Orchestrator-Workers 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。
Guide creation of focused single-purpose agents following the One Agent One Prompt One Purpose principle. Use when designing new agents, refactoring general agents into specialists, or optimizing agent context for a single task.
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。
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent 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。
Run prompt with OpenAI or Gemini provider via agent CLI
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。
Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
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 specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
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。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code 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。
Add a professional footer to README files with engagement prompts, community channels, and call-to-action for subscriptions. Use when you need to enhance your README with a footer that encourages interaction and builds community.
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。
Especialista en configuración de agentes de IA: templates, tools, models, prompts y seed 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。
Use when launching Clownfish in GitHub Actions to create or update one guarded GitHub implementation PR from issue/PR refs, a ClawSweeper report, a custom maintainer prompt, or opt any PR into ClawSweeper-reviewed Clownfish automerge.
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。
Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.
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 you want to benchmark and improve an existing repo skill through repeated evals, prompt mutations, and score tracking.
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。
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
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 and research the GitHub Copilot app desktop experience. Use for driving Copilot App sessions, creating isolated app experiments, mapping app UI/accessibility controls, taking app screenshots, saving PNG captures, optimizing WebP images, testing prompts without submitting, using app/session APIs, canvases, automations, macOS Accessibility, AppleScript, and safe end-to-end Copilot App workflow probes.
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。
Make a papercut reel, video or explainer end to end. Short vertical (9:16) cut-paper explainers from a link (repo, product page, announcement), idea to mp4. Also routes partial asks to the stage skills and hands out the style prompt for other tools.
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。