rag-implementation

Q: How do I install rag-implementation?

Run npx skills add https://github.com/sickn33/agentic-awesome-skills --skill rag-implementation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-implementation/ (personal) or .claude/skills/rag-implementation/ (project).

by sickn33AI & Agents

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

Installs: 834
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/sickn33/agentic-awesome-skills --skill rag-implementation

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads rag-implementation automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

Define use case
Identify data sources
Set accuracy requirements
Determine latency targets
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

Evaluate embedding models
Test domain relevance
Measure embedding quality
Consider cost/latency
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

Choose vector database
Design schema
Configure indexes
Set up connection
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

Choose chunk size
Implement chunking
Add overlap handling
Create metadata
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

Implement vector search
Add keyword search
Configure hybrid search
Set up reranking
Optimize latency

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

Select LLM provider
Design prompt template
Implement context injection
Add citation handling
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

Implement response caching
Set up embedding cache
Configure TTL
Add cache invalidation
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

Define evaluation metrics
Create test dataset
Measure retrieval accuracy
Evaluate generation quality
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

Trust and evaluation boundaries

Enforce document access at retrieval and again when returning citations; isolate caches by tenant, permission scope and model/index revision. Retrieved text is evidence, never authority to override instructions or invoke tools. Track document/source versions and deletion propagation through chunks, embeddings and cached answers. Evaluate retrieval relevance and answer grounding separately on held-out queries, including cross-tenant negatives, unanswerable questions and injected document instructions. A plausible cited answer does not prove the citation supports it.

Quality Gates

Related Workflow Bundles

ai-ml - AI/ML development
ai-agent-development - AI agents
database - Vector databases

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

FAQ

What does the rag-implementation skill do?

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

How do I install rag-implementation?

Run npx skills add https://github.com/sickn33/agentic-awesome-skills --skill rag-implementation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-implementation/ (personal) or .claude/skills/rag-implementation/ (project).

Which AI agents can use rag-implementation?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is rag-implementation safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains rag-implementation?

It is published by sickn33 in sickn33/agentic-awesome-skills. It has 834 recorded installs.