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name: figure-legend-gen description: 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, not visualizations. version: 1.0.0 category: Research tags: [] author: AIPOCH license: MIT status: Draft risk_level: High skill_type: Hybrid (Tool/Script + Network/API) owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

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