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Framework for building RAG and document-centric LLM applications: ingestion connectors, vector indices, query engines and agent integrations for document Q&A an
LlamaIndex provides a comprehensive toolkit for connecting LLMs to your data: document ingestion (300+ connectors), vector indices, retrievers, query engines and patterns for RAG-based apps. It includes code examples for building indexes, query engines, agents and multi-modal retrieval workflows.
Use this skill when building document Q&A systems, knowledge-base chatbots, retrieval-augmented agents, or pipelines that need structured document ingestion, metadata filtering and persistence to vector stores. Prefer LlamaIndex for data-centric LLM tasks; consider LangChain for more agent-centric workflows.
Works well with developer-facing assistant agents that can run code snippets and help build RAG pipelines (Copilot-like assistants, Claude/Codex-style code agents).
Documentation-only skill providing a LlamaIndex reference guide with code examples, comparison tables, and best practices. No bundled scripts or automation — purely a knowledge resource. Well-written content with clear sections, but limited as an actionable agent skill since it can't perform any work without external setup (Python env, LlamaIndex library, data files, API keys).
This is essentially a curated LlamaIndex cheat sheet masquerading as an agent skill. Useful as reference material but doesn't automate anything. The database connection string example uses placeholder credentials which is fine. No security concerns at all — just a large markdown file. Architecture is monolithic (single SKILL.md ~300 lines) with unbundled references.
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