figure-legend-gen

// Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or data charts. Supports bar charts, line graphs, scatter plots, box plots, heatmaps, and microscopy images. This tool generates text legends only

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updated:March 4, 2026

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SKILL.md Frontmatter

namefigure-legend-gen

descriptionGenerate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or data charts. Supports bar charts, line graphs, scatter plots, box plots, heatmaps, and microscopy images. This tool generates text legends only, not visualizations.

version1.0.0

categoryResearch

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:

Figure Number - Sequential numbering
Brief Title - Concise description
Main Description - What the figure shows
Data Details - Key statistics/measurements
Methodology - Brief experimental context
Statistics - P-values, significance markers
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

Basic Functionality: Standard input → Expected output
Edge Case: Invalid input → Graceful error handling
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