AutoRAG Agent is a librarian-style system that searches files on a computer, checks the original documents, and organizes findings into numbered knowledge units using multiple retrieval methods. It helps an agent answer questions from local documents without first copying them into a centralized vector database, while the repository also maintains the earlier AutoRAG tool for optimizing retrieval-augmented-generation pipelines. The catalogue entries provide skills and instructions for working with AutoRAG Agent.
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 Marker-Inc-Korea/AutoRAG --skill autorag-setupgit clone --depth 1 https://github.com/Marker-Inc-Korea/AutoRAGWrote 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/marker-inc-korea/autorag/autorag-setup)<a href="https://agentmods.dev/skills/marker-inc-korea/autorag/autorag-setup"><img src="https://agentmods.dev/badge/skills/marker-inc-korea/autorag/autorag-setup/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/marker-inc-korea/autorag/autorag-setup"><img src="https://agentmods.dev/badge/skills/marker-inc-korea/autorag/autorag-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 148 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00075 | $0.07560 |
| Opus 5.5 | $0.00030 | $0.03024 |
| Sonnet 5.5 | $0.00015 | $0.01512 |
| Haiku 4.5 | $0.00007 | $0.00756 |
Grade A, and why
autorag-setup 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 today.
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 — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoRAG setup
Use this skill when AutoRAG is unconfigured, the autorag CLI is missing, model
resolution fails, indexes are missing or stale, or the user wants to change the
document collection or datasources.
For model-free external-agent search, use autorag-lite-setup to configure
and register autorag-mcp; do not configure a search model or install a search
skill for Lite. This skill's model setup and CLI search apply to the full
librarian, not the Lite MCP server.
Safety
- Inspect only non-secret provider/model metadata and credential availability.
- Never print, copy, migrate, compare, or persist credential values. Store only
environment-variable names such as
apiKeyEnv. - Do not scan the whole filesystem or home directory without explicit approval.
- Setup, indexing, and refresh never move, rename, edit, or delete source documents. The librarian itself may organize files when the user asks it to.
- Do not index system trees, app bundles, caches, credential stores,
node_modules,.git,dist,build,target,.cache,.autorag, or.jikji.
Install the CLI if needed
The CLI is @autorag/librarian (autorag). Runtime is Node.js ≥ 24 or Bun.
command -v autorag >/dev/null || bun install -g @autorag/librarian
autorag --help
If Bun is unavailable, npm install -g @autorag/librarian is acceptable.
Inspect existing configuration
Check --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or
~/.autorag/config.json. Relevant fields are:
searchPaths,workspacePath, andmemoryPathmodel.provider,model.id,model.api,model.baseUrl,model.apiKeyEnvbm25,minSync, andjikjilimits(retrieval, baseline-prefetch, and model-facing candidate caps)datasourcesandui
Preserve explicit user choices and a working config unless the user asks to replace them or health checks fail.
Configure one search model
AutoRAG is the specialized librarian agent. One configured model plans the search, calls retrieval and filesystem tools, reads sources, judges evidence, and curates the answer in one loop. There are no orchestrator/explorer roles.
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.
- today Changed · +24 lines a34af0d4bae0
- yesterday Changed · +2 lines 799b1eb15acc
- 2d ago Changed · +2 lines 72ae885db349
- 3d ago Changed · +2 lines 2c3391143f90
- 5d ago Changed · +57 lines 5ca8a002e245
- 6d ago Changed · +16 lines 870e62842109
- 8d ago Changed e43436fab4d9
- 10d ago Changed · +9 lines 6e407e49f055
- 11d ago Changed · +16 lines 5d3d92825e03
- 13d ago First seen · 457 lines · 75 tokens per session scan A e2ef55165886
autorag-setup is a skill published in the GitHub repository Marker-Inc-Korea/AutoRAG (5,199 stars, last pushed yesterday), licensed Apache-2.0. It adds 75 tokens to every session and 7,560 once invoked, about $0.0003 per session on Opus 5.5. 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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