Safeguarding AI Generated Code

Safeguarding AI-Generated Code

Overview

Use Code Health safeguards before declaring AI-touched code ready. The goal is to catch maintainability regressions early and prevent agents from normalizing technical debt.

When to Use

• The agent changed code and is about to suggest a commit.
• The user asks whether a branch or staged changes are safe to merge.
• The workflow needs a quality gate for AI-generated code.

Do not use this skill for broad refactoring discovery or project-level prioritization.

Quick Reference

• code_health_review: Review each AI-modified file immediately after the change.
• pre_commit_code_health_safeguard: Check staged or modified files before commit.
• analyze_change_set: Check a branch or PR-style change set against a base ref.

Implementation

1. After each AI modification to a file, run code_health_review on that file.
2. If the review reports maintainability problems or regression risk, refactor the file in small steps and review it again.
3. Run pre_commit_code_health_safeguard before commit-oriented recommendations as a broader gate across staged or modified files.
4. Run analyze_change_set before PR-oriented recommendations as a final branch-level gate.
5. If either later gate reports a regression, inspect the affected files with code_health_review and keep iterating until the issue is removed or the user explicitly accepts the risk.

Common Mistakes

• Waiting until commit time to run the first Code Health check.
• Treating safeguard output as optional guidance instead of a release gate.
• Declaring work done after a failing safeguard.
• Jumping straight to broad rewrites instead of inspecting the flagged files first.
• Treating an accepted risk as invisible; call it out explicitly.