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 G1Joshi/Agent-Skills --skill llamaindexgit clone --depth 1 https://github.com/G1Joshi/Agent-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/g1joshi/agent-skills/llamaindex)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/llamaindex"><img src="https://agentmods.dev/badge/skills/g1joshi/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.
<a href="https://agentmods.dev/skills/g1joshi/agent-skills/llamaindex"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/llamaindex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00020 | $0.00319 |
| Opus 5 | $0.00010 | $0.00160 |
| Sonnet 5 | $0.00004 | $0.00064 |
| Haiku 4.5 | $0.00002 | $0.00032 |
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 10d 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.
What it actually says
LlamaIndex
LlamaIndex (formerly GPT Index) connects LLMs to your data. 2025 introduces Workflows, an event-driven way to build complex RAG pipelines.
When to Use
- RAG (Retrieval Augmented Generation): Indexing PDFs, Docs, SQL to chat with them.
- Structured Data: Querying SQL/Pandas with natural language (
NLSQL). - Agents: Building research agents that browse the web and summarize.
Core Concepts
Workflows
Event-driven architecture for agents. Replace DAGs with event listeners (@step).
Query Engine
High-level API (index.as_query_engine()) to ask questions.
Data Loaders (LlamaHub)
Connectors for Notion, Slack, Discord, PDF, etc.
Best Practices (2025)
Do:
- Use Workflows: They are harder to learn but easier to debug than monolithic engines.
- Use Hybrid Search: BM25 (Keyword) + Vector Search for best retrieval accuracy.
- Use Rerankers: Always rerank retrieved nodes (Cohere/BGE) before sending to LLM.
Don't:
- Don't dump raw text: Use "Node Parsers" to chunk data intelligently (Markdown, Semantic).
References
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.
- 10d ago First seen · 45 lines · 20 tokens per session scan A cfa13d147aac
llamaindex is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 20 tokens to every session and 319 once invoked, about $0.0001 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 2026-08-30.
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