A standardized framework for planning and documenting reproducible machine learning experiments before training begins.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ml-experiment-tracker
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ml-experiment-tracker using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is ML Experiment Tracker?
The ML Experiment Tracker is an essential skill designed to bridge the gap between experimental design and execution in the machine learning lifecycle. By providing a structured environment to define datasets, model architectures, and parameter search spaces, this skill ensures that every training run is documented and reproducible. As part of the broader ecosystem of Openclaw Skills, it helps developers and data scientists maintain high standards in MLOps by enforcing the definition of metrics and acceptance thresholds before any training code is executed.
This skill focuses on pre-training standardization, ensuring that artifacts, versions, and performance baselines are not afterthoughts but are instead integral parts of the experiment's DNA. It provides the necessary structure to produce machine-readable run plans that can be easily integrated into various tracking systems.
ML Experiment Tracker Use Cases
- Standardizing experiment definitions across a research or engineering team.
- Preparing tracking-ready run plans for automated MLOps pipelines.
- Ensuring baseline comparisons and success thresholds are set prior to model training.
- Generating consistent, machine-readable configurations for reproducible research.
How ML Experiment Tracker Works
- Define the dataset, specific target task, model family, and the parameter search space required for the run.
- Establish key performance metrics and success thresholds to determine experiment viability.
- Generate a structured run plan that explicitly details versioning and artifact expectations.
- Export the finalized plan for execution within your preferred experiment tracking tools.
ML Experiment Tracker Setup
To start using this tool within your Openclaw Skills environment, follow these steps:
# Navigate to the skill directory
cd skills/ml-experiment-tracker
# Generate a new experiment plan using the bundled script
python scripts/build_experiment_plan.py
Ensure you review the reproducibility checklist located in references/tracking-guide.md before finalizing your plan.
ML Experiment Tracker Data Schema & Taxonomy
| Data Component | Description |
|---|---|
| Run Plan | A structured configuration file containing all experiment parameters. |
| Metrics Schema | Definitions of primary metrics and baseline acceptance criteria. |
| Artifact Registry | Expected outputs, including model weights and evaluation logs. |
| Documentation | Reproducibility checklists and tracking guides for standardization. |
ML Experiment Tracker Advanced Features
- Script-based generation of consistent run plans via the built-in build_experiment_plan utility.
- Pre-defined reproducibility checklists to ensure compliance with MLOps best practices.
- Machine-readable output formats designed for seamless integration with external experiment tracking platforms.
- Strict guardrails for input explicitness to minimize human error in experiment definitions.
SKILL.md
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