SkillHub

figure-legend-gen

v1.0.2

Generate standardized figure legends for scientific charts and graphs.

Sourced from ClawHub, Authored by AIpoch

Installation

Please help me install the skill `figure-legend-gen` from SkillHub official store. npx skills add AIPOCH-AI/figure-legend-gen

Figure Legend Generator

Generate publication-quality figure legends for scientific research charts and images.

Supported Chart Types

Chart Type Description
Bar Chart Compare values across categories
Line Graph Show trends over time or continuous data
Scatter Plot Display relationships between variables
Box Plot Show distribution and outliers
Heatmap Display matrix data intensity
Microscopy Fluorescence/confocal images
Flow Cytometry FACS plots and histograms
Western Blot Protein expression bands

Usage

python scripts/main.py --input <image_path> --type <chart_type> [--output <output_path>]

Parameters

Parameter Required Description
--input Yes Path to chart image
--type Yes Chart type (bar/line/scatter/box/heatmap/microscopy/flow/western)
--output No Output path for legend text (default: stdout)
--format No Output format (text/markdown/latex), default: markdown
--language No Language (en/zh), default: en

Examples

# Generate legend for bar chart
python scripts/main.py --input figure1.png --type bar

# Save to file
python scripts/main.py --input plot.jpg --type line --output legend.md

# Chinese output
python scripts/main.py --image.png --type scatter --language zh

Legend Structure

Generated legends follow academic standards:

  1. Figure Number - Sequential numbering
  2. Brief Title - Concise description
  3. Main Description - What the figure shows
  4. Data Details - Key statistics/measurements
  5. Methodology - Brief experimental context
  6. Statistics - P-values, significance markers
  7. Scale Bars - For microscopy images

Technical Notes

  • Difficulty: Low
  • Dependencies: PIL, pytesseract (optional OCR)
  • Processing: Vision analysis for chart type detection
  • Output: Structured markdown by default

References

  • references/legend_templates.md - Templates by chart type
  • references/academic_style_guide.md - Formatting guidelines

Risk Assessment

Risk Indicator Assessment Level
Code Execution Python scripts with tools High
Network Access External API calls High
File System Access Read/write data Medium
Instruction Tampering Standard prompt guidelines Low
Data Exposure Data handled securely Medium

Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] API requests use HTTPS only
  • [ ] Input validated against allowed patterns
  • [ ] API timeout and retry mechanisms implemented
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no internal paths exposed)
  • [ ] Dependencies audited
  • [ ] No exposure of internal service architecture

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
  • Performance optimization
  • Additional feature support