Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "experiment-tracking-setup",
+  "description": "Set up ML experiment tracking",
+  "version": "1.0.0",
+  "author": {
+    "name": "Claude Code Plugins",
+    "email": "[email protected]"
+  },
+  "skills": [
+    "./skills"
+  ],
+  "commands": [
+    "./commands"
+  ]
+}

README.md

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+# experiment-tracking-setup
+
+Set up ML experiment tracking

commands/track-experiments.md

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+---
+description: Execute AI/ML task with intelligent automation
+---
+
+# AI/ML Task Executor
+
+You are an AI/ML specialist. When this command is invoked:
+
+1. Analyze the current context and requirements
+2. Generate appropriate code for the ML task
+3. Include data validation and error handling
+4. Provide performance metrics and insights
+5. Save artifacts and generate documentation
+
+Support modern ML frameworks and best practices.

plugin.lock.json

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+{
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+  "pluginId": "gh:jeremylongshore/claude-code-plugins-plus:plugins/ai-ml/experiment-tracking-setup",
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+    "generatedAt": "2025-11-28T10:18:26.519235Z",
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+    "repoRoot": "/Users/zhongweili/projects/openmind/42plugin-data"
+  },
+  "manifest": {
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+    "description": "Set up ML experiment tracking",
+    "version": "1.0.0"
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+}

skills/experiment-tracking-setup/SKILL.md

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+---
+name: setting-up-experiment-tracking
+description: |
+  This skill automates the setup of machine learning experiment tracking using tools like MLflow or Weights & Biases (W&B). It is triggered when the user requests to "track experiments", "setup experiment tracking", "initialize MLflow", or "integrate W&B". The skill configures the necessary environment, initializes the tracking server (if needed), and provides code snippets for logging experiment parameters, metrics, and artifacts. It helps ensure reproducibility and simplifies the comparison of different model runs.
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## Overview
+
+This skill streamlines the process of setting up experiment tracking for machine learning projects. It automates environment configuration, tool initialization, and provides code examples to get you started quickly.
+
+## How It Works
+
+1. **Analyze Context**: The skill analyzes the current project context to determine the appropriate experiment tracking tool (MLflow or W&B) based on user preference or existing project configuration.
+2. **Configure Environment**: It configures the environment by installing necessary Python packages and setting environment variables.
+3. **Initialize Tracking**: The skill initializes the chosen tracking tool, potentially starting a local MLflow server or connecting to a W&B project.
+4. **Provide Code Snippets**: It provides code snippets demonstrating how to log experiment parameters, metrics, and artifacts within your ML code.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Start tracking machine learning experiments in a new project.
+- Integrate experiment tracking into an existing ML project.
+- Quickly set up MLflow or Weights & Biases for experiment management.
+- Automate the process of logging parameters, metrics, and artifacts.
+
+## Examples
+
+### Example 1: Starting a New Project with MLflow
+
+User request: "track experiments using mlflow"
+
+The skill will:
+1. Install the `mlflow` Python package.
+2. Generate example code for logging parameters, metrics, and artifacts to an MLflow server.
+
+### Example 2: Integrating W&B into an Existing Project
+
+User request: "setup experiment tracking with wandb"
+
+The skill will:
+1. Install the `wandb` Python package.
+2. Generate example code for initializing W&B and logging experiment data.
+
+## Best Practices
+
+- **Tool Selection**: Consider the scale and complexity of your project when choosing between MLflow and W&B. MLflow is well-suited for local tracking, while W&B offers cloud-based collaboration and advanced features.
+- **Consistent Logging**: Establish a consistent logging strategy for parameters, metrics, and artifacts to ensure comparability across experiments.
+- **Artifact Management**: Utilize artifact logging to track models, datasets, and other relevant files associated with each experiment.
+
+## Integration
+
+This skill can be used in conjunction with other skills that generate or modify machine learning code, such as skills for model training or data preprocessing. It ensures that all experiments are properly tracked and documented.

skills/experiment-tracking-setup/assets/README.md

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+# Assets
+
+Bundled resources for experiment-tracking-setup skill
+
+- [ ] mlflow_example_code.py Example code snippets for logging experiments with MLflow.
+- [ ] wandb_example_code.py Example code snippets for logging experiments with Weights & Biases.
+- [ ] experiment_tracking_template.md A template for documenting experiment results, including parameters, metrics, and artifacts.
+- [ ] configuration_template.yaml A template for configuring the experiment tracking setup.

skills/experiment-tracking-setup/references/README.md

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+# References
+
+Bundled resources for experiment-tracking-setup skill
+
+- [ ] mlflow_api_reference.md Detailed documentation of the MLflow API, including logging parameters, metrics, and artifacts.
+- [ ] wandb_api_reference.md Detailed documentation of the Weights & Biases API, including logging parameters, metrics, and artifacts.
+- [ ] experiment_tracking_best_practices.md A guide to best practices for experiment tracking, including naming conventions, metric selection, and artifact storage.
+- [ ] supported_libraries.md List of supported ML libraries and their integration with MLflow and W&B.

skills/experiment-tracking-setup/scripts/README.md

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+# Scripts
+
+Bundled resources for experiment-tracking-setup skill
+
+- [ ] initialize_mlflow.py Automates MLflow initialization, including setting up the tracking URI and creating the experiment if it doesn't exist.
+- [ ] initialize_wandb.py Automates Weights & Biases initialization, including setting up the project and entity.
+- [ ] log_experiment_params.py A utility script to log experiment parameters to the chosen tracking system (MLflow or W&B).
+- [ ] log_experiment_metrics.py A utility script to log experiment metrics to the chosen tracking system (MLflow or W&B).
+- [ ] log_experiment_artifacts.py A utility script to log experiment artifacts (e.g., models, plots) to the chosen tracking system (MLflow or W&B).