ml-experiment-tracker
Guides ML experiment logging, versioning, and reproducibility using tools like MLflow, Weights & Biases, and DVC for systematic model development.
npx skills add majiayu000/claude-skill-registry --skill ml-experiment-tracker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Experiment Tracker This skill provides guidance for systematic machine learning experimentation with proper tracking, versioning, and reproducibility practices. ## Core Competencies - **Experiment Tracking**: MLflow, Weights & Biases (wandb), Neptune, Comet - **Data Versioning**: DVC, Delta Lake, LakeFS - **Model Registry**: Version control for trained models - **Reproducibility**: Environment, code, data, and hyperparameter tracking ## Experiment Tracking Fundamentals ### What to Track Every experiment should log: | Category | Items | Why | |----------|-------|-----| | Code | Git commit hash, branch, diff | Reproduce exact code state | | Data | Dataset version, hash, lineage | Know which data was used | | Environment | Python version, dependencies, hardware | Reproduce runtime | | Hyperparameters | All config values | Understand what changed | | Metrics | Loss, accuracy, custom metrics | Compare performance | | Artifacts | Models, plots, predictions | Preserve outputs | ### Experiment Organization ``` project/ ├── experiments/ │ ├── baseline/ # Initial experiments │ ├── feature-engineering/ # Data improvements │ ├── architecture/ # Model changes │ └── hyperparameter/ # Tuning
- Core Competencies
- Experiment Tracking Fundamentals
- What to Track
- Experiment Organization
- MLflow Patterns
- Basic Experiment Logging
- Model Registry Workflow
- Weights & Biases Patterns
- Project Structure
- Hyperparameter Sweeps
- DVC for Data Versioning
- Setup and Usage
- DVC Pipeline Definition
- Reproducibility Checklist
Initialize DVC in git repo dvc init Track large files dvc add data/training.csv git add data/training.csv.dvc data/.gitignore git commit -m "Add training data v1" Push to remote storage dvc remote add -d storage s3://bucket/dvc dvc push Create pipeline
What does the ml-experiment-tracker skill do?
Guides ML experiment logging, versioning, and reproducibility using tools like MLflow, Weights & Biases, and DVC for systematic model development.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-experiment-tracker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.