MARKETING

Quarterly UTM Registry Cleanup Agent

An agent that quarterly cross-references the Coda naming registry against actual UTM usage in BigQuery, proposes retiring dead values, merging duplicates, and documenting orphan…

CategoryMarketing
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerQuarterly schedule kicks off the cleanup agent
  • ActionPull full registry from Coda and usage from BigQueryGoogle BigQueryBigQuery
  • LogicClassify each value as active, dead, duplicate, or orphan
  • ActionPost retire/merge/adopt recommendations to Slack for sign-offSlack
  • OutputApply approved edits back to the Coda registryCodaCoda

What it does

Keeps the registry itself honest over time. The agent compares what the registry says is allowed against what's actually being used, then reasons about the gaps: values defined but never used, near-duplicate entries that should merge, and live tags that exist in traffic but never made it into the registry. It writes up a tidy set of recommendations rather than acting blindly.

When to use it

Run it once a quarter when registry rot has set in and nobody remembers why half the entries exist. It does the tedious archaeology of reconciling intent against reality so a human only has to approve the conclusions.

How it works

  1. 1A scheduled quarterly trigger starts the agent.
  2. 2It pulls the full registry from Coda and a quarter of distinct UTM usage from BigQuery.
  3. 3The agent reasons over both sets to classify each value as active, dead, duplicate, or orphan.
  4. 4It drafts specific recommendations: retire, merge, or formally adopt.
  5. 5The proposal is posted to Slack for sign-off, and on approval the agent applies the agreed edits back to the Coda registry.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect CodaDocs, packs, automations.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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