--- title: "Lead a large pull request with its core change and keep formatting noise out of the review" description: "A guided review splits a large diff into chapters in the order the work was reasoned through." url: https://starsling.dev/ci-playbooks/techniques/guided-review-order canonicalUrl: https://starsling.dev/ci-playbooks/techniques/guided-review-order --- # Lead a large pull request with its core change and keep formatting noise out of the review A guided review splits a large diff into chapters in the order the work was reasoned through. ## How to choose an approach This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it. - **[Linear](https://starsling.dev/ci-playbooks/linear#guided-review-order)**: Use it when Large pull requests, often written by agents, are reviewed in file-system order with formatting changes mixed into the substantive edits. Check before adopting: The description is a guide, and the diff itself stays complete. Formatting-only changes go in their own commit, so blame and revert stay clean. Adapted for GitHub Actions; compare the original system with yours. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Lead a large pull request with its core change and keep formatting noise out of the review. A guided review splits a large diff into chapters in the order the work was reasoned through. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/guided-review-order.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How Linear used this technique Read what Linear changed and follow the original sources. ### Linear: Lead a large pull request with its core change and keep formatting noise out of the review **Translated to GitHub Actions** - What Linear built: Linear's Diffs break a pull request into chapters, with the core of the change first and structural highlighting that strips formatting changes. - Your GitHub Actions version: A pull request description an agent writes in reading order, with formatting-only changes in their own commit. Linear's Diffs break a pull request into chapters that follow the order the work was reasoned through: the core of the change first, then its consequences, with auxiliary changes and glue code kept separate. Structural diff highlighting strips the formatting changes, so what is left on the page is the actual change. In a repository without Diffs, the same order comes from a pull request description an agent writes and a formatting-only change in its own commit. Evidence: Company-reported result or observation A 2,000-line PR becomes legible in a single sitting. ([Source](https://linear.app/now/code-review-should-be-fast)) Use it when: Large pull requests, often written by agents, are reviewed in file-system order with formatting changes mixed into the substantive edits. Before applying it: The description is a guide, and the diff itself stays complete. Formatting-only changes go in their own commit, so blame and revert stay clean. GitHub Actions adaptation: Compare the source system with your own workflow before applying this translation. [Read more in the Linear playbook](https://starsling.dev/ci-playbooks/linear#guided-review-order) [Primary source](https://linear.app/now/code-review-should-be-fast) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed