I'll start by loading the relevant skills and understanding the data structure
I have a comprehensive dataset about diet habits and obesity. Let me now load the semantic SQL skill and the slide reference, then explore the data.
Excellent! I have all the context I need. Let me now explore the data thoroughly in Phase 1.
Excellent data! Now let me create the charts for the PPT report. I'll generate them in Phase 2.
I see - I need to use the member short names as aliases. Let me fix the SQL aliases.
All 10 charts generated successfully. Now let me write the summary and outline, then proceed with the PPT pipeline.
Now I need to select a template before proceeding with the report pipeline.
The user chose the "clarity" template. Let me proceed with the report pipeline.
Guideline succeeded. Now proceeding to generate the presentation
Bayes: Your Diet Habits and Obesity Risk Analysis report is ready! Here's a summary of what was delivered:
📊 Report: 12 Slides
Key findings covered:
| Topic | Key Insight |
|---|---|
| Obesity Prevalence | 56% of the population is above healthy weight (45.2% overweight, 10.6% obese) |
| Fast Food | Daily consumers have BMI 27.84 vs 23.85 for those who never eat it — a 4.0 point gap |
| Sugary Drinks | Multiple daily intake → BMI 28.20 vs 23.32 for none — 4.9 point gap |
| Vegetable Intake | No vegetables → BMI 27.95 vs 24.19 with high intake |
| Physical Activity | Strongest factor: Sedentary BMI 28.74 vs Very Active 22.83 — a 5.9 point gap |
| Age | Obese individuals average 49.6 years old vs 40.0 for normal weight |
| Gender | Males 55% overweight; Females 55.9% normal weight |
The file Diet_Habits_and_Obesity_Risk_Analysis.pres is ready for you to download and view.
I'll build a dashboard based on the analysis. Let me load the dashboard skill first
Good. Let me get the explore details for the anchor cube
Now let me check what chart files already exist from the analysis. I also need to create some additional charts for the dashboard (like a pie chart for obesity status share, and a big number KPI tile). Let me first check what's on disk and then create the dashboard design doc and new charts.
Now I need to create the new charts (big number KPIs and pie chart) that don't exist yet. Let me create them using save_embed_chart.
Now let me read the embed-chart skill to understand the chart JSON format, then write the dashboard JSON
Since the charts were already created via save_embed_chart (chart_spec flow), they already exist as charts/<slug>.json files. Now I need to check what charts are available, then write the dashboard JSON.
The charts I need are already created via save_embed_chart, so they're already on disk as charts/<slug>.json. Now I need to write the dashboard JSON. Let me compose it using the correct field IDs from the explore
The pie chart tile is starting at the same y as the KPIs. Let me fix the layout so KPIs are on their own row and charts start after them.
Bayes: The interactive dashboard is ready! Here's what was built:
📊 Dashboard: Diet Habits & Obesity Risk Analysis
3 sections across one scrollable page with 9 charts and 2 global filters:
Section 1 — Overview
- KPI tiles: Total Records (500) and Average BMI (25.57)
- Pie chart: Obesity status distribution — 56% above healthy weight
Section 2 — Dietary Impact on BMI
- Fast Food vs BMI — 4.0 point gap between daily and never
- Sugary Drinks vs BMI — 4.9 point gap between extremes
- Vegetable Intake vs BMI — protective effect (3.76 point gap)
- Gender × Obesity cross analysis — males more overweight
Section 3 — Lifestyle & Demographics
- Physical Activity vs BMI — strongest factor (5.9 point gap)
- Age by Obesity Status — obese avg 49.6 vs normal 40.0
Filters
- Gender and Obesity Status filters apply to all charts for interactive exploration
The dashboard file diet_habits_obesity_dashboard.json is ready for you to view.
