Rag engineer

Skill bcastelino/agent-skills-kit/skills/rag-engineer

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...From its SKILL.md

Install

npx -y skills add bcastelino/agent-skills-kit --skill rag-engineer

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SKILL.md

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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: embedding-strategies, ai-engineer

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most rag retrieval skills give

Counted across 199 of the 213 authors here whose files we hold, read 2026-09-06

Enable caching for frequent queriesin 14 of 199, across 5 files
Enable MMR for diverse resultsin 12 of 199, across 5 files
Enable binary quantization to reduce memoryin 11 of 199, across 4 files
Initialize the database with dimensions matching the embedding modelin 11 of 199, across 4 files
Start the similarity threshold at 0.7in 11 of 199, across 4 files
Check database statistics when diagnosing slow searchin 11 of 199, across 4 files
Export and import vectors as JSONin 10 of 199, across 3 files
Match index dimension to the embedding modelin 10 of 199, across 9 files
Order filters cheap before expensivein 9 of 199, across 2 files
Generate a runnable scaffold in the user's stackin 9 of 199, across 2 files
Recommend multi-action scoring when frequent tuning is expectedin 9 of 199, across 2 files
Batch store documents for bulk insertsin 9 of 199, across 2 files

Said here and by no other author read

Use sentence boundaries, not token limits
Detect topic shifts with embedding similarity
Add metadata to chunks for filtering
Index chunks at multiple sizes
Apply relevance thresholds to retrieved context
Refresh embeddings when source documents change

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.