CI Pipeline Design From Repository Facts
Design stages, caching and gates for a new pipeline.
Added October 15, 2026. Written with AI assistance and checked for clarity and placeholders; not reviewed by a lawyer, accountant, clinician or other professional. How these prompts are made
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Design a continuous integration pipeline for my repository. Language and build tool: [LANGUAGE_AND_BUILD_TOOL]. Test suites and rough run times: [TEST_SUITES]. CI platform: [CI_PLATFORM]. Team size and merge frequency: [TEAM_AND_FREQUENCY]. Deployment target: [DEPLOY_TARGET]. Produce a stage-by-stage design as a table with Stage | Purpose | Commands (generic, then platform syntax) | Typical time | Fails the build when. Order stages so the cheapest checks run first: lint, type check, unit tests, build, integration tests, security scan, package. Add a caching plan listing exactly which directories or keys to cache and the cache invalidation rule. Specify which stages run on pull requests only, on the main branch only, and nightly. Recommend a time budget for pull-request feedback and show how to hit it with parallelism. Finish with a list of five failure modes of the design (flaky tests, cache poisoning, secrets in logs, runaway minutes, unpinned dependencies) and a mitigation for each. Do not include real secrets; use placeholders. Work in two passes. In the first pass, restate the facts I gave in a short table (language, test suites with minutes, platform, team size, deploy target) and list any fact you had to assume, so I can correct it before you design anything. In the second pass, produce the design. Include a worked timing budget that adds up the stage times on a pull request and shows the critical path before and after parallelism, using my minute figures and showing the sum. Include a branch-protection recommendation that names which stage results must be green before merge. Finish with a fourteen-day adoption plan: week one adds lint, type check and unit tests only; week two adds the build, integration tests and caching; and a measurement to take at the end of each week (median pull-request feedback time, failure rate unrelated to code changes, cache hit rate).
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