Ml Experiment (Grade A) logo

Ml Experiment (Grade A)Security-tested data-ai skill for Claude AI. Grade A. Use when starting, logging, or reviewing ML experiments — maintains a persistent experiment journal with hypotheses, results, and learnings across

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Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

Ml Experiment is a security-tested data-ai skill for Claude AI, designed to facilitate machine learning (ML) experiments by maintaining a persistent experiment journal. This journal tracks hypotheses, results, and learnings across sessions, enabling users to externalize their experimental reasoning. The tool emphasizes the importance of logging hypotheses before conducting experiments, following the 'Iron Law' that no new experiment should be started without first logging the hypothesis. The experiment journal is structured into two main files: journal.md for running experiment logs and lessons.md for distilled patterns and rules. The process is divided into four phases: logging the hypothesis before an experiment, logging the result after an experiment, reviewing history before the next iteration, and periodically distilling lessons. This structured approach helps users learn from their experiments and avoid repeating unsuccessful attempts.

Key features

  • persistent experiment journal
  • hypothesis logging
  • result tracking
  • lesson distillation
  • integration with ml-iterate, ml-debug, and ml-verify

Pricing

Model
Free
Category
Skills
Rating
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Use cases

Starting a New ML Experiment

Use Ml Experiment to log your hypothesis before starting a new ML experiment, ensuring a structured approach to testing your ideas.

Reviewing Previous Experiments

Utilize Ml Experiment's journal to review previous experiments, understand what worked and what didn't, and plan your next steps accordingly.

Pros & Cons

Pros

  • structured approach to ML experiments
  • persistent experiment journal
  • emphasis on hypothesis logging
  • facilitates learning from experiments
  • integration with other tools like ml-iterate, ml-debug, and ml-verify

Cons

  • requires adherence to a specific workflow
  • may be overhead for simple experiments
  • 依赖于文本记录的方式可能不适合所有用户

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Q&A

What are the practical limits of using Ml Experiment for simple tests?

The tool requires you to follow its structured workflow—logging a hypothesis before every run and recording results afterward—so for very quick or trivial experiments the extra steps may feel like unnecessary overhead.

Asked by Sami Virtanen · Mar 2, 2026

Can Ml Experiment work with other Claude AI data‑ai skills?

Yes. It integrates directly with ml-iterate, ml-debug, and ml-verify, letting you pass hypothesis and result data between tools without manual copying.

Asked by Anders Lindgren · Feb 18, 2026

How does Ml Experiment help organize my ML workflow?

It creates a persistent experiment journal split into journal.md (for each experiment’s hypothesis, results, and notes) and lessons.md (for distilled patterns and rules), guiding you through hypothesis logging, result tracking, and lesson distillation across four defined phases.

Asked by Rosalind Frost · Jan 3, 2026

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