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Each README is written for a human audience, explaining: - What the skill does (not what format it follows) - What benefit the user gets from installing it - Quick setup and usage patterns - When to load/trigger the skill - What scripts, references, and templates it ships data-scientist already had a README — left unchanged. 48 READMEs added across all skill and bundle directories.
LlamaIndex — RAG & Agent Orchestration Framework
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.