Score Analysis Publish

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分析班级考试成绩,生成可视化图表和分析报告,支持多次成绩纵向对比和同层次班级横向对比。

Install

openclaw skills install score-analysis

Score Analysis Skill

AI-powered class score analysis tool that generates visualized charts and professional reports. Supports longitudinal comparison across multiple exams and horizontal comparison with peer classes.

Features

  • 📊 Auto-detect Excel/CSV headers (supports merged cells, mixed Chinese/English)
  • 📈 Multi-dimensional analysis (horizontal & vertical comparison)
  • 🎯 Critical student identification (near pass lines)
  • 📉 Subject imbalance diagnosis
  • 📋 Grouped radar charts by student type
  • 📄 Professional Word report with charts
  • 🎨 Customizable school branding

Workflow

1. Data Reading & Validation

  • Auto-detect header structure
  • Extract fields: student ID, name, class, total score, subject scores, rankings
  • Output standardized JSON

2. Data Verification (Required)

After reading data, must send to user for verification before analysis:

Submit verification content:

  • Average score per subject per class
  • Number of students per class
  • Total score average
  • Special notes (absences, anomalies)

⚠️ Analysis can only proceed after user confirmation!

3. Analysis Dimensions

Horizontal Comparison (Peer Classes)

  • Total score average comparison
  • Subject average comparison
  • Score segment distribution
  • Special control line / undergraduate line pass rates
  • Top student distribution

Vertical Comparison (Time Dimension)

  • Class average score trends
  • Pass count changes
  • Student ranking fluctuations
  • Subject score changes

Individual Analysis

  • Student score volatility (stability)
  • Subject imbalance diagnosis
  • Progress/regression attribution
  • Critical student identification (within X points of pass line)

4. Chart Generation

Grouped Radar Charts (by student type)

  • Special control line critical students → radar chart (within 10 points, max 5)
  • Undergraduate line critical students → radar chart (max 5)
  • Subject-imbalanced students → radar chart (highest imbalance index, max 5)

Other Charts

  • Subject average score bar chart
  • Class average comparison chart
  • Score segment distribution chart

5. Report Output

Generate Word format analysis report including:

  • School logo, header/footer
  • Three-line tables (research style)
  • Embedded charts
  • Highlight boxes (emphasize conclusions)

6. Presentation PPT (Optional)

Call pptx-master skill to create presentation PPT.

Subject Score Rules

Default Rules

  • Chinese, Math, English, Physics: Use raw scores
  • Chemistry, Biology, Politics, Geography: Use adjusted scores
  • Total score: Default to adjusted total score

Special Cases

  • When adjusted/raw scores are in same column (e.g., 85(92) format), ask user to confirm
  • If table only has raw scores, use raw scores

Data Processing Rules

Absence/Makeup Handling

  • Marks like /, , , 缓考, 0, blank are treated as absence
  • Absent students excluded from average calculation

Score Lines

  • Special control line, undergraduate line provided by user (may be image)
  • May vary per exam, needs individual confirmation

Peer Classes

  • Specified by user during analysis
  • Must ask if not specified

Student Mobility

  • Transferred students excluded from individual analysis

Output Files

  • *_standardized.json - Standardized data
  • *_analysis.json - Analysis results
  • charts/ - Charts directory
  • *_report.docx - Analysis report
  • *_presentation.pptx - Presentation PPT (optional)

Directory Structure

score-analysis/
  SKILL.md                        # Skill description
  scripts/
    create_template.py            # Template generator
    generate_report_from_template.py  # Report from template
    generate_radar_charts.py      # Grouped radar charts
    generate_report.py            # Direct report generation
  references/
    analysis_framework.md         # Analysis framework
  assets/
    report_template.docx          # Word report template
  examples/
    sample_data.json              # Sample data for testing

Requirements

pip install python-docx matplotlib pandas numpy openpyxl

License

MIT