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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, not visualizations.

aipoch-ai0 source installs☆ 0 Repository starsv1.0.0

Installation

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Skill instructions

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...

Source information

Source
github
Repository
openclaw/skills
Path
skills/aipoch-ai/figure-legend-gen
Version
1.0.0
Commit
010ee3b1528fbdf2cc3aa78b818d73164063d088
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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 <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

```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...

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