From jeremylongshore-claude-code-plugins-plus-skills
Guides MLflow tracking setup including experiment configuration and best practices for ML training workflows.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:mlflow-tracking-setupThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for mlflow tracking setup tasks within the ML Training domain.
This skill provides automated assistance for mlflow tracking setup tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with mlflow tracking setup" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin ejentum-reasoningSets up ML experiment tracking with MLflow or Weights & Biases, configuring environment and generating code for logging parameters, metrics, and artifacts.
Sets up MLflow tracking server, configures autologging for ML frameworks, compares runs with metrics, and manages artifacts for reproducible ML workflows.
Dispatches MLflow tasks to the appropriate sub-skill for tracing, evaluation, debugging, and onboarding. Use when the user needs MLflow help but hasn't specified a sub-skill.