Generate SVG+PNG technical diagrams from natural language descriptions.
npx skills add https://github.com/duy-tung/claude-code-setup --skill ck-tech-graph
Please help me install this Agent Skill. Skill: ck:tech-graph Source: https://github.com/duy-tung/claude-code-setup/tree/main/claude/skills/tech-graph Command: npx skills add https://github.com/duy-tung/claude-code-setup --skill ck-tech-graph
This Skill generates high-quality SVG+PNG technical diagrams from natural language descriptions, automating the creation of architecture, data flow, flowchart, sequence, agent/memory, or concept maps.
Use the ck:tech-graph skill to generate a diagram. For example: "Generate a microservices architecture diagram in style 2 (Dark Terminal)".
High-intent search queries and answers about installing and using this skill.
To generate technical diagrams from natural language, you describe the architecture or data flow in plain text. The system processes this input to output production-quality SVG and PNG files, supporting 14 diagram types including UML, sequence, and flowcharts.
Yes, you can use Python to automatically create UML diagrams for AI agent systems. The tool requires Python along with pypdf, pdfplumber, and librsvg, providing built-in patterns for RAG, Multi-Agent, and Tool Call visualizations.
Automated system visualization supports 7 visual styles including Dark Terminal, Blueprint, Glassmorphism, Notion Clean, Flat Icon, Claude Official, and OpenAI Official. These styles apply consistent semantic shape vocabularies across various diagram types.
Yes, generating SVG and PNG architecture diagrams requires external rendering libraries. The environment must include Python, pypdf, pdfplumber, and librsvg to process the semantic shape vocabulary and render the final visual outputs.
The best way to document microservices architecture using AI is generating editable SVG and embeddable PNG files directly from text descriptions. This approach automates architectural design visualization without manual diagramming tools.
You should use natural language diagram generation instead of manual drawing tools when creating production-quality technical documentation for AI/ML products. It automates complex system visualization, ensuring consistent semantic shapes across 14 diagram types.
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