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DataPython

ML Experiment Journal

by Leeroo-AI

ML Experiment Journal is a Data skill for Claude Code, published by Leeroo-AI in superml.

194 stars18 forkson Leeroo-AI/supermlAdded 2026/09/05+1% in starsRepository updated 2026/03/17
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aiclaude-codecodexcoding-agent-skillscoding-agentscursor-aillmmachine-learningmlmulti-agentpluginskill
Install in seconds
Install ML Experiment Journal
Copy ML Experiment Journal into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/Leeroo-AI/superml/tree/main/skills/ml-experiment ~/.claude/skills/ml-experiment

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Leeroo-AI/superml.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/ml-experiment/SKILL.md in Leeroo-AI/superml
Installs to
~/.claude/skills/ml-experiment
Collection
One of 7 skills cataloged from this repository
Category
Data — 762 skills

What ML Experiment Journal does

ML Experiment Journal keeps a persistent log of experiment hypotheses, results, and lessons across sessions. Use it before, during, and after ML runs to track what changed and what was learned.

ML Experiment Journal is cataloged under Data on DirSkills. ML Experiment Journal comes from a repository tagged ai, claude-code, codex, coding-agent-skills and coding-agents.

Documentation

README

Experiment Journal

Externalize your experimental reasoning. Every ML project is a sequence of hypotheses tested — this skill makes that sequence visible, persistent, and learnable.

The Iron Law

NO NEW EXPERIMENT WITHOUT LOGGING THE HYPOTHESIS FIRST

If you're about to change a hyperparameter, swap a dataset, try a new architecture, or modify a training recipe — write down what you expect to happen and why BEFORE running it. This is how you learn from experiments instead of just running them.

File Structure

Maintain these files in the project root (create if they don't exist):

experiments/
├── journal.md    — Running experiment log (append-only)
└── lessons.md    — Distilled patterns and rules (curated)

Phases

This is the opening of the README. Read the full README on GitHub.

Frequently asked about ML Experiment Journal

  • What else does Leeroo-AI publish alongside ML Experiment Journal?

    ML Experiment Journal is one of 7 skills that DirSkills catalogs from Leeroo-AI/superml, the repository it ships in. Its siblings there include ML Debugging, ML Iteration and ML Planning. Each one is a separate skill with its own page in this directory, installs the same way ML Experiment Journal does, and is maintained by Leeroo-AI in that same repository. The rest of the collection is listed on the Leeroo-AI/superml page.

  • How does ML Experiment Journal compare to other Data skills?

    ML Experiment Journal ranks #707 by stars among the 762 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of ML Experiment Journal against them. Open each page to compare what they document and how they install.

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ML Experiment Journal is one of 7 skills cataloged on DirSkills from Leeroo-AI/superml.

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ML Planning

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