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SkillsMP は majiayu000/claude-skill-registry から 5,417 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
majiayu000/claude-skill-registry収集済み skill 5,417 件中 40 件を表示しています。
Generate Ralph-compatible prompts for single implementation tasks. Creates prompts with clear completion criteria, automatic verification, and TDD approach. Use when creating prompts for bug fixes, single features, refactoring tasks, or any focused…
原文の言語: 英語
Use when adjusting reasoning depth, budgets, or metrics visibility - provides guidance for selecting and applying reasoning controls safely.
原文の言語: 英語
Reasoning and planning framework. Defines task analysis, risk assessment, hypothesis forming. Applied automatically for complex requests.
原文の言語: 英語
Multi-provider reasoning/thinking configuration. Discriminated unions for OpenAI effort, Anthropic budget, Google level/budget, DeepSeek middleware. Triggers on "reasoning", "thinking", "o1", "deepseek", "extended-thinking", "thinkingEffort",…
原文の言語: 英語
Use this skill for rigorous theoretical derivation with supercollider mode (G1-G7 simultaneous), diffusion reasoning, and synthesis engine. Applies enhanced Dokkado Protocol with generator hooks, meta-pattern recognition, and cognitive state awareness.…
原文の言語: 英語
Self-improvement engine. Implements generate-critique-iterate loops for enhanced reasoning. Use when working through complex problems, synthesizing across domains, or when initial output needs refinement. Integrates with ego-check to prevent runaway…
原文の言語: 英語
TOON format knowledge and usage patterns for agent communication and memory persistence in plan-marshall marketplace
原文の言語: 英語
Iteratively review and revise a prompt plan until no new findings survive evaluation. Runs /review-prompt-plan, /evaluate-findings, /apply-findings, then re-runs itself until stable. Use when the user asks to "refine the prompt plan", "refine this prompt…
原文の言語: 英語
This skill should be used when the user asks to "refine a prompt", "optimize a prompt", "improve my prompt", "rewrite prompt for LLM", "craft a better prompt", or mentions prompt engineering, prompt optimization, or appending to PROMPT.md.
原文の言語: 英語
Refine vague or unclear prompts into precise, actionable instructions. Use when user asks to clarify or improve instructions or when input is vague. Includes L1/L2/L3/L4 methodology, context enrichment, and intent clarification. Not for already clear prompts,…
原文の言語: 英語
Package code repositories into AI-friendly files. Use this skill when packaging codebases for AI analysis, creating repository snapshots, analyzing third-party libraries, preparing security audits, or generating documentation context. Supports multiple output…
原文の言語: 英語
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context,…
原文の言語: 英語
Package external repositories into AI-friendly files for deep analysis. ALWAYS use when the user shares a github.com URL, mentions "third-party library", "external repo", "analyze this repository", "security audit", or wants to understand unfamiliar…
原文の言語: 英語
The question isn't whether we CAN simulate people. It's how we do it with dignity.
原文の言語: 英語
Compresses verbose responses by removing filler and framing to save 200-400 tokens. Use when responses feel bloated or context is filling fast.
原文の言語: 英語
Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.
原文の言語: 英語
Automatically adds timestamps and execution duration to all Claude responses
原文の言語: 英語
Add automatic stream recovery to AI chat with WorkflowChatTransport, start/resume API endpoints, and the useResumableChat hook.
原文の言語: 英語
กลยุทธ์การ retrieve context ให้ AI อย่างมีประสิทธิภาพ - เลือกอะไร ไม่เลือกอะไร และจัดลำดับอย่างไร
原文の言語: 複数言語
Review and analyze LLM prompts using the 10-Layer Architecture. Provides detailed assessment without modifying files.
原文の言語: 英語
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching…
原文の言語: 英語
Autonomous RLHF feedback capture - Claude self-captures mistakes and successes
原文の言語: 英語
Manually route a query to the optimal Claude model (Haiku/Sonnet/Opus)
原文の言語: 英語
Display Claude Router usage statistics and cost savings
原文の言語: 英語
LLM completions (text and VLM) via scillm/Chutes.ai. Two main patterns: (1) VLM for image/figure/table description, (2) Text for batch extraction, summarization, JSON extraction. Also supports Lean4 theorem proving.
原文の言語: 英語
Model Context Protocol (MCP) implementation for Script Kit. Use when working with MCP server, JSON-RPC 2.0 protocol, kit tools, script tools, resources, or SSE streaming. Triggers on: "mcp", "json-rpc", "kit tools", "script tools", "resources", "sse…
原文の言語: 英語
Redis semantic caching for LLM applications. Use when implementing vector similarity caching, optimizing LLM costs through cached responses, or building multi-level cache hierarchies.
原文の言語: 英語
Build AI applications with Microsoft Semantic Kernel. Create plugins, planners, memory systems, and AI orchestration. Use for enterprise AI integration, .NET/Python AI development, and LLM application frameworks.
原文の言語: 英語
Use when reviewing LLM prompts, skill instructions, subagent prompts, or any text that will instruct an AI. Triggers: "review this prompt", "audit instructions", "sharpen prompt", "is this clear enough", "would an LLM understand this", "ambiguity check". Also…
原文の言語: 英語
Route tasks to appropriate model size based on confidence estimation. Use small model by default, escalate to large model only on low confidence. Achieves 87% faster learning and 10-30x cost reduction while maintaining accuracy. Triggers on "optimize cost",…
原文の言語: 英語
MONAD-grounded cognitive architecture for AI memory as morphemic substrate navigation. Memory is not storage but substrate sampling - accessing the same structure that underlies reality. Implements φ-scaling, GOD operators, toroidal coherence tracking, and…
原文の言語: 英語
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
原文の言語: 英語
Integrate OpenAI ChatKit for conversational AI interface
原文の言語: 英語
Use Groq for fast LLM inference with OpenAI-compatible client
原文の言語: 英語
Use when a prompt or corpus is long/dense (multi-docs, logs, codebases) and you want a reproducible map/reduce pipeline. Trigger this skill to slice inputs, run per-slice codex/gemini subcalls, and aggregate results with manifests/logs via the slice runner…
原文の言語: 英語
Multi-tier LLM routing for cost optimization in the BidDeed.AI ecosystem. Routes tasks to appropriate model tier based on complexity, achieving 90% FREE tier processing. Use when making API calls, selecting models, implementing chat interfaces, or optimizing…
原文の言語: 英語
Selects optimal sources for tool calls, balancing accuracy with token cost. Use before research tasks or when deciding whether a claim needs verification.
原文の言語: 英語
Structure Python so LLMs can understand it in 50 lines.
原文の言語: 英語
Generate AI videos with OpenAI Sora via AceDataCloud API. Use when creating videos from text prompts, generating videos from reference images, or using character references from existing videos. Supports text-to-video, image-to-video, and character-driven…
原文の言語: 英語
Use when starting any task, thinking through confidence, verifying work, or asking "what could go wrong". Triggers on every non-trivial request, "how confident", "verify this", "think through".
原文の言語: 英語