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Agent skill

RAG Implementation

by majiayu000 in majiayu000/claude-skill-registry

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

MITAuto-check passedAI & LLM Engineering

Install RAG Implementation

skills CLI
$ npx skills add majiayu000/claude-skill-registry --skill rag-implementation -a claude-code

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

GitHub CLI
$ gh skill install majiayu000/claude-skill-registry rag-implementation --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/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-llm/rag-implementation-clinscott-cstar .claude/skills/rag-implementation && 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-implementation
GitHub stars
666
Used in
3 other repos
Token cost
~1.1k tokens
SKILL.md length
363 words
Files
2
Skills in repo
971
Repo updated
First seen
Licence
MIT

At a glance

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

  • Works in 8 steps: Requirements Analysis → Embedding Selection → Vector Database Setup → …
  • Tasks that involve Retrieval-augmented generation
  • SKILL.md covers Overview, When to Use This Workflow, Workflow Phases and RAG Architecture, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

RAG Implementation is an agent skill from majiayu000/claude-skill-registry. RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Vector databases and Embeddings. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

  • Tasks that involve Retrieval-augmented generation
  • Tasks that involve Vector databases
  • Tasks that involve Embeddings

Example prompts

  • “/rag-implementation”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Requirements Analysis
  2. Embedding Selection
  3. Vector Database Setup
  4. Chunking Strategy
  5. Retrieval Implementation
  6. LLM Integration
  7. Caching
  8. Evaluation

What it can do on your machine

Read from SKILL.md and the folder at commit 000116a. 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.

    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 Implementation loads about 1.1k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 363 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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 majiayu000/claude-skill-registry at commit 000116a, republished under its MIT licence (© majiayu000). 363 words, ~1,071 tokens.

Download SKILL.mdSave it as .claude/skills/rag-implementation/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
rag-implementation
description
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
category
granular-workflow-bundle
risk
safe
source
personal
date_added
2026-02-27

RAG Implementation Workflow

Overview

Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.

When to Use This Workflow

Use this workflow when:

  • Building RAG-powered applications
  • Implementing semantic search
  • Creating knowledge-grounded AI
  • Setting up document Q&A systems
  • Optimizing retrieval quality

Workflow Phases

Phase 1: Requirements Analysis
Skills to Invoke
  • ai-product - AI product design
  • rag-engineer - RAG engineering
Actions
  1. Define use case
  2. Identify data sources
  3. Set accuracy requirements
  4. Determine latency targets
  5. Plan evaluation metrics
Copy-Paste Prompts
Use @ai-product to define RAG application requirements
Phase 2: Embedding Selection
Skills to Invoke
  • embedding-strategies - Embedding selection
  • rag-engineer - RAG patterns
Actions
  1. Evaluate embedding models
  2. Test domain relevance
  3. Measure embedding quality
  4. Consider cost/latency
  5. Select model
Copy-Paste Prompts
Use @embedding-strategies to select optimal embedding model
Phase 3: Vector Database Setup
Skills to Invoke
  • vector-database-engineer - Vector DB
  • similarity-search-patterns - Similarity search
Actions
  1. Choose vector database
  2. Design schema
  3. Configure indexes
  4. Set up connection
  5. Test queries
Copy-Paste Prompts
Use @vector-database-engineer to set up vector database
Phase 4: Chunking Strategy
Skills to Invoke
  • rag-engineer - Chunking strategies
  • rag-implementation - RAG implementation
Actions
  1. Choose chunk size
  2. Implement chunking
  3. Add overlap handling
  4. Create metadata
  5. Test retrieval quality
Copy-Paste Prompts
Use @rag-engineer to implement chunking strategy
Phase 5: Retrieval Implementation
Skills to Invoke
  • similarity-search-patterns - Similarity search
  • hybrid-search-implementation - Hybrid search
Actions
  1. Implement vector search
  2. Add keyword search
  3. Configure hybrid search
  4. Set up reranking
  5. Optimize latency
Show full SKILL.md (138 more words)Show less
Copy-Paste Prompts
Use @similarity-search-patterns to implement retrieval
Use @hybrid-search-implementation to add hybrid search
Phase 6: LLM Integration
Skills to Invoke
  • llm-application-dev-ai-assistant - LLM integration
  • llm-application-dev-prompt-optimize - Prompt optimization
Actions
  1. Select LLM provider
  2. Design prompt template
  3. Implement context injection
  4. Add citation handling
  5. Test generation quality
Copy-Paste Prompts
Use @llm-application-dev-ai-assistant to integrate LLM
Phase 7: Caching
Skills to Invoke
  • prompt-caching - Prompt caching
  • rag-engineer - RAG optimization
Actions
  1. Implement response caching
  2. Set up embedding cache
  3. Configure TTL
  4. Add cache invalidation
  5. Monitor hit rates
Copy-Paste Prompts
Use @prompt-caching to implement RAG caching
Phase 8: Evaluation
Skills to Invoke
  • llm-evaluation - LLM evaluation
  • evaluation - AI evaluation
Actions
  1. Define evaluation metrics
  2. Create test dataset
  3. Measure retrieval accuracy
  4. Evaluate generation quality
  5. Iterate on improvements
Copy-Paste Prompts
Use @llm-evaluation to evaluate RAG system

RAG Architecture

User Query -> Embedding -> Vector Search -> Retrieved Docs -> LLM -> Response
                |              |              |              |
            Model         Vector DB     Chunk Store    Prompt + Context

Quality Gates

  • Embedding model selected
  • Vector DB configured
  • Chunking implemented
  • Retrieval working
  • LLM integrated
  • Evaluation passing
  • ai-ml - AI/ML development
  • ai-agent-development - AI agents
  • database - Vector databases

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/ai-llm/rag-implementation-clinscott-cstar of majiayu000/claude-skill-registry.

  • SKILL.md
  • metadata.json

Open the folder on GitHubat commit 000116a

Used in 3 other repositories

We found 17 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

Compare with similar skills

RAG Implementation 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 Implementation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
RAG Implementation this skillmajiayu000/claude-skill-registry6663 repos~1.1kAutomated safety check: PassMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence
Pgvector Semantic Searchtimescale/pg-aiguide1.9k1 repos~3.8kAutomated safety check: PassApache-2.0
RAG ArchitectJeffallan/claude-skills12k1 repos~2kAutomated safety check: PassMIT
RAG Implementationwshobson/agents40k9 repos~1.1kAutomated safety check: PassMIT

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Categories

Questions about RAG Implementation

What does RAG Implementation do?

RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. RAG Implementation is an agent skill from majiayu000/claude-skill-registry. RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

When should I use RAG Implementation?

RAG Implementation fits situations like: tasks that involve Retrieval-augmented generation; tasks that involve Vector databases; tasks that involve Embeddings.

How do I install RAG Implementation in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill rag-implementation -a claude-code`. Or copy the skill folder (skills/ai-llm/rag-implementation-clinscott-cstar in majiayu000/claude-skill-registry) into .claude/skills/rag-implementation in your project. Claude Code loads it when a task matches its description.

How do I install RAG Implementation in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill rag-implementation -a codex`. Or copy the skill folder (skills/ai-llm/rag-implementation-clinscott-cstar in majiayu000/claude-skill-registry) into .agents/skills/rag-implementation in your project. Codex loads it when a task matches its description.

Can I use RAG Implementation 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 majiayu000/claude-skill-registry --skill rag-implementation -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-implementation, .gemini/skills/rag-implementation, .github/skills/rag-implementation and .opencode/skills/rag-implementation in your project.

What does RAG Implementation need to run?

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

Does RAG Implementation 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 Implementation 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 Implementation use?

RAG Implementation 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 Implementation use?

About 1.1k tokens (SKILL.md is roughly 4.3k 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 Implementation?

Skills that share tags, products or a category with RAG Implementation: 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 Architect (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains RAG Implementation?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.