Maintainer Skills Lab

0020020048starsClaim this server

Curated developer tools, AST analyzers, and automated verification workflows.

1# Maintainer Skills Lab

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-26

Stars: 48
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Maintainer Skills Lab?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Maintainer Skills Lab need an API key?

No required environment variable is listed on this record.

What transport does Maintainer Skills Lab use?

Maintainer Skills Lab runs locally over stdio.

When was Maintainer Skills Lab last checked?

Last checked 2026-10-26. Curated developer tools, AST analyzers, and automated verification workflows.

From the publisher

Documentation

Maintainer Skills Lab

Shared skills and agent profiles for code review, bug investigation, documentation, and ML debugging. Edit the source in skills/ and agents/; tools/kit.py sync generates the client-specific files.

Quick start · Skills · Client formats · Task examples

Workflows

The library provides instructions and local tools for coding assistants:

Rewrite drafts while checking that facts, code, and caveats survive the edit.
Reproduce reported bugs and compare behavior before and after a fix.
Investigate shapes, losses, gradients, and other ML training failures.
Review pull requests and dependency updates against concrete failure cases.

Generated files are checked against their source. That validates the export, not the behavior of every client or model. See the evaluation guide for the distinction between fixtures and recorded agent runs.

Quick start

Start with one skill, or install the full library. The installation guide covers client-specific paths and removal.

1. Claude Code

/plugin marketplace add 00200200/maintainer-skills-lab
/plugin install mkl-humanize@maintainer-skills-lab

2. Codex, Cursor, or OpenCode

From your destination project directory, use the Skills CLI with Node.js 22.20.0+ and Git:

npx [email protected] add 00200200/maintainer-skills-lab --skill mkl-humanize --agent codex --copy

Use --agent cursor or --agent opencode for those clients. This installs a project-local copy, not a global skill.

3. Python Setup

With Python 3.11+, install the full library into an existing project:

git clone https://github.com/00200200/maintainer-skills-lab.git
cd maintainer-skills-lab
python3 tools/kit.py install --target claude --project /path/to/your/repo

The skills library

The source library currently contains 30 skills and 9 agent profiles.

Skill	What it does
Humanize	Edit drafts and check for changed facts or technical details.
Reproduce bug	Build a minimal reproduction with observable results.
Verify fix	Compare the regression before and after a change.
Debug ML	Investigate NaNs, shape errors, and convergence problems.
Review PR	Identify defects with concrete inputs and consequences.
Write README	Document features, setup, and limitations supported by the code.

Browse the full catalogue and client formats.

Development checks

These checks validate sources, generated files, and local fixtures. They do not start a model session or establish live-client behavior.

python3 tools/kit.py check
python3 tools/kit.py sync --check
python3 -m unittest discover -s tests -v
python3 examples/bugfix/run.py
python3 tools/kit.py build

After editing shared skills or agents, run python3 tools/kit.py sync and include the generated outputs in the same change. Do not edit provider copies directly.

Contribute

See the contributor guide, suggest a workflow, report a bug, or propose a token or latency optimization. You can also browse help-wanted and good first issues.

License

MIT. This is an independent community project.

Maintainer Skills Lab

0020020048starsClaim this server

Curated developer tools, AST analyzers, and automated verification workflows.

Documentation

Install Run on PlugMCPClaim this server

GitHub Website

Run on PlugMCP

1# Maintainer Skills Lab

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-26

Stars: 48
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Maintainer Skills Lab?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Maintainer Skills Lab need an API key?

No required environment variable is listed on this record.

What transport does Maintainer Skills Lab use?

Maintainer Skills Lab runs locally over stdio.

When was Maintainer Skills Lab last checked?

Last checked 2026-10-26. Curated developer tools, AST analyzers, and automated verification workflows.

From the publisher

Documentation

Maintainer Skills Lab

Quick start · Skills · Client formats · Task examples

Workflows

The library provides instructions and local tools for coding assistants:

Rewrite drafts while checking that facts, code, and caveats survive the edit.
Reproduce reported bugs and compare behavior before and after a fix.
Investigate shapes, losses, gradients, and other ML training failures.
Review pull requests and dependency updates against concrete failure cases.

Quick start

Start with one skill, or install the full library. The installation guide covers client-specific paths and removal.

1. Claude Code

/plugin marketplace add 00200200/maintainer-skills-lab
/plugin install mkl-humanize@maintainer-skills-lab

2. Codex, Cursor, or OpenCode

From your destination project directory, use the Skills CLI with Node.js 22.20.0+ and Git:

npx [email protected] add 00200200/maintainer-skills-lab --skill mkl-humanize --agent codex --copy

Use --agent cursor or --agent opencode for those clients. This installs a project-local copy, not a global skill.

3. Python Setup

With Python 3.11+, install the full library into an existing project:

git clone https://github.com/00200200/maintainer-skills-lab.git
cd maintainer-skills-lab
python3 tools/kit.py install --target claude --project /path/to/your/repo

The skills library

The source library currently contains 30 skills and 9 agent profiles.

Skill	What it does
Humanize	Edit drafts and check for changed facts or technical details.
Reproduce bug	Build a minimal reproduction with observable results.
Verify fix	Compare the regression before and after a change.
Debug ML	Investigate NaNs, shape errors, and convergence problems.
Review PR	Identify defects with concrete inputs and consequences.
Write README	Document features, setup, and limitations supported by the code.

Browse the full catalogue and client formats.

Development checks

These checks validate sources, generated files, and local fixtures. They do not start a model session or establish live-client behavior.

python3 tools/kit.py check
python3 tools/kit.py sync --check
python3 -m unittest discover -s tests -v
python3 examples/bugfix/run.py
python3 tools/kit.py build

After editing shared skills or agents, run python3 tools/kit.py sync and include the generated outputs in the same change. Do not edit provider copies directly.

Contribute

See the contributor guide, suggest a workflow, report a bug, or propose a token or latency optimization. You can also browse help-wanted and good first issues.

License

MIT. This is an independent community project.