forge-robustness-first

What problem does it solve?

This skill helps engineering teams implement a robustness-first ML development workflow that prevents overfitting and promotes generalization by requiring explicit partitions, gate checks, and guardrails.

Core Features & Use Cases

• Three-stage data partitions (optimization, evaluation, deployment) with strict temporal ordering to prevent lookahead and data leakage.
• Executable quality gates covering predictive performance, stability, and robustness that all must pass to validate deployment readiness.
• Config-driven pipeline components (data loading, feature engineering, modeling, validation, and evaluation reporting) enabling reproducible experiments and rapid iteration.

Quick Start

Configure a robustness-first run to enforce three-stage validation, then run the evaluation pipeline on a sample dataset and review gate outcomes.