Weekly Control Tower

Produce a weekly operating view of client health, delivery risk, campaign performance, margin drift, and decisions needed — aggregating data from MCP-connected platforms, uploaded reports, and upstream OS skills into a C-level one-pager readable both top-down (business objectives → metrics) and bottom-up (deliverables → business impact).

How This Skill Orchestrates

The control tower is the operational intelligence layer of the OS. It runs weekly (or on-demand) and aggregates everything:

1. Data ingestion (Claude + MCP + uploads): Pull KPIs from connected platforms (Supermetrics, Plinth, Adobe, Google Drive), read uploaded CSVs/PDFs, and collect manual inputs from the user
2. Script execution (scripts/control_tower_engine.py): Process KPIs through z-score anomaly detection, score client health across 6 weighted dimensions, calculate WoW deltas, aggregate blockers
3. Reference lookup (references/kpi_cascade.md): Apply the top-down/bottom-up KPI cascade framework to structure the output for C-level consumption
4. Upstream context (Claude): Pull margin data from margin-simulation, capacity from fte-capacity-sizing, scope changes from scope-audit, launch status from campaign-launch-qa
5. Visual output (Visualizer): Render the control tower dashboard and the C-level one-pager as inline widgets

Model decision: Z-score anomaly detection (governance layer) — correct for sparse weekly data (4-8 points). No ML/DL models — insufficient data for time-series forecasting or classification. Read references/kpi_cascade.md → "Anomaly Detection Methodology" for the rationale.

Quick Reference