moyu

To stop AI coding agents from making unrequested or over-engineered code changes, apply this skill to enforce minimal-change thresholds. It detects scope expansion during automated refactoring workflows, classifies diffs by severity, and prompts for confirmation before modifying unrequested files.

Diff analysis scope control for AI code review is a mechanism that classifies code modifications by size and severity. It enforces minimal-change preferences by detecting unrequested abstractions, limiting edits to user-specified files, and prescribing intervention levels like self-revert or scope reset.

To enforce minimal changes when an AI agent modifies code during automated refactoring, instruct the agent to apply scope control rules. This prioritizes the simplest viable patch, reuses existing code, and applies an emergency brake to prevent adding new dependencies or creating unrelated files.

Yes, this AI agent guardrail works with existing PR and code review workflows by analyzing diffs on the fly. It prevents AI coding agents from expanding scope during automated refactoring, ensuring small, reviewable diffs by asking for confirmation before any non-requested modifications occur.

The intervention levels when an AI coding agent expands scope are classified as L1 through L4 severity. Depending on the diff size and scope expansion, the guardrail prescribes specific actions ranging from course correction and self-revert to a full scope reset or emergency brake.

You should not use automated scope control for AI code generation when your workflow explicitly requires the agent to introduce new abstractions, add dependencies, or refactor unrelated modules. The guardrail is designed to prioritize the simplest viable change and will block large, unrequested modifications.