DEVOPS

Flag Lighthouse regressions against the production baseline

Audits each Vercel preview, compares its Lighthouse scores to the current production baseline stored in Postgres.

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerVercel preview deployment readyVercelVercel
  • ActionRun Lighthouse audit on preview URLBrowserbase
  • ActionFetch production baseline metricsPostgreSQLPostgres
  • LogicCompute deltas vs allowed drift
  • OutputOpen Linear regression issue and hold deployLinearLinear

What it does

Instead of a fixed score threshold, this gate measures change. It audits the preview, looks up the latest production baseline from a Postgres table, and computes the delta per metric. A small dip is tolerated; a regression past your allowed drift blocks promotion and files a Linear issue assigned to the PR author with the before and after numbers.

When to use it

Use this when absolute Lighthouse targets are unrealistic for a heavy app but you still want to stop any single PR from making things measurably worse. Great for teams optimizing incrementally where the rule is do no harm.

How it works

  1. 1A Vercel deployment-ready webhook fires with the preview URL and commit SHA.
  2. 2A Lighthouse run audits the preview and records LCP, CLS, TBT, and category scores.
  3. 3A Postgres query fetches the current production baseline metrics.
  4. 4A logic step computes per-metric deltas and checks them against the allowed drift.
  5. 5If a regression exceeds the budget, a Linear issue is opened with the diff and the deploy is held; otherwise the new scores are written back as the candidate baseline.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect VercelDeploys, runtime logs, analytics.
  2. 2
    Connect BrowserbaseHeadless browsers, sessions, replays.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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