evolution-workflow-skill

Automating code changes across native and Docker environments requires a structured evolution workflow that plans, stages, and verifies modifications in a sandbox before exporting them back. This coordinated approach prevents drift during automated governance.

A multi-stage code evolution workflow is a disciplined sequence of READ, PLAN, WAIT, STAGE, VERIFY, and EXPORT actions. It enforces explicit syntax and pytest validation steps with a WAIT gate, ensuring changes are fully audited before proceeding to commit.

To stage and verify code modifications in a Docker sandbox, apply changes in the child container, run explicit syntax and pytest validation steps, and wait at a gating checkpoint. Once verified, export the staged changes back to the parent environment for a native git commit.

No, the evolution workflow does not require full git access inside the Docker container. It supports a child context where the sandbox only stages changes, which are then exported back to the native parent environment that retains full git access for the final commit.

Yes, you can manage automated code governance using cross-container handoffs that transfer verified changes from a Docker sandbox back to the parent environment. This creates an auditable trail and cataloged evolutions while maintaining strict validation boundaries.

You should avoid automated staging workflows if your task cannot tolerate enforced WAIT gates or mandatory pytest validation steps. The strict governance model requires completing explicit verification checkpoints before allowing any export or commit operations to proceed.