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Figure Legend Gen

Name: Figure Legend Gen
Author: aipoch-ai

作者 AIpoch · GitHub ↗ · v1.0.2 · MIT-0

cross-platform ⚠ suspicious

454

总下载

当前安装

版本数

在 OpenClaw 中安装

/install figure-legend-gen

功能描述

Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or...

使用说明 (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

python scripts/main.py --input \x3Cimage_path> --type \x3Cchart_type> [--output \x3Coutput_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:

Figure Number - Sequential numbering
Brief Title - Concise description
Main Description - What the figure shows
Data Details - Key statistics/measurements
Methodology - Brief experimental context
Statistics - P-values, significance markers
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

Basic Functionality: Standard input → Expected output
Edge Case: Invalid input → Graceful error handling
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

安全使用建议

Before installing or running this skill: 1) Ask the author to explain why SKILL.md claims network/API usage and a 'High' network risk if the included script appears purely local; confirm there are no external endpoints used. 2) Inspect the full scripts/main.py (the manifest listing was truncated) to verify there are no hidden network calls or code that exfiltrates files. 3) Fix dependency mismatches: requirements.txt does not list Pillow/pytesseract which the README references; ensure required packages are explicit and safe. 4) Run the tool in a sandbox or isolated environment the first time, and do not feed it sensitive or proprietary images until you confirm no external communication occurs. 5) If you need stronger assurance, request a signed provenance or a canonical source (homepage/author repo) and ask for reproducible build/install instructions that do not rely on unreviewed remote downloads.

功能分析

Type: OpenClaw Skill Name: figure-legend-gen Version: 1.0.2 The figure-legend-gen skill is a straightforward utility for generating scientific figure legends based on hardcoded templates. The core logic in scripts/main.py is limited to string formatting and basic file I/O (reading an input path and writing text to an output file). Although the SKILL.md documentation self-labels the risk level as 'High' and mentions 'Network Access,' the provided code contains no network calls, shell execution, or data exfiltration logic. The behavior is entirely consistent with its stated purpose of generating academic text legends.

能力评估

ℹ Purpose & Capability

Name/description match the included script: a local Python tool that generates figure legends from an image and templates. However, SKILL.md and metadata label the skill as 'Hybrid (Tool/Script + Network/API)' and list 'Network Access' as high risk while the provided code (visible portion) contains no network calls. Also SKILL.md names PIL and pytesseract as dependencies but requirements.txt does not include them. These inconsistencies suggest the metadata/README and code are out-of-sync.

ℹ Instruction Scope

Runtime instructions tell the agent to run the local Python script on a provided image path and to install requirements.txt. The script validates and reads local files and writes output; there are no instructions to collect unrelated system data. But SKILL.md contains a 'Network/API' claim and a security checklist referencing HTTPS and external APIs; the instructions do not show what external endpoints would be used. The file listing of main.py was truncated in the package summary; the missing tail could contain network calls — this uncertainty increases risk.

✓ Install Mechanism

No install spec is provided (instruction-only + included script). There are no downloads or external installers in the manifest. This is low-risk from an install-mechanism perspective.

✓ Credentials

The skill declares no required environment variables, no credentials, and no special config paths. The code shown only needs access to the input image and optional output path — proportional to the stated purpose.

✓ Persistence & Privilege

Skill flags indicate normal user-invocable behavior and always:false. The package does not request elevated/system persistence or modifications to other skills. No concern here.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install figure-legend-gen
安装完成后，直接呼叫该 Skill 的名称或使用 /figure-legend-gen 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.2

No user-facing changes in this version. - No file or documentation changes detected. - Functionality and features remain unchanged.

v1.0.1

No user-facing changes in this version. - No file changes detected. - Version remains at 1.0.0 in SKILL.md. - No updates to features, documentation, or functionality.

v1.0.0

Initial release of Figure Legend Generator. - Generates standardized figure legends for scientific charts and images (including bar charts, line graphs, scatter plots, box plots, heatmaps, microscopy, flow cytometry, and western blots). - Supports text output in multiple formats and languages. - Command-line interface for flexible integration in research workflows. - Includes security guidelines and risk assessment. - Provides usage examples, technical notes, and evaluation criteria.

元数据

Slug figure-legend-gen

版本 1.0.2

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 3

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