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.
npx skills add ashish7802/awesome-api-skills --skill llamaindexgit clone --depth 1 https://github.com/ashish7802/awesome-api-skillsWrote 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.
[](https://agentmods.dev/skills/ashish7802/awesome-api-skills/llamaindex)<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/llamaindex"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-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.
<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/llamaindex"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/llamaindex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.00683 |
| Opus 5.5 | $0.00000 | $0.00273 |
| Sonnet 5.5 | $0.00000 | $0.00137 |
| Haiku 4.5 | $0.00000 | $0.00068 |
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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LlamaIndex Skill
Data framework for connecting custom data sources to large language models.
Ecosystem Graph Preview
graph LR
llamaindex["llamaindex"]:::core
classDef core fill:#f9f,stroke:#333,stroke-width:4px;
langchain -- "alternative to" --> llamaindex
llamaindex -- "alternative to" --> langchain
llamaindex -- "integrates with" --> openai
llamaindex -- "integrates with" --> pinecone
Recommended Next Skills
- langchain (Score: 0.93) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Can deploy to any, Similar network profile
- pinecone (Score: 0.73) Why: Direct relationship, Both are AI, Similar network profile
- openai (Score: 0.72) Why: Direct relationship, Both are AI, Similar network profile
Quick Start
While LangChain focuses on Agents and Chains, LlamaIndex focuses heavily on Data. It is the premier framework for building advanced Retrieval-Augmented Generation (RAG) applications over unstructured data.
pip install llama-index
Production Patterns
Advanced Retrieval Strategies
Do not rely on naive Top-K semantic search. Production RAG requires advanced strategies like Sentence Window Retrieval (fetching the surrounding context of a hit), Auto-Merging Retrieval, or Re-ranking (using Cohere) to improve hallucination resistance.
Architecture & Scaling
Document Ingestion Pipeline
LlamaIndex handles the entire ingestion pipeline: Data Connectors (PDFs, Notion, SQL) -> Data Indexes (VectorStore, TreeIndex) -> Query Engines.
Error Recovery
If the LLM complains about missing context, it means your Chunk Size is too small or your retrieval strategy is pulling irrelevant nodes. Inspect the source_nodes array attached to the LlamaIndex response to debug exactly what text was fed to the LLM.
Security Notes
When ingesting documents, respect ACLs (Access Control Lists). Ensure that when User A queries the index, the retriever is strictly filtered to only pull vector embeddings derived from documents User A has permission to read.
What ships with it
2 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.
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.
- 1mo ago First seen · 70 lines · 0 tokens per session scan A 33e689beb79c
llamaindex is a skill published in the GitHub repository ashish7802/awesome-api-skills (13 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 683 tokens. 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 2026-08-30.
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