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Agent skill

RAG Engineer

by davila7 in davila7/claude-code-templates

Expert in building Retrieval-Augmented Generation systems. An agent skill from davila7/claude-code-templates.

MITAuto-check passedAI & LLM Engineering

Install RAG Engineer

skills CLI
$ npx skills add davila7/claude-code-templates --skill rag-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates rag-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/ai-research/rag-engineer .claude/skills/rag-engineer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rag-engineer
GitHub stars
32k
Used in
5 other repos
Token cost
~729 tokens
SKILL.md length
215 words
Files
1
Skills in repo
478
Repo updated
First seen
Licence
MIT

At a glance

Expert in building Retrieval-Augmented Generation systems. An agent skill from davila7/claude-code-templates.

  • Semantic search
  • SKILL.md covers Capabilities, Requirements, Patterns and Anti-Patterns, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Document retrieval

What it does

RAG Engineer is an agent skill from davila7/claude-code-templates. 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.

Its SKILL.md is about 730 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Embeddings and Vector databases. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Semantic search
  • Document retrieval

Example prompts

  • “/rag-engineer”

What it can do on your machine

Read from SKILL.md and the folder at commit 46b4d8b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

RAG Engineer loads about 729 tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 215 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~729

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 46b4d8b, republished under its MIT licence (© davila7). 215 words, ~729 tokens.

Download SKILL.mdSave it as .claude/skills/rag-engineer/SKILL.md (or your agent's skills folder).
name
rag-engineer
description
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.
source
vibeship-spawner-skills (Apache 2.0)

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

javascript
- 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

javascript
- 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

Combine semantic and keyword search

javascript
- 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

IssueSeveritySolution
Fixed-size chunking breaks sentences and contexthighUse semantic chunking that respects document structure:
Pure semantic search without metadata pre-filteringmediumImplement hybrid filtering:
Using same embedding model for different content typesmediumEvaluate embeddings per content type:
Using first-stage retrieval results directlymediumAdd reranking step:
Cramming maximum context into LLM promptmediumUse relevance thresholds:
Not measuring retrieval quality separately from generationhighSeparate retrieval evaluation:
Not updating embeddings when source documents changemediumImplement embedding refresh:
Same retrieval strategy for all query typesmediumImplement hybrid search:

Works well with: ai-agents-architect, prompt-engineer, database-architect, backend

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cli-tool/components/skills/ai-research/rag-engineer of davila7/claude-code-templates.

Open the folder on GitHubat commit 46b4d8b

Used in 5 other repositories

We found 8 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 5 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

RAG Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

RAG Engineer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
RAG Engineer this skilldavila7/claude-code-templates32k5 repos~729Automated safety check: PassMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence
Pgvector Semantic Searchtimescale/pg-aiguide1.9k—~3.8kAutomated safety check: PassApache-2.0
RAG Implementationwshobson/agents40k9 repos~1.1kAutomated safety check: PassMIT
RAG ArchitectJeffallan/claude-skills12k—~2kAutomated safety check: PassMIT

Similar skills

  • Chroma Vector Database

    Orchestra-Research/AI-Research-SKILLs

    Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

    13k GitHub starsUsed in 7 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

  • Ms Agent Framework RAG

    shuyu-labs/WebCode

    Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.

    278 GitHub stars~1.1k tokensUpdated 3 mo ago
    AI & LLM EngineeringAuto-check passed

  • Pgvector Semantic Search

    timescale/pg-aiguide

    A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.

    1.9k GitHub stars~3.8k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed

  • RAG Implementation

    wshobson/agents

    Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.

    40k GitHub starsUsed in 9 repos~1.1k tokens
    AI & LLM EngineeringAuto-check passed

  • RAG Architect

    Jeffallan/claude-skills

    Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step.

    12k GitHub stars~2k tokensUpdated 5 days ago
    AI & LLM EngineeringAuto-check passed

  • Vector DB

    RightNow-AI/openfang

    Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

    18k GitHub stars~1k tokensUpdated 3 mo ago
    AI & LLM EngineeringAuto-check passed

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Categories

Questions about RAG Engineer

What does RAG Engineer do?

Expert in building Retrieval-Augmented Generation systems. An agent skill from davila7/claude-code-templates. RAG Engineer is an agent skill from davila7/claude-code-templates. Expert in building Retrieval-Augmented Generation systems.

When should I use RAG Engineer?

RAG Engineer fits situations like: semantic search; document retrieval.

How do I install RAG Engineer in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill rag-engineer -a claude-code`. Or copy the skill folder (cli-tool/components/skills/ai-research/rag-engineer in davila7/claude-code-templates) into .claude/skills/rag-engineer in your project. Claude Code loads it when a task matches its description.

How do I install RAG Engineer in Codex?

Run `npx skills add davila7/claude-code-templates --skill rag-engineer -a codex`. Or copy the skill folder (cli-tool/components/skills/ai-research/rag-engineer in davila7/claude-code-templates) into .agents/skills/rag-engineer in your project. Codex loads it when a task matches its description.

Can I use RAG Engineer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill rag-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rag-engineer, .gemini/skills/rag-engineer, .github/skills/rag-engineer and .opencode/skills/rag-engineer in your project.

What does RAG Engineer need to run?

SKILL.md names no scripts, command-line tools or credentials: RAG Engineer is instructions for the agent only.

Does RAG Engineer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is RAG Engineer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does RAG Engineer use?

RAG Engineer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does RAG Engineer use?

About 729 tokens (SKILL.md is roughly 2.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to RAG Engineer?

Skills that share tags, products or a category with RAG Engineer: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars) and RAG Implementation (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains RAG Engineer?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,483 GitHub stars. The repository holds 478 skills in this directory. The repository was last updated on October 9, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.