Install prebuilt packages and toolchains

Download compatible prebuilt dependencies instead of compiling the same toolchain or package during every CI run.

Prompt

Evaluate and apply this CI technique to my repository: Install prebuilt packages and toolchains.

Download compatible prebuilt dependencies instead of compiling the same toolchain or package during every CI run.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/prebuilt-toolchains.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.

View all playbooks

How companies used this technique

Read what each company changed and follow the original sources.

Install prebuilt packages

The Python SDK selected NumPy versions that ship wheels for every supported interpreter, so installs download a binary. The same PR restored environment caching and ran only the HTTPX 2 suite in the HTTPX 2 job; the job timings cover all three changes. OpenAI later moved the repository to uv (openai/openai-python#3653).

HTTPX 2 job 10m42s to 2m12s; Python 3.14 job 8m37s to 1m53s Source

Repository moved to uv; the NumPy wheel selection carried into uv.lock Source

Use it when: Install logs show a source build for a dependency that also ships compatible binaries.
Before applying it: Check interpreter, platform and dependency compatibility for every version in the matrix before pinning.

Download the compiler your build depends on

SubmitQueue registered a prebuilt protoc toolchain, so cold builds no longer compile protoc and its C++ dependencies. The same 35 tests passed before and after.

On the author's machine, a cold make test after bazel clean took about 84s before and about 11s after, with the same 35 tests passing. Source

Use it when: Cold runs spend substantial time compiling an upstream tool that has a compatible, trusted binary release.
Before applying it: Verify platform support, compiler compatibility, checksums and generated output, and keep a source build for platforms without a binary release.

How companies used this technique

Install prebuilt packages

Download the compiler your build depends on

Related techniques

Bake stable dependencies into runner images

Cache package downloads and dependency stores

Cache the directories the build actually uses

Company Playbooks

Related guides

Cache workflow dependencies

Cut repeated test setup

Share setup steps across jobs

Optimize npm installs

Optimize pnpm installs