qbr-generator
QBR Generator
Create a quarterly business review that functions like a construction progress review: what was planned, what was delivered, what changed, what value was created, what leaked, what risks remain, and what decisions are required next. Convert 13 weeks of fragmented data into a C-level growth narrative.
How This Skill Orchestrates
This is the terminal synthesis skill of the OS — it consumes outputs from ALL 15 upstream skills to produce the quarterly narrative.
- Upstream context (Claude — PRIMARY): Pull data from every skill used during the quarter — control tower health (13 weeks), margin simulation, scope audit, media routing, CRM journeys, audience segments, creative supply, measurement results, performance diagnoses, executive memos, change orders
- Script execution (
scripts/qbr_builder.py): Plan vs actual comparison, KPI gap analysis, 6-dimension executive scorecard (weighted scoring), learning extraction, next-quarter priority ranking (Impact × Readiness × Urgency) - Reference lookup (
references/qbr_framework.md): QBR outline template (14-section structure), plan vs actual methodology, scorecard framework, upstream context mapping, benchmark search protocol - Reference lookup (
references/quarterly_planning.md): Learning extraction framework (Observation → Insight → Implication), priority scoring rules, standard priorities catalog, QBR-to-action bridge (connects QBR findings to next-quarter skill triggers) - Web search (Claude — MANDATORY): Search for
"[INDUSTRY] [COUNTRY] Q[N] [YEAR]","Kantar Worldpanel [CATEGORY]","Nielsen ad intel [INDUSTRY]"for market context - File generation (Claude): Generate PPTX deck, Excel scorecard, or Word document using respective skills. Default to inline visual + PPTX
- Visual output (Visualizer): Executive scorecard dashboard + plan vs actual table + priority matrix
Model decision: Executive layer → LLM narrative generation + Governance → risk scoring (scorecard). Not ML — the script does structured comparison (plan vs actual) and weighted scoring (scorecard). The LLM generates the narrative that connects data points into a coherent story. The 70/30 split: ~70% AI-operated (data compilation, scorecard calculation, learning extraction, slide generation), ~30% human (strategic interpretation, client relationship framing, next-quarter negotiation).