Individual skill
agents-llamaindex
Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.
skills/agents-llamaindex/SKILL.md
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# Data-Centric LLM Applications (Indexing and Retrieval)
When the hard part is data, not dialogue, you need an indexing mindset: ingest documents from many
sources, structure them into queryable indices, and retrieve precisely what each query needs. This
skill covers that data layer.
## Overview
The pipeline is ingest → index → retrieve → synthesize. Ingestion handles the messy reality
of documents: PDFs, web pages, databases, APIs, each parsed into clean text with metadata. Indexing
organizes that text — vector indices for semantic search, keyword indices for exact match,
structured indices for tables, summaries for long documents. Query engines combine retrieval
strategies and synthesize answers, citing sources. Done well, the model answers from your data
instead of its memory.