AI handles execution, humans own the direction, and every run becomes an inspectable research artifact on disk.
# Add to your Claude Code skills
git clone https://github.com/AutoX-AI-Labs/AutoRLast scanned: 5/3/2026
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}AutoR is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by AutoX-AI-Labs. AI handles execution, humans own the direction, and every run becomes an inspectable research artifact on disk. It has 862 GitHub stars.
Yes. AutoR passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/AutoX-AI-Labs/AutoR" and add it to your Claude Code skills directory (see the Installation section above).
AutoR is primarily written in Python. It is open-source under AutoX-AI-Labs on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh AutoR against similar tools.
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AutoR is not a chat demo, not a generic agent framework, and not a markdown-only research toy.
It is a structured research harness over a coding agent execution layer: AI handles execution, humans own the direction, and every run becomes an inspectable research artifact on disk.
New users should start with the step-by-step guides: English Guide or 中文教程.
Most autoresearch systems optimize for autonomy.
AutoR takes a different position: research is too important to hand over as a blind end-to-end loop. The goal is not to remove humans from research. The goal is to give them a stronger execution system.
| Dimension | AutoR |
|---|---|
| Execution model | A coding agent as the execution layer, AutoR as the research control loop |
| Control model | Human approval by default, with an optional strict reviewer-agent gate for unattended runs |
| Research unit | A reproducible run under runs/<run_id>/ |
| Workflow shape | 9-stage workflow: optional intake plus eight formal research stages |
| Quality bar | Artifact-backed outputs, not markdown-only summaries |
| Recovery | Resume, redo-stage, rollback-stage, stage-local continuation |
| Layer | Highlight | What AutoR actually does |
|---|---|---|
| Big idea | Human-centered research execution | AutoR is not an autonomous scientist. AI handles execution; humans retain approval and direction at every stage boundary. |
| Big idea | Research loop over agent loop | The system manages stage progression, validation, repair, recovery, and human checkpoints above the lower-level agent execution loop. |
| Big idea | Every run is a reproducible research artifact | Each run leaves behind prompts, logs, approved summaries, code, data, figures, writing sources, and packaged outputs under runs/<run_id>/. |
| Big idea | Verifiable outputs, not paper-shaped theater | The workflow is judged by inspectable artifacts and human approval, not by whether a generated document merely looks polished. |
| Useful feature | Structured literature organization | Survey notes, bibliographies, related-work tables, and reading artifacts stay under workspace/literature/ instead of disappearing into chat history. |
| Useful feature | Automated experiment manifests | Machine-readable experiment and result files make runs inspectable, comparable, and reusable downstream. |
| Useful feature | Citation verification and writing checks | Writing expects citation verification, build logs, and self-review artifacts before Stage 07 is considered complete. |
| Useful feature | Artifact indexing across stages | artifact_index.json and related manifests help later stages find data, results, and figures without guessing from filenames. |
| Useful feature | Resume, redo, and rollback controls | Long research runs can continue in place, retry a stage, or roll downstream state back without starting over. |
| Useful feature | Venue-aware packaging | AutoR can package manuscript sources, PDFs, review materials, and release-ready artifacts instead of stopping at markdown summaries. |
In practice, that means AutoR is useful not only because of the high-level framing, but also because it handles real research chores: literature organization, experiment manifests, citation verification, artifact indexing, manuscript packaging, and recoverable long-running workflows.
Many systems aim to generate research outputs that look ready.
AutoR takes a harder path:
So the question is not:
Does it look ready?
It is:
Can you verify every part of it?
Latest mainline updates:
workspace-write, but users who intentionally need remote GPU or SSH execution can now opt into --codex-sandbox danger-full-access; the setting is persisted in run_config.json and preserved on resume.--sandbox workspace-write execution flag instead of the deprecated Codex CLI --full-auto flag.--full-auto approval mode. The execution loop is unchanged, but the manual approval gate can now be replaced by a strict simulated reviewer agent backed by Claude or Codex, with reviewer settings persisted in run_config.json.Decision Ledger section and validates draft outputs against the correct .tmp.md path. Added stage recovery controls that let operators /skip the current stage, /back <stage> to an earlier stage, or choose skip / roll back directly after retry exhaustion.--operator codex support alongside Claude, persisted the selected execution backend in run_config.json, and improved terminal rendering for backend JSON streams.--research-diagram dependencies and tightened the README positioning around human-centered, artifact-backed research execution.AutoR already has a full example run used throughout the repository: runs/20260330_101222.
| What the run produced | What it demonstrates |
|---|---|
| example_paper.pdf | A compiled manuscript artifact within a broader research package |
| Executable research code | The run is not just a writing pipeline |
| Machine-readable datasets and result files | Claims are backed by inspectable experiment outputs |
| Real figures used in the research package | The run produces publication-style visuals, not placeholders |
| Review and dissemination materials | The workflow continues past writing into release readiness |
Highlighted outcomes from that run:
AGSNv2 reached 36.21 ± 1.08 on Actor.AutoR is designed for terminal-first execution, but the interaction layer is not limited to raw logs and plain prompts. The current UI supports banner-style startup, colored stage panels, parsed backend event streams, display-width-aware markdown wrapping, keyboard-selectable