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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 self-contained collaboration documents for sharing issues with external AI systems (Gemini, ChatGPT, etc.) with structural anti-skip enforcement (Execute-Verify-Record pattern at every step). Interactively gathers context, reads actual code files,…
原文の言語: 英語
Provides comprehensive guidance for Spring AI Alibaba including Alibaba Cloud AI services integration, model APIs, and AI application development. Use when the user asks about Spring AI Alibaba, needs to use Alibaba Cloud AI services, or integrate AI…
原文の言語: 中国語
Model Context Protocol (MCP) server implementation patterns with Spring AI. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates using Spring's official AI framework.
原文の言語: 英語
Provides comprehensive guidance for Spring AI including AI model integration, prompt templates, vector stores, and AI applications. Use when the user asks about Spring AI, needs to integrate AI models, implement RAG applications, or work with AI services in…
原文の言語: 中国語
This skill should be used when the user asks about "Effect Stream", "Stream.from", "Stream.map", "Stream.filter", "Stream.run", "streaming data", "async iteration", "Sink", "Channel", "Stream.concat", "Stream.merge", "backpressure", "Stream.fromIterable",…
原文の言語: 英語
Techniques for ensuring LLM responses adhere to strict JSON schemas, utilizing Pydantic models, JSON mode, and schema-based refusals. Triggers: structured-output, pydantic, json-schema, json-mode, llm-response-parsing.
原文の言語: 英語
次のタスクの内容（コーディング、リサーチ、リファクタリングなど）に応じて、最適なLLMモデルを提案する
Provides flag definitions and usage rules when double-dash flags (--flag-name) detected in user prompt - describes execution modes, tool selection, and behavioral modifications
原文の言語: 英語
Expert SuperClaude prompt engineering assistant that analyzes user needs and crafts optimal prompts using the full SuperClaude framework - commands, flags, personas, MCP servers, wave orchestration, parallel execution patterns, continuous execution…
原文の言語: 英語
タスクの複雑さに応じて Claude のモデルを自動的に切り替える。複雑なタスク（アーキテクチャ設計、大規模リファクタリング、難解なバグ修正）では opus を、単純なタスク（小さな修正、フォーマット、簡単な質問）では sonnet を自動選択。コストと品質のバランスを最適化。
Build neuro-symbolic LLM applications with Synalinks framework. Use when working with DataModel, Program, Generator, Module, training LLM pipelines, in-context learning, structured output, JSON operators, Branch/Decision control flow, FunctionCallingAgent,…
原文の言語: 英語
System prompt assembly for blah.chat AI backend. Multi-layer prompt construction with priority ordering, parallel context loading, memory truncation, budget awareness. Use when working with "system prompt", "base prompt", "identity memories", "custom…
原文の言語: 英語
Parse natural language task input and extract structured data (title, due date, priority, category) using Google Gemini AI
原文の言語: 英語
使用Tavily API进行网络搜索，获取实时信息、回答问题或研究主题
原文の言語: 中国語
Web search, online search, real-time search, internet search, Google alternative, Bing alternative, DuckDuckGo alternative, search the web, lookup online, find information, research,查询,搜索,搜索结果,网页搜索,联网搜索,实时搜索,网络查询,资料查找,信息检索,最新资讯,新闻搜索, Tavily Search API for…
原文の言語: 英語
Use Tavily Search API for optimized, real-time web search results for RAG. A pre-configured, cost-effective search tool.
原文の言語: 複数言語
"Tavily AI search API for LLM applications: web search, content extraction, site crawling, mapping, and research. Keywords: Tavily, AI search, RAG, web search API, LLM search, extract, crawl, map, research, tavily-python."
原文の言語: 英語
Executes Tavily AI search, extraction, crawling, mapping, and deep research via Python CLI. Use when web search, URL content extraction, site crawling, or multi-step research needed.
原文の言語: 英語
Web search, content extraction, crawling, and research capabilities using Tavily API. Use when you need to search the web for current information, extracting content from URLs, or crawling websites.
原文の言語: 英語
Step-by-step guidance for tensorrt LLM.
原文の言語: 英語
Deploy LLMs with Hugging Face Text Generation Inference. Configure quantization, continuous batching, and tensor parallelism. Use for production LLM serving, high-throughput inference, and model deployment.
原文の言語: 英語
CRITICAL - Read FIRST before any work. Strategies to minimize token usage and reduce costs by 60-80%.
原文の言語: 英語
Token usage monitoring and cost tracking with configurable thresholds. Reference this skill to understand pricing and limits.
原文の言語: 英語
Imported skill tools from langchain
原文の言語: 英語
Token-Oriented Object Notation (TOON) format expert for 30-60% token savings on structured data. Auto-applies to arrays with 5+ items, tables, logs, API responses, database results. Supports tabular, inline, and expanded formats with comma/tab/pipe…
原文の言語: 英語
Formats structured data using TOON v2.0 to minimize tokens while preserving readability. Use when outputs include tables, logs, events, or repeated records and token budgets matter. Triggers include "format table", "structured data", "TOON", "minimize…
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
Voice-first runtime steering + nightly deep analysis to learn per-user conversation priors.
原文の言語: 英語
Specifies guidelines for the AI assistant to provide accurate, thoughtful answers, admit when it doesn't know something, and be concise while ensuring clarity. This rule promotes trustworthy and helpf
原文の言語: 英語
Best practices for using the Council MCP server in Tzurot v3 development - When to consult external AI, how to structure prompts, model selection, and multi-turn conversations. Use when planning major changes or needing a second opinion.
原文の言語: 英語
Route tasks to small model by default, escalate to large model only on low confidence detection, achieving 87% faster learning and 10-30x cost reduction while maintaining accuracy. Use for cost optimization, confidence-based delegation, routine vs complex…
原文の言語: 英語
Utilizing Dynamic 4-bit quantization, FP8 training, and 8-bit optimizers to minimize VRAM usage without sacrificing accuracy. Triggers: quantization, dynamic 4-bit, fp8, bitsandbytes, adamw_8bit, qat.
原文の言語: 英語
Supervised fine-tuning using SFTTrainer, instruction formatting, and multi-turn dataset preparation with triggers like sft, instruction tuning, chat templates, sharegpt, alpaca, conversation_extension, and SFTTrainer.
原文の言語: 英語
Fine-tuning Speech-to-Text models like Whisper using Unsloth's optimized LoRA pipeline. Triggers: stt, whisper, transcription, audio fine-tuning, speech-to-text, audio normalization.
原文の言語: 英語
Fine-tune LLMs with Unsloth using GRPO or SFT. Supports FP8, vision models, mobile deployment, Docker, packing, GGUF export. Use when: train with GRPO, fine-tune, reward functions, SFT training, FP8 training, vision fine-tuning, phone deployment, docker…
原文の言語: 英語
Fine-tuning Text-to-Speech (TTS) models with Unsloth for voice cloning and synthetic speech (triggers: TTS, text-to-speech, voice cloning, Orpheus-TTS, audio fine-tuning, speech synthesis).
原文の言語: 英語
Fine-tuning multimodal vision-language models (Llama 3.2 Vision, Qwen2.5 VL) using optimized vision layers (triggers: vision models, multimodal, Llama 3.2 Vision, Qwen2.5 VL, UnslothVisionDataCollator, finetune_vision_layers).
原文の言語: 英語
Usage Optimization Skill
原文の言語: 英語
Detect user's technical level from first messages. Adjust all output language accordingly.
原文の言語: 英語
LLM specialist router to prompt engineering, fine-tuning, RAG, evaluation, and safety skills.
原文の言語: 英語