refactor-with-docs

To safely refactor a module without changing behavior, use a structured loop that researches the target, applies atomic single-file edits, and runs coherence checks to preserve behavioral parity. This approach isolates changes to a single mutable file per cycle, ensuring safe and repeatable refactoring.

A research-driven refactor loop is a workflow that performs fresh research before making targeted code edits, then validates changes through automated coherence checks. It drives decision-making across multiple experiments to improve code health while enforcing branch isolation and strict logging for auditability.

You can automate code cleanup for architectural drift by running a repeatable refactor loop that targets degraded modules. The loop executes research, per-cycle atomic edits, and evaluations to improve cohesion, automatically reverting changes that fail behavioral parity constraints.

Yes, you can revert a refactoring step if the coherence check fails. The workflow enforces strict logging and per-cycle branch isolation, supporting immediate rollback to maintain behavioral parity when automated evaluation guardrails disapprove of targeted edits.

You should use an automated refactor loop when a module shows degraded health or architectural drift and requires safe, repeatable cleanliness. It is designed for codebases needing strict auditability, where research, targeted edits, and evaluation must drive decision-making across multiple experiments.

No, behavior-preserving refactoring in this workflow targets only a single mutable module per cycle. It enforces atomic edits and branch isolation to maintain strict behavioral parity, ensuring safe rollback and auditability rather than modifying multiple files simultaneously.