npx skills add majiayu000/claude-skill-registry --skill mlflow-g1joshi-agent-skills --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.
# MLflow MLflow is the standard for tracking experiments. v3.0 (2025) pivots to **GenAI**, adding LLM Tracing, Prompt Management, and "LLM-as-a-Judge". ## When to Use - **Experiment Tracking**: Logging hyperparameters (`lr=0.01`) and metrics (`accuracy=0.98`). - **GenAI Tracing**: Visualizing the full chain of a RAG application. - **Model Registry**: Versioning models (`my-model/v3`) for deployment. ## Core Concepts ### Tracking URI Where logs are stored (local `./mlruns` or remote `http://mlflow-server`). ### Autologging `mlflow.autolog()` automatically captures params from Scikit-learn, PyTorch, etc. ### LLM Tracing OpenTelemetry-based tracing to debug prompt chains. ## Best Practices (2025) **Do**: - **Use `mlflow.evaluate()`**: To run "LLM-as-a-Judge" metrics on your RAG pipeline. - **Use Prompt Engineering UI**: MLflow 3.0 has a UI to iterate on prompts. **Don't**: - **Don't use it for data storage**: Log artifacts (models), not datasets. Log metadata about datasets instead. ## References - [MLflow Documentation](https://mlflow.org/)
- When to Use
- Core Concepts
- Tracking URI
- Autologging
- LLM Tracing
- Best Practices (2025)
- References
What does the mlflow skill do?
MLflow ML lifecycle management. Use for ML experiment tracking.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlflow-g1joshi-agent-skills --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.