它通过强制使用本地预览、截图审查和缺陷日志的可重复工作流程，解决了 LLM 生成的 draw.io XML 常见问题（文本溢出、箭头错误、图标缺失），并提供了捆绑的图标资源和验证脚本。
A portable agent skill for producing publication-style, editable diagrams.net / draw.io figures from papers, prompts, codebases, project context, screenshots, and one or more visual references.
npx skills add Will-hxw/drawio-diagram-builder-skill
Copy this prompt into Codex, Claude Code, or another local coding agent:
Install and test the drawio-diagram-builder skill from:
https://github.com/Will-hxw/drawio-diagram-builder-skill
After installing, run its smoke test and tell me the exact skill path.
After installation, ask the agent to run the bundled update check against its installed skill path:
python \scripts\check_skill_update.py
| Requirement | Why |
|---|---|
| Python 3 (3.7+) | Preview and validation scripts |
| Browser automation (Playwright MCP, Puppeteer, browser tools, etc.) | Screenshot feedback loop — the skill is evidence-driven |
| Internet access | Preview loads https://embed.diagrams.net/ |
Without browser automation the agent can still generate .drawio XML, but cannot visually verify the result. The iterative refinement loop is the skill's main value.
LLMs can write draw.io XML, but the first result is usually not right:
This skill gives the agent a repeatable workflow: synthesize the user's text and image inputs into a diagram brief → create editable XML → preview through a local URL (not a giant encoded one) → screenshot → self-review visible and semantic defects → fix → repeat → validate.
For reference-image replication, the skill now enforces a stricter protocol: the agent must write a visual spec, coordinate layout grid, asset ledger, and defect log before drawing. Final handoff must include a screenshot-reviewed defect log, not just valid XML.
For prompt, paper, codebase, or mixed-input diagrams, the skill now also requires a self-supervision protocol: classify each input as content, structure, style, layout, or asset evidence; define connector semantics before drawing arro
Portable agent skill for research-style editable draw.io diagrams and screenshot-driven refinement
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