Validate machine learning models for production readiness across multiple languages.
npx skills add https://github.com/robotijn/ctoc --skill ml-model-validator
Please help me install this Agent Skill. Skill: ml-model-validator Source: https://github.com/robotijn/ctoc/tree/main/skills/data-ml/ml-model-validator Command: npx skills add https://github.com/robotijn/ctoc --skill ml-model-validator
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.
Use the ml-model-validator skill to audit your production ML model for all readiness gaps and generate a full validation report.
High-intent search queries and answers about installing and using this skill.
To check ML models for production readiness, you must validate them against critical failure modes including data leakage, unfair bias, distribution drift, and missing safety guardrails before deployment. This prevents costly outages and compliance violations.
Validating LLM deployments requires evaluating model faithfulness, hallucination rates, and prompt-injection resistance. These LLM-specific checks ensure generative models are safe for production inference and meet operational compliance requirements.
ML production validation works across Python, C#, Java, and TypeScript inference stacks. This multi-language support covers training pipeline audits and deploy-readiness checks for classical ML, deep learning, and LLM workflows.
An MLOps compliance check verifies model card completeness, registry versioning, kill-switch implementation, and canary rollout planning. It ensures deployed models meet regulatory and operational standards before shipping to production.
Detecting data leakage and distribution drift involves auditing your training pipeline to identify hidden biases and shifts in data distribution. This validation step flags critical failure modes early, preventing harmful or inaccurate model predictions in production.
UnitOneAIReview AI/ML model supply chains for provenance, lineage, and backdoor indicators.
uzumaki-incMonitor live apps for console errors, performance regressions, and page failures.
pluginagentmarketplaceManage ML lifecycle with MLflow tracking, registry, and deployment pipelines.
RHEcosystemAppEngMonitor deployed InferenceServices for bias and data drift using SPD, DIR, and statistical metrics.
