An expert-level skill for building LLM applications over your data with LlamaIndex. RAG pipelines, multi-agent orchestration, event-driven workflows, knowledge graph construction, and production deployment.

Why Install This Skill

When your agent loads this skill, it becomes a LlamaIndex expert who can:

Build production RAG pipelines — from data ingestion to deployed query engines
Create agent workflows — AgentWorkflow for tool-using agents
Construct knowledge graphs — PropertyGraphIndex for structural path traversal
Optimize retrieval — hybrid search, reranking, metadata filters, sentence window parsing
Add observability — OpenTelemetry-native tracing with Phoenix
Evaluate systematically — span-attached evaluation with ParamTuner

What You Get

Directory	Purpose
`SKILL.md`	9-phase pipeline guide, pipeline modes, quick reference
`references/`	Ingest, chunk, index, retrieve, agent, workflow, deploy, evaluate — one per phase plus framework comparisons

Framework Comparison

LlamaIndex evolved from a RAG indexing library into a full workflow framework. It differs from LangChain (broader integration ecosystem) and Haystack (declarative DAG pipelines) — LlamaIndex's unique strength is its data-aware indexing and knowledge graph construction.

Requirements

Python 3.8+ with llama_index package.

Quick Start

Start with the setup and first workflow in SKILL.md, then use the linked resources for the specific task you need to complete.

Triggers

Use this skill for the task types and keywords described in its SKILL.md description.