AI writes fast. Pipelines make it right.

It starts knowing what you know.

Before any model writes code, the task picks up your stored product and feature context.Your product and feature context loads first.

It picks up the skills you want.

Choose the skills this task should use, like Test-driven change and Conventional commits. The pipe gathers them before any code gets written.The skills you chose come along, before any code.

A strong model writes the plan.

Spend on thinking where it counts. The plan sets the scope, the files to touch and the tests to write.Spend where the thinking counts.

A cheaper model does the heavy lifting.

It builds against the plan, so a smaller price tag doesn’t mean smaller quality.It follows the plan, so quality holds.

Slop gets caught here, not in production.

1. ReviewingChecking the change against the plan.Checked against the plan.
2. RejectedThe empty result set isn’t handled.Empty results not handled.
3. ReworkBack up the pipe. The build station fixes it.Back up the pipe to fix.
4. ApprovedReviewed again. It passes.Passes the second look.

Only work that passes every gate leaves the pipe.

Tests run on every change before it can leave the pipe.Tests run before anything leaves.

The pull request opens on GitHub.

Reviewed and tested, with the task, the pipe and the run in its description. Nothing merges without the approval you set.Nothing merges without your approval.

Every station, itemised.

Swap any station’s model. Keep the pipe.

Claude to Codex, big to small. Nothing gets rebuilt.