agentsclimarketplace

Rag implementation

Skill Ghosteken/agent-harness/skills/rag-implementation

Production-grade engineering skills and specialist agent personas for AI coding assistants. Orchestrates the full SDLC, from spec to ship, with structured verification gates, anti-rationalization checks, and senior-level engineering discipline.

Install
npx -y skills add Ghosteken/agent-harness --skill rag-implementation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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

SKILL.md

3.7 KB, as published. Nobody here has run it

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

  • 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

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

Actions

  1. Choose vector database
  2. Design schema
  3. Configure indexes
  4. Set up connection
  5. Test queries

Copy-Paste Prompts

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

Actions

  1. Implement vector search
  2. Add keyword search
  3. Configure hybrid search
  4. Set up reranking
  5. Optimize latency

Copy-Paste Prompts

Phase 6: LLM Integration

Skills to Invoke

Actions

  1. Select LLM provider
  2. Design prompt template
  3. Implement context injection
  4. Add citation handling
  5. Test generation quality

Copy-Paste Prompts

Phase 7: Caching

Skills to Invoke

  • 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

Phase 8: Evaluation

Skills to Invoke

  • llm-evaluation - LLM 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

Related Workflow Bundles

  • ai-agent-development - AI agents

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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.