ml-experiment-tracker

What This Skill Does

The ml-experiment-tracker skill is a robust tool designed to standardize the way machine learning researchers and engineers define their training runs. By moving away from ad-hoc experiment logging, this skill mandates the creation of structured, machine-readable run plans. It ensures that every experiment is documented with clear parameter search spaces, specific metrics for performance evaluation, and defined artifact expectations. This standardization is critical for maintaining reproducibility across complex model training pipelines, allowing teams to compare results accurately and audit the evolution of their models over time.

Installation

To integrate this skill into your environment, use the OpenClaw CLI provided with your installation. Run the following command in your terminal: clawhub install openclaw/skills/skills/0x-professor/ml-experiment-tracker Once installed, you can verify the setup by checking the scripts/ directory for the build_experiment_plan.py utility, which provides the backbone for generating your standardized experiment manifests.

Use Cases

This skill is ideal for data science teams aiming for rigorous MLOps practices. Primary use cases include: 1. Standardizing hyperparameter tuning workflows to avoid drift. 2. Defining objective success thresholds for production models prior to execution. 3. Organizing artifact metadata to ensure downstream systems can locate and evaluate training checkpoints. 4. Facilitating team-wide knowledge sharing by ensuring all experiment plans follow the same schema.

Example Prompts

1. "Build an experiment plan for a ResNet-50 fine-tuning task. Define the parameter search space for learning rate between 1e-5 and 1e-3, set accuracy as the primary metric with an acceptance threshold of 0.85, and output the result in JSON format."
2. "I need to run a baseline check for a new NLP model. Use the ml-experiment-tracker to generate a plan that includes model version 1.2.0 and expected output artifacts like training logs and model weights files."
3. "Review my current experiment configuration and compare it against the reproducibility checklist in references/tracking-guide.md to ensure I haven't missed any required tracking fields."

Tips & Limitations

To maximize the utility of this skill, always complete the experiment plan before initializing any training jobs. The tool works best when integrated into your CI/CD pipeline. A key limitation is that this skill handles the planning phase; it does not automatically execute the training itself. Ensure your local environment has read/write permissions for the artifacts folder to allow the script to save metadata effectively. Keep your metrics measurable and your baseline criteria objective to ensure the generated plans remain actionable.