TICKET MANAGEMENT
BigQuery-Backed Spillover Prediction
Joins live ClickUp sprint state with historical velocity from BigQuery to predict slip likelihood per ticket using each owner's real throughput.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule triggers prediction run
- ActionFetch open tickets from ClickUpClickUp
- ActionQuery historical throughput from BigQueryBigQuery
- LogicJoin live tickets to history and score slip risk
- ActionWrite scored results to BigQueryBigQuery
- OutputPost high-risk summary to SlackSlack
What it does
This workflow grounds its forecast in history rather than the current sprint alone. It pulls each owner's and ticket-type's true historical throughput from a BigQuery warehouse, joins it with the live ClickUp board, and scores every open ticket on the odds it ships on time.
When to use it
Use it when you already warehouse sprint history in BigQuery and want predictions calibrated to real past velocity instead of naive linear burn-down. Best for data-mature teams that distrust simple projections.
How it works
- 1A schedule triggers the prediction run mid-sprint.
- 2Live open tickets are fetched from ClickUp.
- 3A BigQuery query returns historical close-rate per owner and per ticket type.
- 4A logic step joins live tickets to historical rates and computes a slip score for each.
- 5Scored results are written back to a BigQuery table for trend tracking.
- 6A summary of high-risk tickets is posted to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect ClickUpDocs + tasks + chats in one workspace.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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