PROJECT MANAGEMENT
Weekly scope-creep trend report from BigQuery sprint history to Slack
Queries a BigQuery table of sprint snapshots to measure how much scope was added after lock across recent sprints.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule
- ActionAggregate sprint scope history in BigQueryBigQuery
- LogicRank teams and flag worsening trends
- OutputPost trend digest to leadership SlackSlack
What it does
This workflow turns historical sprint data into a trend signal. Instead of reacting to one ticket, it reads accumulated snapshots in BigQuery to show whether scope creep is getting better or worse over time, by team, so leadership can act on the pattern.
When to use it
Use it when individual creep alerts already exist but you need the longitudinal view for retros and staffing decisions. Best for engineering or program managers tracking delivery predictability across multiple squads.
How it works
- 1A weekly schedule triggers the report.
- 2A BigQuery query aggregates committed-vs-final scope per sprint and team over the trailing N sprints, computing the post-lock added-point percentage.
- 3A logic step ranks teams by creep and flags any whose creep rose for two or more consecutive sprints.
- 4The results are formatted into a ranked digest with sparkline-style figures.
- 5The digest is posted to a leadership Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SlackChannels, DMs, threads, mentions.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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