Skillget

labmate

agent
Security Audit
Pass
Health Pass
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 17 GitHub stars
Code Pass
  • Code scan — Scanned 10 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
LabMate is a research assistant agent designed for the Claude Code CLI. It provides structured workflows for reading academic papers, running and monitoring machine learning experiments, and visualizing results while maintaining persistent memory across sessions.

Security Assessment
Overall risk: Low. The automated code scan of 10 files found no dangerous patterns, and the tool does not request any overly broad or dangerous permissions. The primary risk stems from its core functionality: as an agent that executes shell commands, it has the inherent ability to run scripts on your machine (such as experiment scaffolding and monitoring). It likely makes network requests to fetch external content like arXiv papers or links from social media platforms via recommended companion plugins. No hardcoded secrets were detected.

Quality Assessment
The project is actively maintained, with its most recent push occurring today. It uses the permissive MIT license and includes clear documentation. It has a small but growing community footprint with 17 GitHub stars, indicating early-stage adoption. The repository description and README are well-written, offering straightforward installation instructions and detailing exactly what the tool can do.

Verdict
Safe to use.
SUMMARY

Research Harness for Claude Code. Keep your agent grounded in context, not lost in vibe coding.

README.md

LabMate

version
license

Your AI labmate for the full research lifecycle — from reading papers to running experiments to writing them up.

中文

The problem

You start a research project with Claude. Three hours later you're debugging a CUDA kernel and have completely forgotten what hypothesis you were testing.

Your agent is no better — doesn't know what you tried last week, can't read your reference papers, and treats every session like day one.

LabMate fixes both sides. It gives your agent persistent experiment memory and domain knowledge. It gives you a research flow that keeps hypotheses, baselines, and findings visible — even when you're deep in implementation.

Install

# From the Anthropic plugin marketplace
/plugin install labmate

Then run /init-project in your existing research project. LabMate auto-detects your project and sets up the skeleton. Done.

Tutorial

New to Claude Code for research? Start here: CC Research Playbook — covers context engineering, skills, hooks, sub-agents, and how LabMate ties it all together.

Recommended companions

LabMate works on its own, but these plugins make it better:

# Development workflow (TDD, planning, code review, brainstorming)
/plugin install superpowers

# Better slides quality (visual spec for slide generation)
/plugin install frontend-slides

# Fetch Twitter/X, XiaoHongShu, Bilibili content for paper discovery
/plugin install agent-reach

superpowers is strongly recommended — it powers the structured development workflow that keeps research projects from going off the rails.

What can it do?

Reading papers

Drop a link or PDF. LabMate breaks down the methodology, flags the assumptions, and connects it to your own work.

/read-paper https://arxiv.org/abs/2401.04088

After the deep-dive, ask follow-up questions. Say "save" when done — it archives to your literature base automatically.

Want a broader picture? Survey a whole topic:

/survey-literature attention sink mechanisms in Diffusion Transformers

Running experiments

Describe what you want to test. LabMate scaffolds the experiment directory, config, run script, and analysis script.

/new-experiment

After you start the run, check status anytime:

/monitor

LabMate diagnoses failures, retries crashed jobs, and tells you when it's done.

Analyzing results

One command to get domain interpretation, literature comparison, and presentation slides:

/analyze-experiment

Then see the results as an interactive dashboard:

/visualize

Staying organized

LabMate remembers across sessions. Your experiment history, paper notes, and key findings persist. Every new session starts with context — your agent knows what stage you're at and what to do next.

Commit your work with automatic CHANGELOG updates:

/commit-changelog

You don't need to memorize commands

LabMate tells you what to do next. After creating an experiment, it suggests /monitor. After analysis finishes, it suggests /visualize. On Fridays it reminds you to write your weekly summary. Just follow the prompts.

The full research lifecycle

/init-project → /new-experiment → /monitor → /analyze-experiment → /visualize → /commit-changelog → repeat
    read papers anytime: /read-paper, /survey-literature

Pipeline state lives in .pipeline-state.json. Your agent picks up where you left off.

How it compares

Capability labmate K-Dense Orchestra ARIS
Deep paper reading Yes No No No
Literature survey Yes No No No
Experiment design Yes No Partial No
Research memory Yes No No No
Experiment monitoring Yes No No Yes
Results dashboard Yes No No No
Cross-discipline Yes Bio/Chem ML/AI only ML only

Customization

Override anything by creating a local copy in your project:

mkdir -p .claude/agents
# Your local .claude/agents/domain-expert.md overrides the plugin version

Under the hood

5 specialized agents, 9 skills, 8 hooks working together. See CLAUDE.md for the technical architecture.

Acknowledgments

Citing

@software{labmate2026,
  title   = {LabMate: Research Harness for Claude Code},
  author  = {freemty},
  year    = {2026},
  version = {0.5.0},
  url     = {https://github.com/freemty/labmate}
}

License

MIT

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