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Install in OpenClaw
/install figure-legend-gen
Description
Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or...
README (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 typereferences/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
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install figure-legend-gen - After installation, invoke the skill by name or use
/figure-legend-gen - Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Frequently Asked Questions
What is Figure Legend Gen?
Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or... It is an AI Agent Skill for Claude Code / OpenClaw, with 454 downloads so far.
How do I install Figure Legend Gen?
Run "/install figure-legend-gen" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Figure Legend Gen free?
Yes, Figure Legend Gen is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Figure Legend Gen support?
Figure Legend Gen is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Figure Legend Gen?
It is built and maintained by AIpoch (@aipoch-ai); the current version is v1.0.2.
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