A Python framework for building applications that use language models with your own documents and data. It provides components for loading content, finding relevant passages, generating answers, and coordinating agents and workflows.

Use it to build retrieval-augmented generation (RAG), document question answering, knowledge graphs, tool-using agents, and event-driven LLM workflows.

It gives you a structured way to connect language models to private or changing information instead of putting all knowledge into a prompt.

llamaindex — Skill for Claude Code and Codex by magnus919

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add magnus919/agent-skills --skill llamaindex

Clone the repo

git clone --depth 1 https://github.com/magnus919/agent-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin llamaindex — /plugin install llamaindex after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/magnus919/agent-skills/llamaindex/github.svg)](https://agentmods.dev/skills/magnus919/agent-skills/llamaindex)

Your own site

<a href="https://agentmods.dev/skills/magnus919/agent-skills/llamaindex"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/llamaindex/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/magnus919/agent-skills/llamaindex"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/llamaindex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,651 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00048	$0.02651
Opus 5	$0.00024	$0.01326
Sonnet 5	$0.00010	$0.00530
Haiku 4.5	$0.00005	$0.00265

Measured 8d ago against content hash 5872d4be8289, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

llamaindex scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/check-setup.py, templates/agentic-rag.py, templates/basic-rag.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

llamaindex/SKILL.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

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.
Rerank before you generate. Hybrid retrieval + reranker is the minimum viable production RAG configuration.
Agents are Workflows. FunctionAgent and AgentWorkflow are pre-configured Workflows. Drop to raw Workflow when you need custom control flow.
Graphs are not just vector stores. PropertyGraphIndex adds structural path traversal that vector similarity cannot provide — combine both for maximum retrieval quality.
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...	Start at phase	What to do
Nothing — blank project	Ingest	Set up data loading, then proceed through the full pipeline
Documents in a directory	Chunk	Choose a chunking strategy, build your index
A working vector index	Retrieve	Add hybrid search, reranking, metadata filters
An existing RAG pipeline to harden	Deploy	Add observability, llama-deploy, production debugging
A need to measure and improve quality	Evaluate	Set up evaluators, ParamTuner, span-attached scoring
Nothing — comparing frameworks	See Framework Routing Guide	Don't start the pipeline — pick the right tool first

Read the full file on GitHub · 165 lines

Files

What ships with it

18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

8d ago First seen · 165 lines · 48 tokens per session scan A 5872d4be8289

Subscribe to this mod's changes

llamaindex is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 2,651 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Building agents Data and AI LLM RAG and vectors agent-skills agentskills ai-agents automation

neurolink-guide

Guide for using the NeuroLink SDK and CLI. Invoke when users ask how to use neurolink, integrate AI providers, add MCP tools, configure RAG, set up memory, deploy servers, or work with multimodal content. Covers SDK, CLI, providers, tools, and enterprise features.

juspay/neurolink · 65 tokens

llamaindex