Autonomous Lab — The Work Unit, Reimagined
Description: A multi-agent system where AI fills the Senior and Junior roles, while the human acts as Overseer. Works across research, software, consulting, legal, medical, and creative domains. MCP-powered.

Professor
collaborate
Trainee
submit
🧑‍🔬
You (Editor)

Senior. Junior. You. The work unit, reimagined.

# Autonomous Lab

You did the research. Now you judge it.

AI takes the PI and Trainee roles — designing experiments, running analysis, writing the paper. You become the Editor: the sole decision maker who curates, judges, and steers the science.

🔌 MCP Server Integrates seamlessly into your existing AI agent workflow. Reuse your skills. Configure characters. Run 24-hour sessions.
Cursor Claude Code Codex CLI Windsurf Any MCP Client
See How It Works Browse Characters
📝 LaTeX Papers 🧬 Skill Containers ⏱️ 24-Hour Sessions ⚙️ Fully Configurable

## 📜 How It Works

Watch a research session unfold, step by step

⚗️ AUTONOMOUS LAB
Iteration 1 | Next PI | Tutorial
⏱ 00:00
🔬 Professor
Idle
🧪 Trainee
Idle
Consultants
Dr. Tanaka
Statistician
📜 Meeting Log

Click Start Tutorial to begin

Watch how PI and Trainee collaborate on a research project

Step 0 / 9
📦 Inventory
📁data0
📁scripts0
📁figures0
📁results0
📁paper0
📄 Paper
0%
abstract--
introduction--
methods--
results--
discussion--
📨

### Editor's Desk

Round 1

#### 📜 Cover Letter

Dear Editor, we present a novel analysis of single-cell chromatin accessibility in tumor microenvironments. Our work identifies cell-type-specific regulatory programs driving treatment resistance using a robust pseudo-bulk DE approach validated by survival analysis (C-index = 0.78)...

#### 📄 Manuscript Summary

Sections:5 (Abstract, Intro, Methods, Results, Discussion)
Figures:4
References:42
Word count:~4,650

#### Select Reviewers (2–3 recommended)

Dr. Kim Comp. Biologist
✅
Dr. Patel Immunologist
✅
Dr. Tanaka Statistician
Dr. Rivera Oncologist
Dr. Liu Geneticist
Dr. Okafor Epidemiologist

#### Selected: 2

#### 📋 Reviewer Reports

🔍 Reviewer #1 — Dr. Kim (Comp. Biologist) Minor Revision

Solid statistical framework. Add external validation cohort and improve Figure 3 resolution.

🔍 Reviewer #2 — Dr. Patel (Immunologist) Minor Revision

Cell-type annotations are convincing. Expand discussion of immune checkpoint markers in resistant populations.

#### ✍️ Your Decision

📝

#### Decision sent: Minor Revision

Feedback sent to the PI. The revision loop continues until you Accept.

✔ Tutorial complete! The PI–Trainee loop resumes with revisions.

## 🧬 Characters as Skill Containers

Each character is a persona backed by a curated set of scientific skills

Dr. Maria Chen
Computational Biology PI
scanpy pytorch-lightning scvi-tools scientific-writing scientific-visualization statistical-analysis
➡
📄 pi.yaml

    title: Computational Biology PI
    expertise: single-cell genomics and ML
    goal: discover cell-type-specific
      gene regulatory programs
    skills:
      - scanpy
      - pytorch-lightning
      - scvi-tools
      - scientific-writing
      - scientific-visualization
      - statistical-analysis
    personality:
      - "Visionary: identifies novel
        biological questions"
      - "Rigorous: demands reproducible
        pipelines"

### How It Works

1
Define a Persona

Give your character a title, expertise, and personality traits. These shape how the AI agent approaches problems and communicates.

2
Attach Cursor Skills

Each skill name maps to a `SKILL.md` file—the same agent skills used by Cursor and Claude. The AI reads these instructions when acting as this character. Skills include scanpy, scientific-writing, pytorch-lightning, statistical-analysis, and 200+ more.

3
Deploy to Your Lab

Drop the YAML file into your project's `.autolab/profiles/` folder. The character is now part of your research team, with all attached skills active.

## 🏪 Character Marketplace

Browse community-created characters or share your own

🔗 List Your Character

Share your character with the community by creating a GitHub repo from our template. Characters are ranked by GitHub stars.

1
Fork the template repo

Start from our character template which includes the YAML schema, a README, and example files.

Fork Template Repo
2
Create your character YAML

Edit the `character.yaml` in your forked repo. Define the persona, skills, and personality.

    name: Dr. Your Name
    role: pi  # pi | trainee | collaborator
    avatar: neuroscientist
    title: Your Title Here
    expertise: your domain expertise
    goal: what this character aims to achieve
    skills:  # Cursor skill names
      - scanpy
      - scientific-writing
      - statistical-analysis
    personality:
      - "Trait: description"

3
Submit your repo URL

Open an issue on our main repo with your GitHub repo link. We'll index it automatically.

Submit Your Repo
⭐
Get ranked by stars

Once indexed, your character appears in the marketplace. The more GitHub stars your repo gets, the higher it ranks. Official characters from MarcusOfficial get a verified badge.

🔬 Character

## 🚀 Get Started

Set up your own Autonomous Lab in minutes

1

### Install the MCP Server

Add Autonomous Lab to your Cursor MCP settings (`~/.cursor/mcp.json`):

    {
      "mcpServers": {
        "autonomous-lab": {
          "command": "uv",
          "args": [
            "--directory",
            "/path/to/autonomous-lab",
            "run", "autonomous-lab"
          ],
          "timeout": 86400,
          "env": {
            "MCP_WEB_PORT": "8766"
          }
        }
      }
    }

2

### Add Character Skills

Install skills in `~/.cursor/skills/`. Each skill is a `SKILL.md` that the AI reads when a character uses it:

    # Skills directory structure:
    ~/.cursor/skills/
      scientific-skills/
        scanpy/SKILL.md
        scvi-tools/SKILL.md
        pytorch-lightning/SKILL.md
        scientific-writing/SKILL.md
        statistical-analysis/SKILL.md
        ...

    # 200+ skills available
    # Characters reference these
    # by name in their YAML

3

### Customize Your Team

Download characters from the Marketplace or create your own in `.autolab/profiles/`:

    title: Your Custom PI
    expertise: your domain
    goal: what to achieve
    skills:  # Cursor skill names
      - scanpy
      - scientific-writing
      - statistical-analysis
    personality:
      - "Trait: description"

4

### Run the Loop

The PI-Trainee loop runs, then you act as Editor:

    # The research cycle:
    autolab_next  # PI sets agenda
    autolab_next  # Trainee executes
    autolab_next  # PI reviews
      ...          # iterate

    # When paper is ready:
    autolab_editorial # You decide!
    # Invite reviewers, then:
    # Accept / Minor / Major / Reject