Rag Engineer
技能 活跃Expert 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.
To provide expert guidance and best practices for building effective Retrieval-Augmented Generation systems, ensuring optimal LLM performance through quality retrieval.
功能
- Expertise in RAG systems
- Masters embedding models and vector databases
- Detailed chunking and retrieval strategies
- Guidance on semantic and hybrid search
使用场景
- Building RAG systems
- Implementing vector search
- Optimizing document retrieval for LLMs
- Designing semantic search pipelines
非目标
- Developing LLM base models
- Managing general database operations
- Providing generic NLP consulting
Trust
- warning:Issues Attention17 issues opened and 4 closed in the last 90 days indicates a low closure rate, suggesting slow maintainer response.
安装
npx skills add davila7/claude-code-templates通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
类似扩展
Rag Architect
100Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Hybrid Search Implementation
98Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Chat Format
100Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Embeddings
99Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
AgentDB Vector Search
99Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.