--- title: "Reuse validation for unchanged inputs" description: "Skip repeated validation only when the relevant inputs and previous passing result establish that the work is unchanged." url: https://starsling.dev/ci-playbooks/techniques/skip-identical-validation canonicalUrl: https://starsling.dev/ci-playbooks/techniques/skip-identical-validation --- # Reuse validation for unchanged inputs Skip repeated validation only when the relevant inputs and previous passing result establish that the work is unchanged. ## How to choose an approach Compare the examples by repository setup, prerequisites, and risks before adopting one. Each company implemented this in its own environment; measure each change against your own baseline. - **[Linear](https://starsling.dev/ci-playbooks/linear#skip-work-already-passed)**: Use it when Expensive jobs run on pull requests that leave their inputs unchanged, or run again after the same inputs already passed. Check before adopting: The gating jobs sit on the critical path, so keep them small and fast. A recorded pass has to cover every input the skipped job reads. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#skip-identical-revalidation)**: Use it when A formatter or code generator re-validates output that is identical to its input. Check before adopting: Compare the final output, after every restoration step. Every changed result still goes through the original checks. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Reuse validation for unchanged inputs. Skip repeated validation only when the relevant inputs and previous passing result establish that the work is unchanged. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/skip-identical-validation.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 companies used this technique Read what each company changed and follow the original sources. ### Linear: Skip jobs whose tests already passed for the same inputs Every Linear run first checks which paths a pull request touched and whether its tests already passed for the same inputs. The workflow gates on those checks at the job level, so skipped work never reserves a runner. Evidence: Documented technique Use it when: Expensive jobs run on pull requests that leave their inputs unchanged, or run again after the same inputs already passed. Before applying it: The gating jobs sit on the critical path, so keep them small and fast. A recorded pass has to cover every input the skipped job reads. [Read more in the Linear playbook](https://starsling.dev/ci-playbooks/linear#skip-work-already-passed) [Primary source](https://linear.app/now/ci-bottleneck-reworked#optimize-the-jobs-that-gate-other-work) ### OpenAI: Avoid revalidating a transformation that changed nothing The Ruby SDK's RBS formatter compares the final string with the original and skips lexing both again when they are identical. Changed output still gets the comment-preservation check before it is written. Evidence: Source PR/commit evidence Formatter median 5.79s to 4.11s across four warm local runs ([Source](https://github.com/openai/openai-ruby/pull/966)) Use it when: A formatter or code generator re-validates output that is identical to its input. Before applying it: Compare the final output, after every restoration step. Every changed result still goes through the original checks. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#skip-identical-revalidation) [Primary source](https://github.com/openai/openai-ruby/pull/966) ## 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