PROJECT MANAGEMENT
Calibrate Asana capacity thresholds from BigQuery velocity history
Weekly, pulls historical completed-task velocity per assignee from BigQuery, recomputes realistic capacity ceilings.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule after sprint boundary
- ActionQuery historical delivered effort per assigneeBigQuery
- LogicCompute trimmed-mean sustainable capacity
- ActionWrite recalibrated capacity to Asana fieldsAsana
- OutputPost threshold-change summary to SlackSlack
What it does
Static capacity numbers go stale. This workflow reads each assignee's actual delivered effort over recent sprints from a BigQuery warehouse table, computes a data-backed sustainable capacity per person, and writes that updated ceiling into Asana so the assignment guards downstream enforce realistic limits instead of guesses.
When to use it
Use it when your overcommit guards rely on capacity numbers that were set once and never revisited. Calibrating from real throughput keeps the thresholds honest as the team's velocity changes.
How it works
- 1A weekly schedule kicks off after the sprint boundary.
- 2The workflow queries BigQuery for completed-task effort per assignee across the trailing sprints.
- 3Logic computes a trimmed-mean sustainable capacity, discarding outlier crunch sprints so the ceiling reflects a healthy pace.
- 4It compares each new value to the current Asana capacity field and keeps only meaningful changes.
- 5It writes the recalibrated capacity into each member's Asana custom field.
- 6A summary of who moved up or down posts to Slack for the lead to review.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect AsanaTasks, projects, milestones — everywhere.
- 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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