jakepage91/k8s-validation-skills

This is a nascent guardrail project (35 days old, 1 star, 0 forks, no activity) with no discernible user adoption. The idea of validation rules for K8s manifests is not new—mature solutions exist (Kyverno, OPA/Gatekeeper, kubewarden) with years of production deployments, plugin ecosystems, and enterprise backing. The specific angle of 'guardrails for AI-generated code' is a reasonable positioning but lacks differentiation: (1) the core validation logic is commodity; (2) existing policy engines already support AI code contexts; (3) major platforms (AWS ECS validation, Google Config Connector, Azure Policy) are expanding into AI-aware deployment validation. Without published code, research novelty, or traction metrics, this appears to be an early-stage personal project. Platform vendors (OpenAI, Anthropic, Meta) are actively embedding safety checks into code generation models themselves, reducing the need for downstream guardrails. The 6-month displacement horizon reflects both the crowded K8s policy space and the likelihood that AI model providers will bake this validation upstream before a standalone guardrails library gains adoption.