Agent skill

AutoRAG Librarian

by Marker-Inc-Korea in Marker-Inc-Korea/AutoRAG

Searches, summarizes, compares and answers questions from an already configured AutoRAG librarian agent over local documents and authorized datasources.

MITAuto-check passedKnowledge Management

Install AutoRAG Librarian

skills CLI
$ npx skills add Marker-Inc-Korea/AutoRAG --skill autorag -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Marker-Inc-Korea/AutoRAG autorag --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Marker-Inc-Korea/AutoRAG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/autorag .claude/skills/autorag && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
autorag
GitHub stars
5.1k
Token cost
~2.1k tokens
SKILL.md length
977 words
Files
1
Skills in repo
4
Repo updated
First seen
Licence
MIT

At a glance

Searches, summarizes, compares and answers questions from an already configured AutoRAG librarian agent over local documents and authorized datasources.

  • Searching local PDFs, wikis or notes through an already-configured AutoRAG agent
  • SKILL.md covers Preflight, Search, Maintenance and Rules
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Finding and reviewing duplicate files in the AutoRAG-indexed corpus

What it does

The skill assumes AutoRAG is already configured and routes search, summarize, compare and question-answering requests over local PDFs, wikis, notes, papers or other authorized datasources to it. One configured model plans the search itself, calling MinSync, Jikji, datasource and filesystem tools, reading sources, judging evidence and curating the final answer, with no separate subagent or model role; AutoRAG only reads source documents and writes indexes under its own workspace directories, never moving, renaming, editing or deleting source files.

Before anything else, it confirms a config file exists at one of several standard locations and inspects only non-secret search paths and model metadata, then runs duplicates, status and health commands as JSON. A duplicate-file review command finds exact or near/contains duplicate files read-only, useful for reducing index space, with exact matches found by a canonical text hash and near matches needing human review; an excludePaths setting in the config keeps specific files or folders out of the index entirely, reversible by removing the entry and refreshing.

The status command is model-free, while health resolves the configured model, checks that credentials are present, and normally probes one live completion, falling back to a separate autorag-setup skill rather than guessing at private provider details when something is unhealthy.

When your agent uses it

  • Searching local PDFs, wikis or notes through an already-configured AutoRAG agent
  • Finding and reviewing duplicate files in the AutoRAG-indexed corpus
  • Checking whether AutoRAG's configured model and index are healthy
  • Excluding specific files or folders from the AutoRAG index

Example prompts

  • “Search my indexed notes for anything about the Q3 budget decision.”
  • “Find duplicate files in my AutoRAG corpus and suggest which copies to keep.”
  • “Check AutoRAG's health and tell me if the model connection is working.”

Requirements

  • An already configured AutoRAG instance with a config.json

What it can do on your machine

Read from SKILL.md and the folder at commit 29ccab9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AutoRAG Librarian loads about 2.1k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 977 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Marker-Inc-Korea/AutoRAG at commit 29ccab9, republished under its MIT licence (© Marker-Inc-Korea). 977 words, ~2,055 tokens.

Download SKILL.mdSave it as .claude/skills/autorag/SKILL.md (or your agent's skills folder).
name
autorag
description
Search, summarize, compare, and answer questions from an already configured AutoRAG librarian over local documents and configured datasources. Use when the user asks AutoRAG to search PDFs, wikis, notes, or a knowledge base. Use autorag-setup for install, model, roots, indexing, or datasource changes.
license
MIT

AutoRAG Librarian Skill

Use this skill when AutoRAG is already configured and the user asks to search, summarize, compare, or answer questions from local PDFs, wikis, notes, research papers, knowledge bases, or configured datasources.

AutoRAG is the specialized librarian agent. One configured model plans the search, calls MinSync, Jikji, datasource, and filesystem tools, reads sources, judges evidence, and curates the final answer. There is no subagent or separate model role.

AutoRAG reads source documents and writes indexes only under the configured workspace .autorag/ directory and Jikji's per-source .jikji/ caches. Never move, rename, edit, or delete source files.

Preflight

Confirm a config exists at --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or ~/.autorag/config.json. Inspect only non-secret searchPaths and model metadata, then run:

bash
autorag duplicates --json
autorag status --json
autorag health --json
Duplicate-file review

Use autorag duplicates when the user asks to find duplicate files, choose likely latest copies, or reduce corpus/index space. The command and the scan_duplicate_documents Agent tool are read-only and never delete or move source files. Exact means dupey's canonical extracted-text hash matches; near/contains families require human review. Exact duplicate exclusion during refresh is enabled by default and can be disabled with "excludeExactDuplicates": false in config.json.

Excluding local files from the index

"excludePaths" in config.json lists files or folders (absolute, or relative to workspacePath) that refresh keeps out of the parsed mirror and MinSync. A folder entry excludes everything under it. Removing an entry and running autorag refresh --method minsync indexes the file again. Excluded sources are recorded as user-excluded skips, so they are not reported as stale. Jikji indexes the source folders directly and is not refreshed here, so AutoRAG also drops excluded paths from the jikji_find answer pack and the baseline prefetch at retrieval time; the on-disk .jikji_agent_map.md stays complete, and direct file reads remain available.

status is model-free and path-opaque. health resolves the single model, checks credential presence, and normally probes one live completion. If the model, authentication, configuration, or indexes are unhealthy, use autorag-setup rather than guessing private provider details.

MinSync and Jikji should normally be healthy. Answering a question never builds or installs them: autorag refresh auto-installs both by default and builds their indexes incrementally. If they are missing or stale, run a full autorag refresh or return to setup rather than silently degrading to lexical-only search.

Prefer --json --debug when another agent will consume the result. --json alone omits sessionId. --debug adds session/diagnostics fields and does not print filesystem paths.

bash
autorag search "what were the key findings in the Q3 report" --top-k 5 --json --debug

--json --debug includes answer, numbered results (number, title, summary, optional source), and sessionId. --json without --debug is only answer plus results. Every bracketed [n] citation in answer resolves to a results[].number of the same response; an unmatched citation is removed and reported as a citation-without-result diagnostic.

To inspect the exact persisted evidence behind numbered results, use:

bash
autorag evidence <sessionId> --result 1 --json

The response includes the original source, retrieval method, stable evidence ID, raw excerpt/content, and any available chunkIndex, lineNumber, retrievalResultId, and metadata. Omit --result to inspect every result in the session. Prefer this command whenever the caller wants detailed chunk text rather than only the curated summary.

  • --scope narrows datasource retrieval to a requested sub-path (ordinary per-query filtering).
  • --json is required for programmatic consumption.
  • --debug is required for sessionId and diagnostics in search output.
  • autorag evidence is the detailed source/chunk inspection path.

Do not bypass the librarian with ad hoc raw search when the user requested AutoRAG. The search loop can use Jikji, MinSync lexical/vector/hybrid retrieval, datasource retrieval, and direct source reading as appropriate. If search fails because of model, provider, auth, or timeout problems, diagnose with autorag health --json.

Every search is two-phase: a fast answer, then verification. With Jev on (the default, OpenRouter), Jev first routes the question:

Show full SKILL.md (377 more words)Show less
  • General knowledge or small talk is answered directly.
  • A request to view or change AutoRAG's own settings (model, providers, datasources) is handled by the agent itself: it loads the setup skill, edits the active config, verifies it, and reports what changed. The running agent keeps its startup model; changes apply to the next autorag launch.
  • Private-data questions use local search; public current facts use web search.
  • A multi-part question is split into up to five parallel search queries.
  • On local search, Jev also picks which registered datasources (Slack, Discord, KakaoTalk, email, ...) to search before the fast answer, from each datasource's description and where similar past questions were answered; their chunks are reranked together with local file evidence.

After the fast answer, Jev ends the run if the answer is complete and evidence-backed. So results may come straight from the fast answer, with no verification phase. --debug diagnostics show the decision: query-routed (branch and queries), datasources-selected (datasources searched or skipped, with probabilities), follow-up-skipped (fast answer final), or query-route-fallback (Jev unavailable, single local search).

Maintenance

bash
autorag setup --format json
autorag status --json
autorag health --json
autorag refresh --json
autorag refresh --method minsync,jikji --json
autorag watch --once --json
autorag watch
autorag refresh --force --json
autorag index rebuild --yes --json
autorag index reset --method parsed --yes --json
autorag memory inspect --json
autorag tui
autorag serve --force
autorag p2p policy list --json

Prefer a full refresh so parsed mirrors, MinSync, Jikji, configured datasources, and (on Windows) the bundled Everything file-name index stay aligned. --method accepts parsed,minsync,datasources,jikji,everything,all. BM25 is a MinSync retrieval mode, not a --method name. Use --method only for deliberate narrowing. Scheduled maintenance should use non-daemon autorag watch --once, typically every 1 hour, with the same config used by search and no overlapping runs. autorag tui is the shipped beta terminal UI. autorag serve / autorag p2p are opt-in SimpleX peer sharing (disabled until p2p.enabled is true, unless --force).

Reset and rebuild commands remove only selected workspace .autorag indexes. Never target source documents. memory inspect is read-only and path-opaque.

Rules

  • Use only configured and approved search paths.
  • Never expose provider credentials or authentication payloads.
  • Never invent provider identities or model ids.
  • A Pi-usable subscription is valid; a subscription Pi cannot invoke is not.
  • Preserve real source mapping.
  • Prefer --json --debug when another agent consumes search output.
  • Do not invent CLI commands. autorag --help is the command list of record, including the shipped setup, gateway, models, serve, p2p, tui, and lite commands. autorag <command> --help prints that command's own flags, and autorag --version prints the installed version. See docs/embedding-runtime.md for the local embedding runtime and gateway setup.

© Marker-Inc-Korea, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/autorag of Marker-Inc-Korea/AutoRAG.

Open the folder on GitHubat commit 29ccab9

Compare with similar skills

AutoRAG Librarian next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AutoRAG Librarian compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AutoRAG Librarian this skillMarker-Inc-Korea/AutoRAG5.1k—~2.1kAutomated safety check: PassMIT
Vault Contextual RetrievalAgriciDaniel/claude-obsidian15k—~1.4kAutomated safety check: PassMIT
Knowledgebaseopen-edge-platform/edge-ai-suites140—~900Automated safety check: PassApache-2.0
Clarity Gatesickn33/agentic-awesome-skills47k3 repos~709Automated safety check: PassCC-BY-4.0
Openloomimelandlabs/openloomi1k—~835Automated safety check: PassApache-2.0
Gnogmickel/gno1151 repos~1.6kAutomated safety check: PassMIT

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Questions about AutoRAG Librarian

What does AutoRAG Librarian do?

Searches, summarizes, compares and answers questions from an already configured AutoRAG librarian agent over local documents and authorized datasources. The skill assumes AutoRAG is already configured and routes search, summarize, compare and question-answering requests over local PDFs, wikis, notes, papers or other authorized datasources to it. One configured model plans the search itself, calling MinSync, Jikji, datasource and filesystem tools, reading sources, judging evidence and curating the final answer, with no separate subagent or model role; AutoRAG only reads source documents and writes indexes under its own workspace directories, never moving, renaming, editing or deleting source files.

When should I use AutoRAG Librarian?

AutoRAG Librarian fits situations like: searching local PDFs, wikis or notes through an already-configured AutoRAG agent; finding and reviewing duplicate files in the AutoRAG-indexed corpus; checking whether AutoRAG's configured model and index are healthy; excluding specific files or folders from the AutoRAG index.

How do I install AutoRAG Librarian in Claude Code?

Run `npx skills add Marker-Inc-Korea/AutoRAG --skill autorag -a claude-code`. Or copy the skill folder (skills/autorag in Marker-Inc-Korea/AutoRAG) into .claude/skills/autorag in your project. Claude Code loads it when a task matches its description.

How do I install AutoRAG Librarian in Codex?

Run `npx skills add Marker-Inc-Korea/AutoRAG --skill autorag -a codex`. Or copy the skill folder (skills/autorag in Marker-Inc-Korea/AutoRAG) into .agents/skills/autorag in your project. Codex loads it when a task matches its description.

Can I use AutoRAG Librarian in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Marker-Inc-Korea/AutoRAG --skill autorag -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/autorag, .gemini/skills/autorag, .github/skills/autorag and .opencode/skills/autorag in your project.

What does AutoRAG Librarian need to run?

SKILL.md names no scripts, command-line tools or credentials: AutoRAG Librarian is instructions for the agent only. Our summary lists: An already configured AutoRAG instance with a config.json.

Does AutoRAG Librarian access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AutoRAG Librarian safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AutoRAG Librarian use?

AutoRAG Librarian is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AutoRAG Librarian use?

About 2.1k tokens (SKILL.md is roughly 8.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AutoRAG Librarian?

Skills that share tags, products or a category with AutoRAG Librarian: Vault Contextual Retrieval (AgriciDaniel/claude-obsidian, 15k stars), Knowledgebase (open-edge-platform/edge-ai-suites, 140 stars), Clarity Gate (sickn33/agentic-awesome-skills, 47k stars) and Openloomi (melandlabs/openloomi, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AutoRAG Librarian?

Marker-Inc-Korea (a GitHub organization) maintains it in Marker-Inc-Korea/AutoRAG, which has 5,122 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on October 11, 2026.

Source: Marker-Inc-Korea/AutoRAG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.