ml-model-validator

What problem does it solve?

This Skill eliminates the high risk of deploying machine learning models that fail in production due to hidden data leakage, unfair bias, distribution drift, missing safety guardrails, or incomplete validation, avoiding costly outages, compliance violations, and harm to end users.

Core Features & Use Cases

• End-to-end Production Validation: Audits ML models for performance, fairness, robustness, drift, and full deployment readiness across classical ML, deep learning, and LLM workflows.
• Multi-Language Support: Covers validation for Python, C#, Java, and TypeScript inference stacks, including LLM-specific checks for faithfulness, hallucination rate, and prompt-injection resistance.
• Use Case: For a credit risk model, this Skill would flag missing fairness audits across protected attributes, absent drift monitoring, lack of a kill switch, and incomplete model card documentation before the model is allowed to ship.

Quick Start

Use the ml-model-validator skill to audit your production ML model for all readiness gaps and generate a full validation report.