Create 2d Composition
Skill ActiveCompose 2D graphics programmatically using SVG generation, diagram layout algorithms, image compositing, and batch processing workflows. Use when generating diagrams, flowcharts, or infographics programmatically, creating reproducible scientific figures, automating production of badges or visual assets, building custom chart types not in standard libraries, or batch generating graphics with parameter variations.
To programmatically compose 2D graphics using SVG generation, layout algorithms, image compositing, and batch processing, useful for generating diagrams, scientific figures, and automated visual assets.
Features
- Programmatic SVG generation
- Diagram layout algorithms
- Image compositing and manipulation
- Batch processing of graphics
- Conversion to PNG and PDF
Use Cases
- Generating diagrams, flowcharts, or infographics programmatically
- Creating reproducible scientific figures
- Automating production of badges or visual assets
- Batch generating graphics with parameter variations
Non-Goals
- Complex 3D scene generation
- Real-time interactive graphics rendering
- Direct manipulation of graphical user interfaces
Documentation
- info:Configuration & parameter referenceInput parameters are described in a table, but default values and precedence order are not explicitly detailed.
Trust
- warning:Issues AttentionOpen issues: 9, Closed issues (last 90d): 195. The closure rate is high (195 / (9+195) ≈ 95.6%), but the number of open issues (9) is not low, suggesting potentially slower response to new issues despite a high historical closure rate.
Code Execution
- info:ValidationInput parameters are described, and some functions include basic comments about expected inputs, but there is no explicit schema validation library used in the provided code snippets.
- info:Error HandlingSome functions have comments indicating expected behavior on failure (e.g., checking Python version, directory writability), but explicit error handling with structured fields is not demonstrated in the snippets.
Errors
- info:Actionable error messagesThe SKILL.md mentions failure modes and expected recovery steps in a general sense, but specific error messages within the Python code are not detailed.
Practical Utility
- info:Edge casesThe SKILL.md mentions handling edge cases like empty data or negative values in comments, but explicit documentation of failure modes with symptoms and recovery steps is limited.
Installation
/plugin install agent-almanac@pjt222-agent-almanacQuality Score
Trust Signals
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