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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 来源安装数☆ 0 仓库 Starsv1.0.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install aipoch-ai/figure-legend-gen

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

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查看来源 ↗使用指南

技能说明

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...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/aipoch-ai/figure-legend-gen
Version
1.0.0
Commit
010ee3b1528fbdf2cc3aa78b818d73164063d088
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

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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