DEVOPS

Schedule a 30-day removal reminder when a flag hits 100%

When your flag platform fires a webhook that a flag reached 100% rollout, this records the date and, 30 days later, checks it's still fully on and opens a cleanup PR.

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFlag platform webhook on 100% rolloutHTTP webhook
  • ActionRecord flag and completion date in PostgresPostgreSQLPostgres
  • LogicAfter 30 days, re-check flag is still fully onDatadogDatadog
  • ActionOpen GitHub removal PR for the matured flagGitHubGitHub
  • OutputMark the tracking row as cleaned upPostgreSQLPostgres

What it does

Instead of scanning everything weekly, this workflow reacts the instant a flag finishes rolling out. When your feature-flag system posts a webhook saying a flag hit 100%, it stamps the completion date in a tracking table. A companion check 30 days later confirms the flag is still fully on and untouched, then opens the cleanup PR — so each flag gets exactly one timely removal nudge.

When to use it

Use it when you want flag cleanup driven by the actual rollout event rather than a calendar sweep, and when your flag platform can emit webhooks on rollout state changes. It produces less noise than a periodic scan because each flag is evaluated on its own 30-day timer.

How it works

A webhook from the flag platform triggers on a 100% rollout event, and the workflow upserts the flag and completion date into a Postgres tracking table. A scheduled sweep reads rows that crossed the 30-day mark, re-checks current state in Datadog, and for any flag still pinned at 100% with no change it opens a GitHub removal PR and marks the row done.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect DatadogMetrics, traces, log search.
  4. 4
    Connect GitHubRepos, issues, pull requests, actions.
  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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