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Skills figure-legend-gen

Generate standardized figure legends for scientific charts and graphs.

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/figure-legend-gen" ~/.claude/skills/clawdbot-skills-figure-legend-gen && rm -rf "$T"
manifest: skills/aipoch-ai/figure-legend-gen/SKILL.md
source content

Figure Legend Generator

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

Supported Chart Types

Chart TypeDescription
Bar ChartCompare values across categories
Line GraphShow trends over time or continuous data
Scatter PlotDisplay relationships between variables
Box PlotShow distribution and outliers
HeatmapDisplay matrix data intensity
MicroscopyFluorescence/confocal images
Flow CytometryFACS plots and histograms
Western BlotProtein expression bands

Usage

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

Parameters

ParameterRequiredDescription
--input
YesPath to chart image
--type
YesChart type (bar/line/scatter/box/heatmap/microscopy/flow/western)
--output
NoOutput path for legend text (default: stdout)
--format
NoOutput format (text/markdown/latex), default: markdown
--language
NoLanguage (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 IndicatorAssessmentLevel
Code ExecutionPython scripts with toolsHigh
Network AccessExternal API callsHigh
File System AccessRead/write dataMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureData handled securelyMedium

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