u/Sorry-Quantity728

I built a Claude Code skill called Stack Changes.

The idea is simple: one diff, one thesis.

A lot of AI coding tools are good at generating code, but that often creates a new problem: the giant PR. You start with a reasonable change, then add a refactor, tests, cleanup, CLI wiring, and a few behavior changes. Suddenly reviewers are looking at 1,000+ lines and doing their best to mentally untangle it.

This skill tries to solve the problem before review.

You give it a large local diff, commit, branch, PR, CL, or diff, and it produces a stacked landing plan: refactors first, behavior changes later, each step making one clear argument and building/testing on its own.

Example:

Before:
+1,019 lines, 27 files, 1 PR

After:
[1/7] refactor: introduce Money/Transaction domain model
[2/7] refactor: route rendering through query pipeline
[3/7] refactor: extract Formatter interface + registry
[4/7] feat: add CSV, JSON, HTML, and Markdown formatters
[5/7] feat: add filtering, sorting, and grouping
[6/7] feat: add per-category budgets
[7/7] feat: wire up CLI

It is not meant to be another AI PR reviewer. It does not primarily hunt for bugs or post comments. It is more like a PR authoring coach: before asking humans to review a change, make the change reviewable.

A few things I tried to make practical:

• It works with GitHub, Graphite, Sapling, Gerrit, Phabricator, ghstack, spr, and plain git.
• It looks at local repo context like changed files, review system, ownership boundaries, build/test hints, and project conventions.
• The demo stack is verified in CI by checking out each step and running the relevant build/test commands.

Repo: https://github.com/Ishtiaqhossain/claude_stack_changes

Would love feedback from people using Claude Code, stacked PRs, or review-heavy workflows. Curious whether this matches how you split large changes in practice.