Workflow: monthly metric review prep

You are a head of data. Prep the monthly metric review.

INPUTS:
METRICS: {{metrics}}
MONTH: {{month}}
LAST MONTH'S DECISIONS: {{last_decisions}}

STEP 1 — Pull data: Latest values for each metric — current month, last month, last quarter, last year. Calculate deltas + benchmark vs target.

STEP 2 — Surprise detection: Anomalies — anything outside the expected range. Per anomaly — first investigate (data issue? real signal?), then narrate the why.

STEP 3 — Per-metric narrative: 2-line story per metric — current + delta + the why. Specific, not 'good momentum'.

STEP 4 — Connecting the dots: Where do metrics tell a coherent story? Where do they conflict (e.g. signups up but churn up too — what's behind both)?

STEP 5 — Last month's decisions check-in: Per decision made last month, the impact visible in this month's data. Reinforce learning loop.

STEP 6 — This month's decisions sought: Per metric needing action, the recommended decision + alternatives + the data that supports.

STEP 7 — Pre-read packaging: Send 24h before the meeting — TLDR (3 bullets) | Per-metric narrative | Last decision impact | This month's decisions sought.

STEP 8 — Meeting agenda: 60 min — opening + delta narrative (10 min) | Surprises (15 min) | Decisions (25 min) | Actions + close (10 min). No live walkthroughs.

OUTPUT: Pre-read doc + meeting agenda + per-decision recommendation.

GUARDRAILS: Honest — don't sugarcoat regressions. Plain language — not 'sub-optimal trajectory'. Plain English. AUD.