Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Figure Legend Gen
Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or...
skill openclawclawhub Free
Generate standardized figure legends for scientific charts and graphs. Trigger when user uploads/requesting legend for research figures, academic papers, or...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- requirements.txt, SKILL.md, scripts/main.py, references/academic_style_guide.md, references/legend_templates.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- AIPOCH-AI
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Figure Legend Generator
Generate publication-quality figure legends for scientific research charts and images.
Supported Chart Types
Chart TypeDescriptionBar ChartCompare values across categoriesLine GraphShow trends over time or continuous dataScatter PlotDisplay relationships between variablesBox PlotShow distribution and outliersHeatmapDisplay matrix data intensityMicroscopyFluorescence/confocal imagesFlow CytometryFACS plots and histogramsWestern BlotProtein expression bands
Usage
python scripts/main.py --input <image_path> --type <chart_type> [--output <output_path>]
Parameters
ParameterRequiredDescription--inputYesPath to chart image--typeYesChart type (bar/line/scatter/box/heatmap/microscopy/flow/western)--outputNoOutput path for legend text (default: stdout)--formatNoOutput format (text/markdown/latex), default: markdown--languageNoLanguage (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 IndicatorAssessmentLevelCode ExecutionPython scripts with toolsHighNetwork AccessExternal API callsHighFile System AccessRead/write dataMediumInstruction TamperingStandard prompt guidelinesLowData ExposureData handled securelyMedium
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
Success Metrics
Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable
Test Cases
Basic Functionality: Standard input โ Expected output Edge Case: Invalid input โ Graceful error handling Performance: Large dataset โ Acceptable processing time
Lifecycle Status
Current Stage: Draft Next Review Date: 2026-03-06 Known Issues: None Planned Improvements: Performance optimization Additional feature support
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Scripts1 Files
- SKILL.md
- references/academic_style_guide.md
- references/legend_templates.md
- scripts/main.py
- requirements.txt