Developer Builds Verification Layer With Gates, Hooks and Mutation Tests for AI Coding

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A developer building a wellbeing analytics platform with Next.js and a Python AI service has documented a structured verification framework designed to catch errors introduced by AI-generated code. The system includes four agent skills, sixteen repository gates, and an edit-time hook, all aimed at proving code correctness rather than just writing it. The project was prompted by a real failure where an AI silently created a duplicate skill file in a gitignored directory, making it invisible to code reviews and diffs. To prevent such drift, the framework enforces a single canonical location for agent instructions and uses filesystem structure — not config files — as the source of truth. The author argues that prose-based instructions lack enforcement mechanisms, making tooling-level guardrails a structural necessity rather than a discipline issue.

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