--- name: figure-legend-gen description: "Generate standardized figure legends for scientific charts and graphs." --- # 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 ```bash python scripts/main.py --input --type [--output ] ``` ### 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 ```bash # 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 ```bash # 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