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Playbook Recipes

  • Data Ops recipes
    • Recipe: Data sync and ETL to a warehouse
    • Recipe: Scheduled reports and readouts
  • Engineering Ops recipes
    • Recipe: AI code review on pull requests with Cursor
    • Recipe: Incident response and on-call paging
    • Recipe: Triage new errors into tracked issues
  • Finance Ops recipes
    • Recipe: Invoice and expense processing
    • Recipe: Reconcile payments and payouts
  • Introduction
  • Marketing Ops recipes
    • Recipe: Campaign performance report
    • Recipe: Competitor monitoring digest
    • Recipe: Content and marketing operations
    • Recipe: Generate a content brief
  • RevOps recipes
    • Recipe: Automate new customer onboarding
    • Recipe: Enrich new leads automatically
    • Recipe: Route a deal review with an evaluation agent
    • Recipe: Score and route a lead with an evaluation agent
    • Recipe: Score, route, and follow up on leads
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    • Recipe: Auto-answer and deflect common tickets
    • Recipe: Catch a refund request with an evaluation agent
    • Recipe: Check the work before a reply
    • Recipe: CSAT follow-up after ticket resolution
    • Recipe: Escalate SLA breaches and priority tickets
    • Recipe: Triage a support email with an evaluation agent
    • Recipe: Triage and route support tickets
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Recipe: Scheduled reports and readouts

TL;DR: On a schedule, this playbook queries your data source, summarizes the results into a readout, and delivers it to Slack or email. Trigger is a schedule (e.g. weekday mornings); outputs are a formatted summary and optional chart link.

Who this is for

Data, analytics, and ops teams that send recurring metrics readouts by hand.

What you'll need

  • Connectors: BigQuery (or your warehouse/HTTP data API), Slack and/or email.
  • Secrets: Warehouse/API credentials, Slack token or email API key.
  • Trigger: Schedule (cron) for the cadence you want.

How to build it with Ask Ballet

  1. Open the Console on Home.
  2. Paste the prompt below; set your query, cadence, and recipients.
  3. Review the plan and confirm.
  4. Connect the data source and Slack/email, then set the schedule.
Build a playbook that runs every weekday at 8am. Query BigQuery for yesterday's key
metrics (signups, active users, revenue) and the week-over-week change. Summarize the
results in a short readout with the top movers and any anomalies. Post the summary to
#daily-metrics in Slack and email it to the leadership list. Include a link to the
dashboard.

What the playbook does

  1. Schedule trigger — runs on the configured cron.
  2. HTTP/Code step (query) — runs the query against the warehouse.
  3. Code step (summarize) — computes deltas and writes the readout.
  4. HTTP step (deliver) — posts to Slack and/or sends the email.

Variations and tips

  • Start with a manual run to validate the query and formatting before scheduling.
  • Keep heavy aggregation in the warehouse query; use the Code step for formatting.
  • Feed clean tables from Data sync and ETL so reports stay reliable.

Related articles

  • Data sync and ETL to a warehouse
  • How do I manage Schedules?
  • How do I share run output?
  • How do I edit steps (Code and HTTP)?