ml-experimentation

What problem does it solve?

Planning, executing, and reporting machine learning experiments can be complex and error-prone; this skill provides a repeatable lifecycle with journaling, diagnostics, and a canonical artifact structure to improve reproducibility and communication.

Core Features & Use Cases

• Experiment lifecycle management: planning, fast iteration, script execution, logging, journaling, plotting, and scientific report writing.
• Deterministic runs and artifacts: strict canonical tree with JOURNAL.md, runs/, logs/, plots/ to enable reproducibility and auditability.
• Scalability and governance: supports rapid de-risked runs (< 60 seconds) and full runs for hypothesis validation, with structured data and plots for reporting.
• Use Case: a data scientist plans a new experiment, conducts quick iterations, records observations in JOURNAL.md, and publishes a final report with plots and tables.

Quick Start

Plan a new hypothesis, initialize an experiment, and run an initial de-risked iteration using the provided scripts to generate a JOURNAL.md and baseline logs.