A comprehensive Claude Code skill for building production-grade agentic RAG systems with LlamaIndex in Python.

This skill provides deep expertise in LlamaIndex's agentic capabilities, covering six core pillars:

This skill activates when you ask Claude Code to:

• "Build a LlamaIndex agent"
• "Set up semantic chunking"
• "Index source code with CodeSplitter"
• "Implement hybrid search"
• "Create a knowledge graph with LlamaIndex"
• "Implement query routing"
• "Debug RAG pipeline"
• "Add Phoenix observability"
• "Create an event-driven workflow"

Or when discussing: PropertyGraphIndex, SemanticSplitterNodeParser, CodeSplitter, BM25Retriever, hybrid search, ReAct agent, Workflow pattern, LLMRerank, Text-to-Cypher

The recommended way to install this skill across different AI coding agents is using the skilz universal installer. This skill supports the Agent Skill Standard, which means it works with 14+ coding agents including Claude Code, OpenAI Codex, Cursor, and Gemini CLI.

pip install skilz

# Install to user home (available in all projects)
skilz install -g https://github.com/SpillwaveSolutions/developing-llamaindex-systems

# Install to current project only
skilz install -g https://github.com/SpillwaveSolutions/developing-llamaindex-systems --project

# Claude Code (user home)
skilz install SpillwaveSolutions_developing-llamaindex-systems/developing-llamaindex-systems

# Claude Code (project level)
skilz install SpillwaveSolutions_developing-llamaindex-systems/developing-llamaindex-systems --project

Add --project to any command above for project-level installation.

Skilz supports 14+ coding agents including Windsurf, Qwen Code, Aidr, and more. For the full list of supported platforms, visit SkillzWave.ai/platforms or see the skilz-cli GitHub repository.

View this skill on the marketplace: SkillzWave Listing

# 1. Semantic chunking
from llama_index.core.node_parser import SemanticSplitterNodeParser
from llama_index.embeddings.openai import OpenAIEmbedding

splitter = SemanticSplitterNodeParser(
    buffer_size=1,
    breakpoint_percentile_threshold=95,
    embed_model=OpenAIEmbedding()
)
nodes = splitter.get_nodes_from_documents(docs)

# 2. Build index
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex(nodes)

# 3. Query
response = index.as_query_engine().query("What is X?")

developing-llamaindex-systems/
├── SKILL.md                        # Main skill definition
├── README.md                       # This file
├── CONTINUATION-GUIDE.md           # Session continuation guide
├── references/
│   ├── ingestion.md               # Chunking strategies, IngestionPipeline
│   ├── retrieval-strategies.md    # BM25, hybrid search, fusion
│   ├── property-graphs.md         # PropertyGraphIndex, extractors, retrievers
│   ├── context-rag.md             # Query routing, decomposition, reranking
│   ├── orchestration.md           # ReAct agents, Workflows, multi-agent
│   └── observability.md           # Phoenix, debugging, evaluation
└── scripts/
    ├── requirements.txt           # Pinned dependencies
    ├── ingest_semantic.py         # Production ingestion script
    └── agent_workflow.py          # Event-driven workflow template

• Python 3.9+
• LlamaIndex 0.10+
• OpenAI API key (or configure local models)

Install dependencies:

pip install -r scripts/requirements.txt

Embedding-based chunking that preserves logical coherence, ideal for legal documents, technical manuals, and research papers.

Language-aware splitting for source code with configurable chunk sizes and overlap.

Combine BM25 keyword search with vector similarity using configurable alpha weighting.

Hybrid retrieval combining vector search with graph traversal. Supports Neo4j and in-memory graph stores.

LLM-based routing to direct queries to specialized engines based on intent.

Type-safe, async workflows with branching, cycles, and human-in-the-loop support.

One-line Arize Phoenix integration for full tracing, plus custom handlers for metrics and alerting.

• LangChain projects - Different framework
• Non-Python environments - Python 3.9+ only
• Simple Q&A bots - Overkill if you don't need graphs, routing, or workflows
• Offline/local-only setups - Scripts default to OpenAI APIs; modification required for local models

• SkillzWave Marketplace - Largest Agentic Marketplace for Agent Skills
• SpillWave - Leaders in AI Agent Development
• Agent Skill Standard - Cross-platform skill specification

MIT