I'll start by loading the relevant skills and understanding the data.
Now let me load the semantic-sql skill and the slide reference for the report pipeline
Good. I have a clear picture of the data and the workflow. Let me now explore the data to understand the exam scores comprehensively.
Phase 1 — Explore: I'll query the data to understand distributions, top performers, class performance, subject performance, and gender differences
Good, I have comprehensive data. Now I'll proceed to Phase 2 — generate the charts. Let me create the charts that will go into the PPT.
Since the data is from an uploaded file (not a Cube semantic model with measures), I need to use save_csv_chart. Let me load the data and create all charts.
All 6 charts are generated. Now let me write the summary and outline, then proceed with the report pipeline.
Now I need to select a template for the presentation. Let me ask the user
The user selected the "pulse" template. Let me now proceed with the report pipeline.
Guideline succeeded. Now I'll immediately proceed to generate the presentation.
Bayes: The PPT report is complete! Here's a summary of what was delivered:
✅ High School Exam Score Analysis Report
File: High_School_Exam_Score_Analysis.pres (9 slides)
Report Structure
| Page | Topic |
|---|---|
| 1 | Cover |
| 2 | Executive Summary — 500 students, 12 classes, avg score 492.1 |
| 3 | Score Range Distribution — 48.6% of students in 450-499 range |
| 4 | Class Performance Ranking — Class 6 leads (500.6), Class 5 trails (477.6) |
| 5 | Subject-Level Performance — Chinese strongest (104.2), History weakest (59.6) |
| 6 | Top vs Bottom Classes — Mathematics (+6.1) is the biggest differentiator |
| 7 | Gender Comparison — Males slightly ahead (493.6 vs 490.7), biggest gap in English |
| 8 | Elective Combinations — Chemistry+Politics+Biology has highest avg (537.6) |
| 9 | Recommendations — Focus on Math improvement, targeted History/Biology support |
Key Takeaways
- Overall average: 492.1 / 650 — moderate performance with significant room for improvement
- Mathematics is the decisive subject separating top from bottom classes (+6.1 pts gap)
- Gender gap is modest (2.9 pts), with males leading in English/Physics and females in History/Politics
- Elective choice matters — the best combination averages 82 pts higher than the worst
Let me load the dashboard skill to build an interactive BI dashboard.
Now let me load the design template and references.
Now let me explore the available data source to understand the cube structure for the dashboard
Now let me get the full explore details to see all field IDs properly
I have all the field IDs. Now let me write the design doc and then create the chart JSON files for the dashboard. Let me first check what chart files we already have from the analysis.
Now I need to create all the chart JSON files. Let me load the embed-chart skill to understand the correct chart JSON format.
Let me look at the sample chart files to get the exact JSON structure
Now I have all the reference samples. Let me use save_embed_chart for the dashboard charts since the cube is accessible. Let me check what chart files already exist from the analysis
The analysis charts are on disk. But for the dashboard, I need charts/<slug>.json format (Lightdash embed JSON). Let me use save_embed_chart to generate the proper chart JSON files for the dashboard, then compose the dashboard.
Let me create the 8 chart files needed for the dashboard using save_embed_chart:
The field name mapping is different from the SQL alias. Let me check the actual field names from the query results
The field names look correct - the alias is matching. The issue was the selectedField. Let me check what the chinese_avg etc. get mapped to in the chart. Since big_number needs the fieldId from the cube, let me check the metric query conversion.
Let me now write all chart JSON files directly using write_file (the embed-chart fallback approach), which gives me more control over the field IDs.
Good - 5 charts created via save_embed_chart. Now I need to create the remaining charts (subject averages and electives) using write_file since they need a different data shape. Let me also check what was generated
Now I need to create the remaining 3 charts. Let me use write_file for the subject averages (which need a different data approach) and the elective combinations pie chart.
Let me check what the cube's field IDs look like in the generated chart JSON:
Now I see the cube ID is 46f02304-7b3d-4f7e-9378-341214e6ffc0. Let me create the remaining chart (subject averages) using write_file as a cartesian bar chart. I'll also need to create the dashboard JSON file.
Now let me also create the top vs bottom classes chart
Now let me check the chart files we have
All 8 charts are ready. Now let me write the dashboard JSON file
The dashboard is verified. Let me report the deliverables.
Bayes:
✅ Interactive Dashboard Delivered
The dashboard dashboards/exam_score_dashboard.json has been created and verified. Here's what it includes:
Dashboard Structure (Single Page, No Tabs)
| Section | Content |
|---|---|
| 📊 Overview | 3 KPI tiles (Avg Score: 492.1, Students: 500, Highest: 617) + insights markdown |
| 🏫 Class Performance | Class ranking bar chart + Top 3 vs Bottom 3 classes subject comparison |
| 📚 Subject Performance | Subject average scores + Gender comparison by subject |
| 📋 Elective Analysis | Top 10 elective combinations pie chart + Key insights & recommendations |
Interactive Features
- Filter by Class — select any class to focus on specific groups
- Filter by Gender — compare male vs female performance
- 8 charts total covering all key metrics from the analysis
Key Charts Included
- KPI: Average Total Score — 492.1
- KPI: Total Students — 500
- KPI: Highest Score — 617
- Class Average Total Score — ranked bar chart
- Top 3 vs Bottom 3 Classes — subject comparison
- Subject Average Scores — multi-metric comparison
- Gender Comparison by Subject — male vs female breakdown
- Top Elective Combinations — distribution pie chart
