Agent skill · magnus919
llamaindex
Expert skill for building LLM applications with the LlamaIndex framework — RAG pipelines, multi-agent orchestration, event-driven workflows, knowledge graph construction, production deployment, and evaluation. Use when working with LlamaIndex or comparing RAG and agent orchestration frameworks.
What it needs
About 7k tokens when loaded.
What this skill does
LlamaIndex Expert Skill LlamaIndex is an MIT-licensed Python framework for building LLM applications over your data. In 2026, it has evolved from a RAG indexing library into an event-driven workflow framework with integrated production runtime (llama-deploy), agent orchestration (AgentWorkflow), knowledge graph construction (PropertyGraphIndex), and OpenTelemetry-native observability. The framework is organized around seven core primitives: Reader (data loaders), Document/Node (chunked content model), Index (data structures over Nodes), Retriever (relevant Node selection), Query Engine (retriever + synthesis), Agent (LLM with tools), and Workflow (event-driven orchestration). Key Principles These principles govern every decision when building with LlamaIndex. Read them before proceeding to the reference guides. 1. Decouple retrieval chunks from synthesis chunks. The embedding representation that retrieves well differs from the context representation that generates well. Use SentenceWindowNodeParser + MetadataReplacementNodePostProcessor for this pattern. 2. Rerank before you generate. Hybrid retrieval + reranker is the minimum viable production RAG configuration. 3. Agents are Workflows. FunctionAgent and AgentWorkflow are pre-configured Workflows. Drop to raw Workflow when you need custom control flow. 4. Graphs are not just vector stores. PropertyGraphIndex adds structural path traversal that vector similarity cannot provide — combine both for maximum retrieval quality. 5. Evaluate in the same process. Span-attached evaluation preserves the connection between the output and the retrieval context that produced it. Where to Start The pipeline has 9 phases from Ingest to Deploy. If you're joining mid-stream with existing work, find your entry point: You already have... …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/llamaindex