Writing Better Playbook Prompts

Objective: Daily product usage summary for the operations team. Run every weekday morning.

Steps:
1. Pull the following metrics for yesterday (use the most recent complete day if yesterday's data is not yet available):
   - Total messages sent (from the messages table, exclude internal users)
   - Daily active users (from user_activity, exclude internal users)
   - New signups (from accounts, created_at = yesterday)
2. Compare each metric to the same day last week and calculate the day-over-day change.
3. Flag any metric that changed more than 20% in either direction.

Output:
- One-paragraph summary highlighting the most notable changes
- A table with: metric name, yesterday's value, same day last week, % change, flag (yes/no)
- Keep the tone factual and brief. This is an internal ops report, not an executive summary.

Date handling: Use Eastern Time. If yesterday's data is incomplete, use the most recent complete day and note the date at the top of the report.

Objective: Weekly executive summary of key business metrics. Sent every Monday morning covering the prior week (Monday through Sunday).

Steps:
1. Pull the following metrics for the prior full week:
   - Revenue (from the orders table, sum of order_value where status = 'completed')
   - New customers (from accounts, created_at in the prior week)
   - Churn (from subscriptions, cancelled_at in the prior week)
   - Net revenue retention (current week revenue from accounts that existed 4 weeks ago / revenue from those same accounts 4 weeks ago)
2. Compare each metric to the prior week and to the same week last year.
3. Identify the top 3 revenue-generating customer segments for the week.

Output:
- Executive summary paragraph (3–5 sentences, written for a non-technical audience)
- KPI table: metric, this week, prior week, WoW change, same week last year, YoY change
- Bar chart: revenue by customer segment, top 10 segments, current week vs. prior week
- Close with one sentence on what to watch next week based on the trends.

Tone: Confident and direct. Assume the reader has 2 minutes and wants the headline first.

Objective: Detect and surface unusual patterns in product usage data. Run daily.

Steps:
1. For each of the following metrics, calculate the value for yesterday and compare it to the 14-day rolling average:
   - Daily active users
   - Messages sent per user
   - Error rate (from the errors table, errors / total requests)
   - Average session duration
2. Flag any metric where yesterday's value is more than 2 standard deviations from the 14-day mean.
3. For flagged metrics, pull the daily trend for the past 14 days so the reader can see the pattern.

Output:
- If no anomalies: one sentence confirming all metrics are within normal range.
- If anomalies exist: for each flagged metric, show (1) the current value, (2) the 14-day average, (3) the deviation, and (4) a line chart of the past 14 days.
- Order anomalies by severity (largest deviation first).

Edge cases: If a metric has fewer than 7 days of data, skip the anomaly check for that metric and note it. If a metric returns null, note it explicitly.