Claude MarketplacesSubmit
giuseppe-trisciuoglio avatar

Rag

giuseppe-trisciuoglio/developer-kit

This handles the full RAG pipeline from document ingestion through retrieval, covering chunking strategies, embedding generation, vector storage, and query optimization. You get practical guidance on picking vector databases (Pinecone for production, Chroma for local dev) and embedding models based on your constraints. The examples show real LangChain4j code for common patterns like metadata filtering and multi-source retrieval. What's helpful here is the chunking advice, 500-1000 tokens with 10-20% overlap, which actually matters for quality. The security warnings about prompt injection in retrieved documents are worth reading. If you're building a chatbot over company docs or a semantic search system, this covers the decisions you need to make and includes enough error handling patterns to avoid the obvious mistakes.

skills.sh installs
3.9K
GitHub stars
369
last updated
Aug 21, 2026
View on GitHub

Install

$ npx -y skills add giuseppe-trisciuoglio/developer-kit --skill rag --agent claude-code

Installs into .claude/skills of the current project.

Files

RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

Overview

This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation.

When to Use

  • Building Q&A systems over proprietary documents
  • Creating chatbots with factual information from knowledge bases
  • Implementing semantic search with natural language queries
  • Reducing hallucinations with grounded, sourced responses
  • Building documentation assistants and research tools
  • Enabling AI systems to access domain-specific knowledge

Instructions

Step 1: Choose Vector Database

Select based on your requirements:

RequirementRecommended
Production scalabilityPinecone, Milvus
Open-sourceWeaviate, Qdrant
Local developmentChroma, FAISS
Hybrid searchWeaviate with BM25

Step 2: Select Embedding Model

Use CaseModel
General purposetext-embedding-ada-002
Fast and lightweightall-MiniLM-L6-v2
Multilinguale5-large-v2
Best performancebge-large-en-v1.5
SKILL.md on GitHub

Sponsors

Promote your product hereReach 125k+ visitors monthlyPromote your product hereReach 125k+ visitors monthlyAgent CaffeineSponsoredClose your lid, come back to finished workSnugSponsoredThe clipboard manager for the AI eraPromote your product hereReach 125k+ visitors monthlyWaxSponsoredBlock anything with supervision, without erasing your iPhonePromote your product hereReach 125k+ visitors monthlyknobsSponsoredSee your site on any phone, any day, in any language

Details

Repository
github.com/giuseppe-trisciuoglio/developer-kit
Category
AI & Agent Building
First seen
May 16, 2026

More from giuseppe-trisciuoglio/developer-kit

All 102

Recommended

More AI & Agent Building
1giuseppe-trisciuoglio avatarlangchain4j-ai-services-patternsgiuseppe-trisciuoglio/developer-kitProvides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in 3.9Kskills.sh installs
2giuseppe-trisciuoglio avatarlangchain4j-vector-stores-configurationgiuseppe-trisciuoglio/developer-kitProvides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/p3.8Kskills.sh installs
3netlify avatarnetlify-ai-gatewaynetlify/context-and-toolsReference for Netlify AI Gateway — the managed proxy that routes calls to OpenAI, Anthropic, and Google Gemini SDKs without provider API keys. Use this skill an3.3Kskills.sh installs
4arize-ai avatararize-ai-provider-integrationarize-ai/arize-skillsCreates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM pro3.1Kskills.sh installs
5getsentry avatarsentry-setup-ai-monitoringgetsentry/sentry-for-aiSetup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, track conversations, or instrument OpenAI/Anthropic/Verce3Kskills.sh installs
6starchild-ai-agent avatarbyok-custom-modelstarchild-ai-agent/official-skillsRegister a custom LLM endpoint with your own API key for chat in Starchild. Use when adding a personal Anthropic, OpenAI, Grok, Qwen, DeepSeek, Meta (Muse Spark2.7Kskills.sh installs