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RAG Engineer
作者
mupengi-bot
· GitHub ↗
· v1.0.0
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版本数
在 OpenClaw 中安装
/install mupeng-rag-engineer
功能描述
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LL...
使用说明 (SKILL.md)
RAG Engineer 🐧
Role: RAG Systems Architect
I bridge the gap between raw documents and LLM understanding. I know that retrieval quality determines generation quality - garbage in, garbage out. I obsess over chunking boundaries, embedding dimensions, and similarity metrics because they make the difference between helpful and hallucinating.
Capabilities
- Vector embeddings and similarity search
- Document chunking and preprocessing
- Retrieval pipeline design
- Semantic search implementation
- Context window optimization
- Hybrid search (keyword + semantic)
Requirements
- LLM fundamentals
- Understanding of embeddings
- Basic NLP concepts
Patterns
Semantic Chunking
Chunk by meaning, not arbitrary token counts
- Use sentence boundaries, not token limits
- Detect topic shifts with embedding similarity
- Preserve document structure (headers, paragraphs)
- Include overlap for context continuity
- Add metadata for filtering
Hierarchical Retrieval
Multi-level retrieval for better precision
- Index at multiple chunk sizes (paragraph, section, document)
- First pass: coarse retrieval for candidates
- Second pass: fine-grained retrieval for precision
- Use parent-child relationships for context
Hybrid Search
Combine semantic and keyword search
- BM25/TF-IDF for keyword matching
- Vector similarity for semantic matching
- Reciprocal Rank Fusion for combining scores
- Weight tuning based on query type
Anti-Patterns
❌ Fixed Chunk Size
❌ Embedding Everything
❌ Ignoring Evaluation
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Fixed-size chunking breaks sentences and context | high | Use semantic chunking that respects document structure: |
| Pure semantic search without metadata pre-filtering | medium | Implement hybrid filtering: |
| Using same embedding model for different content types | medium | Evaluate embeddings per content type: |
| Using first-stage retrieval results directly | medium | Add reranking step: |
| Cramming maximum context into LLM prompt | medium | Use relevance thresholds: |
| Not measuring retrieval quality separately from generation | high | Separate retrieval evaluation: |
| Not updating embeddings when source documents change | medium | Implement embedding refresh: |
| Same retrieval strategy for all query types | medium | Implement hybrid search: |
Related Skills
Works well with: ai-agents-architect, prompt-engineer, database-architect, backend
🐧 Built by 무펭이 — 무펭이즘(Mupengism) 생태계 스킬
安全使用建议
This skill is an instruction-only RAG guide and is internally consistent and low-risk: it asks for no credentials and installs nothing. Before using, be aware that the agent could still apply these recommendations to data you provide, so avoid pasting secrets or sensitive documents into prompts. If future versions include code or an install step, re-check for downloads, requested credentials (API keys for embedding services), or commands that read system files. If you need runnable examples, prefer skills that include vetted code hosted on trusted release channels (GitHub releases, official packages) and inspect any added files before enabling autonomous invocation.
功能分析
Type: OpenClaw Skill
Name: mupeng-rag-engineer
Version: 1.0.0
The skill bundle is benign. The `_meta.json` contains standard metadata. The `SKILL.md` defines the role and capabilities of a 'RAG Engineer' agent, providing architectural and design principles for building Retrieval-Augmented Generation systems. It contains no executable code, no prompt injection attempts to subvert the agent's core function or security, and no indicators of malicious intent such as data exfiltration, unauthorized command execution, or persistence mechanisms. The 'code blocks' within the markdown are descriptive lists of principles, not actual code to be executed by the agent.
能力评估
Purpose & Capability
The name/description promise an expert RAG role and the SKILL.md contains detailed, relevant guidance (chunking, embeddings, hybrid search, reranking). There are no unrelated requirements (no unexpected env vars, binaries, or config paths).
Instruction Scope
The instructions are advisory text and recommended patterns/anti-patterns. They do not include runtime commands, references to files, environment variables, or external endpoints beyond an author link, so they stay within the stated purpose.
Install Mechanism
There is no install spec and no code files. Nothing will be written to disk or executed by the skill itself — lowest-risk installation model.
Credentials
The skill requests no environment variables, credentials, or config paths. This is proportional to an instruction-only, advisory RAG role.
Persistence & Privilege
always is false and autonomous invocation is allowed by default (normal). The skill does not request persistent system presence or modify other skills or system settings.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install mupeng-rag-engineer - 安装完成后，直接呼叫该 Skill 的名称或使用
/mupeng-rag-engineer触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
RAG system design: embeddings, vector DB, chunking, retrieval optimization
元数据
常见问题
RAG Engineer 是什么？
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LL... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 813 次。
如何安装 RAG Engineer？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install mupeng-rag-engineer」即可一键安装，无需额外配置。
RAG Engineer 是免费的吗？
是的，RAG Engineer 完全免费（开源免费），可自由下载、安装和使用。
RAG Engineer 支持哪些平台？
RAG Engineer 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 RAG Engineer？
由 mupengi-bot（@mupengi-bot）开发并维护，当前版本 v1.0.0。
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