DEVOPS

Nightly audit of the live Vercel production build with Slack digest

On a nightly schedule, audits the current Vercel production deployment with Lighthouse and a Honeycomb health pull, then sends a single Slack digest summarizing scores, trends…

CategoryDevOps
Enginesim
Difficultybeginner
Triggerschedule
Steps6
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule
  • ActionGet current production deploymentVercelVercel
  • ActionRun Lighthouse on production URLShell
  • ActionPull 24h health stats from HoneycombHoneycomb
  • LogicFlag any threshold breaches
  • OutputPost production health digest to SlackSlack

What it does

This workflow keeps a steady pulse on what is actually live in production. Each night it identifies the current Vercel production deployment, runs a Lighthouse audit against the production URL, pulls 24-hour error and latency stats from Honeycomb, and rolls everything into one readable digest posted to Slack so the team starts the day knowing the state of prod.

When to use it

Use it as an ongoing health report independent of deploy events — useful for catching slow drift in performance or reliability that no single PR gate would flag.

How it works

  1. 1A nightly schedule trigger fires the run.
  2. 2An action queries the Vercel API for the current production deployment and its URL.
  3. 3A shell step runs Lighthouse against the production URL and captures scores.
  4. 4An action pulls the last 24 hours of error rate and p95 latency from Honeycomb.
  5. 5A logic step compares both against thresholds and flags any breaches.
  6. 6An output step posts a formatted Slack digest with scores, telemetry, and flagged items to the team channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect VercelDeploys, runtime logs, analytics.
  2. 2
    Connect ShellRun sandboxed commands inside the workspace.
  3. 3
    Connect HoneycombDistributed traces and queries.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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