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Claude Skills by majiayu000
github.com/majiayu00015,497 skills907 installs34,333 views
- Prompt Analyzer Ttmouse Skills 2提示词分析与洞察 - 查看Prompt详情、对比差异、推荐相似提示词、元素库统计Votes: 0GitHub stars: 666
- Prompt Engineering Skill```python template = """ Use the following context to answer the question. If you don't know, say "I don't know based on the provided context."Votes: 0GitHub stars: 666
- Prompt Exec Jordangunn Nunchuck 3Execute the forged prompt exactly as written. Requires explicit consent and a ready artifact. Deletes artifact after successful execution.Votes: 0GitHub stars: 666
- Prompt Improver Existential Birds BeagleOptimize prompts for code-related tasks following Claude best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.Votes: 0GitHub stars: 666
- Prompt SenseiStage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.Votes: 0GitHub stars: 666
- Prompton Code InterpreterSimulate the execution of Prompton, a Python-like language where `prompt'...'` expressions evaluate to the result of the query inside the quotes.Votes: 0GitHub stars: 666
- Pronunciation SpecialistScans lyrics for pronunciation risks and prevents Suno mispronunciations. Use when writing lyrics with proper nouns, technical terms, homographs, or non-English words.Votes: 0GitHub stars: 666
- Protocol Simhacker MoollmK-lines as semantic activation — the name activates the tradition.Votes: 0GitHub stars: 666
- Provenance AuditAI generation provenance and audit trail tracking. Records decision factors, data lineage, reasoning chains, confidence scoring, and cost tracking for AI-generated content.Votes: 0GitHub stars: 666
- Pun DecompositionPun Decomposition Skill (MINUS -1)Votes: 0GitHub stars: 666
- Qdrant SparseQdrant sparse vector operations: collection creation with SparseVectorParams, Modifier.IDF for miniCOIL/SPLADE/BM42, upserting SparseVector points, sparse search, hybrid search with prefetch + RRF/DBSF fusion, converting model outputs to SparseVector format, payload filtering, and performance tuning. Covers the sparse vector gap not handled by the official Qdrant MCP (which only supports dense vectors via FastEmbed).Votes: 0GitHub stars: 666
- Qdrant Thinkfleetai Thinkfleet EngineManage Qdrant vector database — collections, points, and vector search via the REST API.Votes: 0GitHub stars: 666
- Query DecompositionQuery decomposition for multi-concept retrieval. Use when handling complex queries spanning multiple topics, implementing multi-hop retrieval, or improving coverage for compound questions.Votes: 0GitHub stars: 666
- Qwen CoderProvides Qwen Coder CLI delegation workflows for coding tasks using Qwen2.5-Coder and QwQ models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Qwen for tasks such as code generation, refactoring, debugging, or architectural analysis. Triggers on "use qwen", "use qwen coder", "delegate to qwen", "ask qwen", "second opinion from qwen", "qwen opinion", "continue with qwen", "qwen session".Votes: 0GitHub stars: 666
- Qwen DelegationDelegates tasks to Qwen CLI via delegation-core for Alibaba's models. Use when delegation-core selects Qwen or large-context batch processing is needed.Votes: 0GitHub stars: 666
- Qwen Wsp Enhancement Foundup Foundups AgentEnhance WSP protocols using Qwen strategic analysis and 0102 supervision. Use when enhancing WSPs, analyzing protocol gaps, generating WSP recommendations, or coordinating multi-WSP updates.Votes: 0GitHub stars: 666
- Rag And Vector SearchUse when building RAG systems, implementing semantic/hybrid search, selecting vector databases, tuning retrieval quality, or choosing chunking and embedding strategies.Votes: 0GitHub stars: 666
- Rag Architect Alexander Danilenko Cortex Ai SkillsUse when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.Votes: 0GitHub stars: 666
- Rag Architecture Fabioc Aloha Airs Data AnalysisBuild retrieval-augmented generation systems that ground LLMs in your data.Votes: 0GitHub stars: 666
- Rag Architecture PatternsRAG (Retrieval-Augmented Generation) architecture patterns cover systemVotes: 0GitHub stars: 666
- Rag Design PatternsAutomatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, vector database patterns, embedding management, reranking, and retrieval optimization.Votes: 0GitHub stars: 666
- Rag DesignProduction-grade RAG (Retrieval-Augmented Generation) system design patterns from OpenClaw. Use when implementing semantic search, vector retrieval, memory systems, or knowledge bases. Covers hybrid search (vector + keyword), embedding providers, MMR re-ranking, temporal decay, and enterprise features.Votes: 0GitHub stars: 666
- Rag Engineer Bcastelino Agent Skills KitExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...Votes: 0GitHub stars: 666
- Rag Engineer Dokhacgiakhoa Antigravity IdeExpert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.Votes: 0GitHub stars: 666
- Rag EvaluationComprehensive guide to evaluating Retrieval-Augmented Generation systems including retrieval metrics, generation quality, faithfulness, and end-to-end evaluation frameworksVotes: 0GitHub stars: 666
- Rag ExpertExpert in Retrieval-Augmented Generation systems - knowledge bases, chunking strategies, embedding optimization, and production RAG architecturesVotes: 0GitHub stars: 666
- Rag Giuseppe Trisciuogli Developer KitImplements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.Votes: 0GitHub stars: 666
- Rag Icewhaletech ToolfsSemantic search over vector databases for document retrieval. Use this skill when the user requests searching documents, finding relevant content, or performing semantic queries such as "Search for information about X", "Find documents related to Y", or "Query the knowledge base".Votes: 0GitHub stars: 666
- Rag Implementation Aisa Group Skill InjectBuild 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.Votes: 0GitHub stars: 666
- Rag Implementation Amnadtaowsoam CerebraskillsComprehensive guide for Retrieval-Augmented Generation (RAG) implementationVotes: 0GitHub stars: 666
- Rag Implementation Bcastelino Agent Skills KitRAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. Use when building RAG systems.Votes: 0GitHub stars: 666
- Rag Implementation Dokhacgiakhoa Antigravity IdeBuild 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.Votes: 0GitHub stars: 666
- Rag Implementation Tringo0108 Z CommandBuild 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.Votes: 0GitHub stars: 666
- Rag Implementation Wshobson AgentsBuild 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.Votes: 0GitHub stars: 666
- Rag ImplementerImplement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.Votes: 0GitHub stars: 666
- Rag InfrastructureBuild and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.Votes: 0GitHub stars: 666
- Rag Juanre LlmemoryUse when building Retrieval-Augmented Generation systems - covers document ingestion, hybrid search retrieval, reranking results, and prompt augmentation for accurate LLM responses grounded in your knowledge baseVotes: 0GitHub stars: 666
- Rag Patterns Agentient VibekitVertex AI RAG Engine integration patterns for grounding agent responses in private data sources including corpus management, retrieval tool creation, and citation extraction. PROACTIVELY activate for: (1) RAG pipeline integration and Vertex AI RAG Engine setup, (2) corpus creation and document ingestion, (3) retrieval tool configuration and grounding metadata parsing. Triggers: "add rag", "rag pipeline", "vertex ai search"Votes: 0GitHub stars: 666
- Rag PatternsRetrieval-Augmented Generation patterns and best practices. Implement chunking, embedding, retrieval, reranking, and generation pipelines. Use for knowledge-grounded AI, document QA, and semantic search applications.Votes: 0GitHub stars: 666
- Rag PersonalizerTransform textbook content based on the 10-dimension user profile to provide personalized learning experiences. Agent: AIEngineerVotes: 0GitHub stars: 666
- Rag Pipeline Builder Eddiebe147 Claude SettingsBuild retrieval-augmented generation systems that ground LLM responses in your dataVotes: 0GitHub stars: 666
- Rag Pipeline BuilderDesigns retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".Votes: 0GitHub stars: 666
- Rag QueryConsulta ao corpus RAG do projeto. Busca conhecimento em decisoes, documentacao, learnings e padroes armazenados. Use quando: buscar decisoes passadas, encontrar documentacao, recuperar padroes.Votes: 0GitHub stars: 666
- Rag RetrievalRetrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing context from retrieved documents, adding citations, or implementing hybrid search.Votes: 0GitHub stars: 666
- Rag System BuilderBuild Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.Votes: 0GitHub stars: 666
- Rag SystemsBuild Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and grounding LLM responses in factual data.Votes: 0GitHub stars: 666
- Ralph Prompt GeneratorUse when a user has a rough prompt that needs a staged prompt-improvement workflow for `$ralph-wiggum-codex`, with saved draft/final files and explicit review checkpoints.Votes: 0GitHub stars: 666
- Ralph Prompt Multi TaskGenerate Ralph-compatible prompts for multiple related tasks. Creates phased prompts with sequential milestones, cumulative progress tracking, and phase-based completion promises. Use when creating prompts for CRUD implementations, multi-step features, staged migrations, or any work requiring multiple distinct but related tasks.Votes: 0GitHub stars: 666
- Ralph Prompt ProjectGenerate Ralph-compatible prompts for entire projects from scratch. Creates comprehensive prompts with architecture phase, implementation phases, testing, and documentation. Use when building complete applications, libraries, CLI tools, or any greenfield project requiring end-to-end development.Votes: 0GitHub stars: 666
- Ralph TechniqueThe Ralph Wiggum technique for minimal, declarative prompts that enable loop-based autonomous execution.Votes: 0GitHub stars: 666