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-doctorgit 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-doctor)<a href="https://agentmods.dev/skills/marker-inc-korea/autorag/autorag-doctor"><img src="https://agentmods.dev/badge/skills/marker-inc-korea/autorag/autorag-doctor/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-doctor"><img src="https://agentmods.dev/badge/skills/marker-inc-korea/autorag/autorag-doctor.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 | $0.00152 | $0.03414 |
| Opus 5.5 | $0.00061 | $0.01366 |
| Sonnet 5.5 | $0.00030 | $0.00683 |
| Haiku 4.5 | $0.00015 | $0.00341 |
Grade A, and why
autorag-doctor 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 3d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoRAG doctor
Goal: every configured datasource is indexed and actually returns
hits. A green status is not enough — the run is done only when a real query
returns a real hit per datasource, or the datasource is reported as genuinely
empty or not configured.
Always finish with the status table in Report.
Safety
- Never delete, move, or edit source documents.
- Never delete a datasource's native store (
~/Library/Application Support/katok,~/.discrawl,~/.mailcrawl,.qmd, Telegram/WhatsApp/Notion archives). AutoRAG only reads them; rebuilding them is the owning CLI's job. - Only AutoRAG-owned state under
AUTORAG_HOMEand the workspace.autoragdirectory may be reset. - Never print token or password values. Report credential names only.
- Kill a process only after confirming it is an AutoRAG-owned orphan.
1. Triage the core
autorag status --json
autorag health --json
autorag gateway status --format json
statusreportsstate,stale,diagnostics, and per-component state forminsync,jikji, anddatasources.stale: trueor anystale-indexdiagnostic means the corpus changed since the last successful refresh.healthresolves the single search model and does one live completion probe.--skip-probesonly proves config shape, never live access; do not claim a healthy model from it.- The gateway is on-demand:
stoppedis normal when nothing is embedding.unavailablewhile a refresh is running is a real failure.
Config lives at --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or
~/.autorag/config.json. Read searchPaths, workspacePath, minSync,
jikji, and datasources before changing anything.
If the CLI itself is missing or the config does not exist, stop and run the
autorag-setup skill first — doctor repairs an existing install, it does not
create one.
2. Probe every datasource natively
Each CLI owns its archive, so ask the CLI, not AutoRAG. A datasource is only
active when its binary exists, its store is present, and its own check passes.
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.
- 3d ago Changed 0ce73d567b02
- 5d ago Changed · +2 lines a1b74673288b
- 6d ago Changed · +2 lines 15b1140ceeb8
- 7d ago Changed · +1 lines d13b24db35fe
- 9d ago Changed · +9 lines f7140176636b
- 13d ago Changed · +2 lines 9a14eb45380d
- 22d ago Changed · +1 lines e2279bb8732e
- 24d ago Changed · +2 tokens per session 17912c8129c0
- 25d ago First seen · 238 lines · 150 tokens per session scan A fc4565a15558
autorag-doctor is a skill published in the GitHub repository Marker-Inc-Korea/AutoRAG (5,126 stars, last pushed yesterday), licensed Apache-2.0. It adds 152 tokens to every session and 3,414 once invoked, about $0.0006 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 2026-09-21.
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browserwing-admin
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vector-db
Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies.
gno
Search local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs…
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…