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giuseppe-trisciuoglio/developer-kit

Comprehensive developer toolkit providing reusable skills for Java/Spring Boot, TypeScript/NestJS/React/Next.js, Python, PHP, AWS CloudFormation, AI/RAG, DevOps, and more.

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README.mdplugins/developer-kit-ai/docs/

Developer Kit AI Plugin — Documentation

Overview

The AI plugin provides Claude Code with specialized capabilities for building AI-powered applications. It covers the full lifecycle of LLM integration:

  • Prompt Engineering — designing, optimizing, and scaling prompts
  • RAG Systems — retrieval-augmented generation pipelines
  • Chunking Strategies — document preprocessing for vector databases

Plugin Structure

developer-kit-ai/
├── agents/
│   └── prompt-engineering-expert.md     # Expert agent for prompt design
├── commands/
│   └── devkit.prompt-optimize.md        # Prompt optimization command
├── skills/
│   ├── prompt-engineering/              # Prompt design & optimization skill
│   │   ├── SKILL.md
│   │   └── references/
│   │       ├── cot-patterns.md
│   │       ├── few-shot-patterns.md
│   │       ├── optimization-frameworks.md
│   │       ├── system-prompt-design.md
│   │       └── template-systems.md
│   ├── rag/                             # RAG implementation skill
│   │   ├── SKILL.md
│   │   ├── assets/
│   │   │   ├── retriever-pipeline.java
│   │   │   └── vector-store-config.yaml
│   │   └── references/
│   │       ├── document-chunking.md
│   │       ├── embedding-models.md
│   │       ├── langchain4j-rag-guide.md
│   │       ├── retrieval-strategies.md
│   │       └── vector-databases.md
│   └── chunking-strategy/               # Document chunking skill
│       ├── SKILL.md
│       └── references/
│           ├── advanced-strategies.md
│           ├── evaluation.md
│           ├── implementation.md
│           ├── research.md
│           ├── semantic-methods.md
│           ├── strategies.md
│           ├── tools.md
│           └── visualization-tools.md
└── docs/
    ├── README.md                        # This file
    ├── guide-agents.md                  # Agent guide
    └── guide-commands.md                 # Command guide

Available Components

TypeCountDetails
Skills3prompt-engineering, rag, chunking-strategy
Agents1prompt-engineering-expert
Commands1devkit.prompt-optimize

Quick Start

  1. Optimize a prompt — use /developer-kit-ai:devkit.prompt-optimize with your prompt text
  2. Design a prompt — invoke the prompt-engineering-expert agent for new prompt creation
  3. Build a RAG pipeline — use the rag skill with the LangChain4j reference guide
  4. Choose chunking strategy — use the chunking-strategy skill to select the right approach for your data

Key Features

Prompt Engineering

  • Few-shot learning with strategic example selection
  • Chain-of-thought and tree-of-thought reasoning
  • System prompt architecture and role definition
  • Prompt template systems with modular composition
  • A/B testing and progressive optimization frameworks

Retrieval-Augmented Generation

  • Vector database selection and configuration (Pinecone, Weaviate, Milvus, Chroma, Qdrant, FAISS)
  • Embedding model selection (OpenAI, Sentence Transformers, Hugging Face)
  • Retrieval strategies: dense, sparse, hybrid, reranking
  • LangChain4j integration with complete Java examples

Chunking Strategies

  • 5-level strategy hierarchy: fixed-size (L1) through advanced semantic methods (L5)
  • Semantic coherence validation
  • Retrieval precision/recall evaluation metrics
  • Integration with LangChain, LlamaIndex, and Unstructured

See Also

plugins

developer-kit-ai

docs

guide-agents.md

guide-commands.md

README.md

README.md

CHANGELOG.md

context7.json

CONTRIBUTING.md

README_CN.md

README_ES.md

README_IT.md

README.md

tessl.json

tile.json