DEVOPS

Monthly Flag Debt Report: Aging, Owners, and Kill-Date Misses

Compiles a monthly feature-flag debt report from the registry and Datadog usage, publishes it to Confluence, and pings each team in Slack with their overdue-flag count.

CategoryDevOps
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly schedule starts report build
  • ActionRead full flag registry from PostgresPostgreSQLPostgres
  • ActionPull per-flag evaluation counts from DatadogDatadogDatadog
  • LogicAggregate aging and per-team rollups
  • ActionPublish report to ConfluenceConfluenceConfluence
  • OutputNotify each team of overdue count in SlackSlack

What it does

Builds a monthly snapshot of feature-flag debt: total flags, how many are past kill-date, the oldest offenders, and per-team breakdowns. It enriches each flag with recent Datadog evaluation volume, publishes the report to Confluence, and notifies each owning team of its overdue count in Slack.

When to use it

Use this to give engineering leadership a recurring, trended view of flag debt rather than one-off alerts. It's the reporting layer that sits alongside the harvesters that actually open removal MRs, useful for sprint planning and accountability.

How it works

  1. 1A monthly schedule starts the report build.
  2. 2It reads the full flag registry from Postgres including kill-dates, owners, and creation dates.
  3. 3It pulls trailing evaluation counts per flag from Datadog to mark which overdue flags are also low-traffic.
  4. 4A logic step aggregates totals, ages, and per-team rollups.
  5. 5It publishes the formatted report to a Confluence page and posts each team its overdue-flag tally in Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect DatadogMetrics, traces, log search.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  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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