feat: implement scatter-basic

[claude]

Summary

• Created specification for basic scatter plot optimized for many points
• Implemented matplotlib and seaborn versions
• Both implementations achieve self-review scores > 90

Implementation Details

Matplotlib Implementation

• Path: plots/matplotlib/scatter/scatter-basic/default.py
• Self-review score: 92/100 (PASS ✅)
• Features:
  • Complete input validation (empty data, missing columns, numeric type checking)
  • Configurable parameters with sensible defaults
  • Optimized alpha value (0.6) for handling many overlapping points
  • Clear error messages with context
  • Full type hints and comprehensive docstring

Seaborn Implementation

• Path: plots/seaborn/scatterplot/scatter-basic/default.py
• Self-review score: 91/100 (PASS ✅)
• Features:
  • Uses seaborn's scatterplot function for consistent styling
  • Same validation and error handling as matplotlib version
  • Maintains compatibility with all specified parameters
  • Clean whitegrid style with subtle grid (alpha=0.3)

Quality Criteria Met

✅ Visual Quality:

• Axes clearly labeled
• Grid visible but subtle (alpha=0.3)
• Points clearly distinguishable with appropriate size and transparency
• No overlapping labels
• Colorblind-safe color (steelblue)
• Professional appearance with 10x6 figure size

✅ Code Quality:

• Complete type hints on all parameters
• Google-style docstrings with Args, Returns, Raises, Example
• Comprehensive input validation
• Clear, contextual error messages
• No hardcoded values - all parameters configurable

✅ Correctness:

• Data accurately represented from specified columns
• All spec requirements implemented
• Edge cases handled (empty data, missing columns, non-numeric data)

Test Plan

• Matplotlib implementation executes without errors
• Seaborn implementation executes without errors
• Both produce visually identical scatter plots with 500 sample points
• Input validation works correctly (tested with empty data, missing columns)
• Generated plots saved as 'plot.png' per workflow requirements
• CI/CD tests will run automatically on PR

Closes #30

🤖 Generated with Claude Code

[github-actions]

@claude

🎯 Task: AI Quality Review (Attempt 1/3)

Tests passed and preview images are ready. Evaluate if the implementation matches the specification.

Your Task

1. Read the spec file: specs/scatter-basic.md

   • Note all quality criteria listed
   • Understand the expected visual output

2. Read the implementation files:

   • plots/matplotlib/*/scatter-basic/default.py
   • plots/seaborn/*/scatter-basic/default.py

3. View the plot images in plot_images/ directory

   • Use your vision capabilities to analyze each image
   • Compare with the spec requirements

4. Evaluate against spec requirements and create a checklist

5. Post your verdict to Issue Plot: scatter mit vielen punkten [scatter-basic] #30 using this format:

## 🔍 Quality Check

### Status: [✅ Approved | ⚠️ Needs Work | ❌ Rejected]

### Spec Requirements
- [x] Requirement 1 from spec
- [x] Requirement 2 from spec
- [ ] Requirement 3 ← Issue here

### Details
{Detailed analysis of what's good and what needs improvement}

### Recommendation
{If not approved: specific actionable steps to fix}

6. Take action based on result:
   • ✅ Approved (all requirements met):
     • Run: gh pr edit 31 --add-label ai-approved
     • Run: gh issue comment 30 --body "...quality check result..."
   • ⚠️ Needs Work (minor issues, attempt < 3):
     • Run: gh pr edit 31 --add-label needs-work
     • Run: gh pr edit 31 --add-label ai-attempt-1
     • Run: gh issue comment 30 --body "...quality check result..."
     • Attempt to fix the issues
   • ❌ Rejected (major issues or attempt 3):
     • Run: gh pr edit 31 --add-label ai-rejected
     • Run: gh issue comment 30 --body "...quality check result..."

Be specific and actionable in your feedback!