ml-pipeline-orchestrator

What This Skill Does

The ml-pipeline-orchestrator skill acts as the bridge between standard software engineering practices and the iterative, experimental nature of data science. By leveraging the SpecWeave framework, it imposes a rigid, version-controlled structure on machine learning development. Instead of disparate, messy Jupyter notebooks and "lost" experiment results, this skill forces every model training session and feature engineering effort into a formal increment lifecycle (spec → plan → tasks → implement → validate). It manages the entire workflow, from defining success metrics and data schemas in spec.md to organizing serialized model artifacts and tracking experiments in dedicated subdirectories. This ensures that every ML feature is reproducible, traceable, and thoroughly documented, transforming ad-hoc data science into a disciplined, enterprise-ready pipeline.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/anton-abyzov/sw-ml-pipeline-orchestrator

Once installed, the orchestrator will automatically hook into your SpecWeave increment workflow whenever you initiate a machine learning-related task.

Use Cases

• End-to-End Model Lifecycle: Perfect for teams moving from initial data exploration to production-grade deployment who need strict versioning.
• Experiment Tracking: Use it when you need to compare multiple model architectures (e.g., XGBoost vs. Neural Nets) while keeping performance metrics and configuration files side-by-side.
• Automated Documentation: Ideal for regulated industries or high-compliance environments where every decision, from feature engineering choices to evaluation metrics, must be documented in living files.
• Team Collaboration: Ensures that any team member can pick up an ML increment and understand the current state, history, and evaluation results without relying on a single person's context.

Example Prompts

1. "OpenClaw, please build a recommendation model increment. I need to predict product clicks based on user history with a precision of at least 0.25."
2. "I'm starting a new ML pipeline for customer churn prediction. Help me initialize the spec and planning tasks."
3. "Let's optimize our current model training workflow. Create a new increment for testing a gradient boosting approach against our existing baseline."

Tips & Limitations

• Strictness: This skill is intentionally strict. It will prevent you from skipping the spec/plan phase. Ensure you have your data requirements ready before starting.
• File Management: All artifacts generated (models, logs, notebooks) are stored within the increment directory; avoid manual file movement to maintain link integrity.
• Scope: While it orchestrates the pipeline, it relies on your local environment or specified cloud compute for heavy-duty GPU training tasks; it does not replace your cluster infrastructure.