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Agents365-ai/drawio-skill

Generate draw.io diagrams from natural language — 6 presets, vision self-check + up to 5-round refinement, codebase-to-diagram, 10,000+ official shapes & 321 AI/LLM brand logos. Exports PNG/SVG/PDF/JPG.

★ 3.1K langPython licenseMIT updated2026-06-03

drawio-skill is a tool that converts natural-language descriptions and existing codebases into editable .drawio XML diagrams. It utilizes the draw.io desktop CLI to export these diagrams into PNG, SVG, PDF, or JPG formats. The project supports codebase visualization for Python, JS/TS, Go, and Rust, and includes a self-correction loop to fix layout issues like overlapping labels. It is compatible with the Agent Skills format and integrates with agents such as Claude Code, Cursor, and Copilot.

  • Converts natural language and codebases into .drawio XML diagrams
  • Supports ERD, UML, Sequence, Architecture, ML, and Flowchart presets
  • Includes 10,000+ official shapes and 321 AI/LLM brand logos
full readme from github

drawio-skill — From Text to Professional Diagrams

License: MIT GitHub stars GitHub forks Latest Release Last Commit

SkillsMP ClawHub Claude Code Plugin Agent Skills Discord

English · 中文 · 📖 Online Docs

A skill that turns natural-language descriptions into .drawio XML and exports them to PNG / SVG / PDF / JPG via the native draw.io desktop CLI. It can also turn an existing codebase (Python / JS-TS / Go / Rust) into an auto-laid-out structure diagram. Works with Claude Code, Cursor, Copilot, OpenClaw, Codex, Hermes, and any agent compatible with the Agent Skills format.

Microservices Architecture — generated from a single natural-language prompt

✨ Highlights

  • 6 diagram type presets — ERD, UML Class, Sequence, Architecture, ML/Deep Learning, Flowchart
  • Visualize a codebase — extract and auto-lay-out the structure of a Python / JS-TS / Go / Rust project (import graphs) or a Python class hierarchy — Graphviz placement, transitive reduction, nested module containers
  • Search 10,000+ official shapes — resolve the exact AWS / Azure / GCP / Cisco / Kubernetes / UML / BPMN icon style instead of guessing (no more blank-box shape=mxgraph.* typos)
  • AI / LLM brand logos — 321 logos (OpenAI, Claude, Gemini, Mistral, Llama, Ollama, LangChain…) that draw.io has none of, for LLM-app architecture diagrams
  • Self-check + auto-fix — reads its own PNG output and auto-fixes overlaps, clipped labels, stacked edges, and more (up to 2 rounds)
  • Iterative feedback loop — up to 5 rounds of targeted refinement
  • Style presets — capture your visual style from a .drawio file or image, reuse on demand
  • Clean layout — grid-aligned, spacing scales with diagram size, connectors routed clear of nodes
  • Multi-agent, zero-config — runs from a single SKILL.md; no MCP server, no background daemon (the optional npx installer needs Node, the skill itself does not)

🖼️ Examples

[!TIP] The hero image above was generated from this single prompt:

Create a microservices e-commerce architecture with Mobile/Web/Admin clients,
API Gateway (auth + rate limiting + routing), Auth/User/Order/Product/Payment
services, Kafka message queue, Notification service, and User DB / Order DB /
Product DB / Redis Cache / Stripe API

The skill is designed to route edges cleanly across different topologies, avoiding lines that cross through shapes:

Star topology
Star · 7 nodes
Central message broker with 6 microservices radiating outward, no edge crossings on this example.
Layered flow
Layered · 10 nodes / 4 tiers
E-commerce stack with horizontal and diagonal cross-connections routed via corridors.
Ring cycle
Ring · 8 nodes
CI/CD pipeline with a closed loop and 2 spur branches flowing along the perimeter.

Full walkthrough in docs/USAGE.md.

🚀 Installation

1. Install the draw.io desktop CLI

Platform Command
macOS brew install --cask drawio
Windows Download installer
Linux .deb/.rpm from releases; sudo apt install xvfb for headless

Verify with drawio --version. On WSL2 the CLI is the Windows desktop exe reached via /mnt/c — the skill detects this automatically (see troubleshooting). Full recipes in docs/INSTALL_CLI.md.

2. Install the skill

# Any agent (Claude Code, Cursor, Copilot, ...)
npx skills add Agents365-ai/365-skills -g
# Claude Code plugin marketplace
> /plugin marketplace add Agents365-ai/365-skills
> /plugin install drawio
# Manual install
git clone https://github.com/Agents365-ai/drawio-skill.git \
  ~/.claude/skills/drawio-skill

Also indexed on SkillsMP and ClawHub.

Updating: /plugin update drawio (Claude Code), skills update drawio-skill (SkillsMP), clawhub update drawio-pro-skill (OpenClaw), or git pull for manual installs — see docs/INSTALL_SKILL.md#updates. Release history in CHANGELOG.md.

⚡ Quick Start

After installation, just describe what you want. For example, an ML model:

Draw a Transformer encoder-decoder for machine translation: 6-layer encoder
with self-attention, 6-layer decoder with cross-attention, input embeddings
(batch × 512 × 768), positional encoding, and a final output projection.
Annotate tensor shapes between layers and color-code by layer type.

The skill plans the layout, generates the .drawio XML, exports to your chosen format, self-checks the result, and lets you iterate.

🗺️ Visualize a Codebase

Beyond hand-authored diagrams, the skill turns existing code into structure diagrams — no manual coordinates. Just ask:

"Visualize the module structure of this Python project" · "Draw the class hierarchy of mypackage"

Auto-generated class hierarchy of Python's logging package — modules boxed, inheritance arrows resolved

↑ Python's logging package as a class hierarchy — one command, modules auto-boxed, every inheritance edge resolved.

Under the hood it runs a bundled extractor → auto-layout → validate pipeline:

# Import graph — Python / JS-TS / Go / Rust
python3 scripts/pyimports.py   myproject --group -o graph.json
python3 scripts/jsimports.py   ./src     --group -o graph.json
python3 scripts/goimports.py   ./module  --group -o graph.json
python3 scripts/rustimports.py ./crate   --group -o graph.json

# Python class-inheritance hierarchy
python3 scripts/pyclasses.py   mypackage --group -o graph.json

# any extractor → auto-layout → editable .drawio
python3 scripts/autolayout.py  graph.json -o diagram.drawio
Piece What it does
5 extractors import graphs for Python · JS/TS · Go · Rust, plus Python class inheritance
Auto-layout Graphviz places nodes and routes orthogonal edges around them — removes the manual-coordinate ceiling for large graphs
Transitive reduction drops edges implied by a longer path, turning a dense hairball into a traceable graph (asyncio: 149 → 46 edges)
Nested containers --group boxes modules by sub-package, nested for deep package trees
Deterministic validator validate.py lints the .drawio (dangling edges, duplicate ids, overlaps) before the visual self-check

Layout needs Graphviz (brew install graphviz / apt install graphviz) — optional; everything else works without it. Full format + flag reference in references/autolayout.md.

🧩 Supported Diagram Types

Category Examples Notable features
Architecture microservices, cloud (AWS/GCP/Azure), network topology, deployment Tier-based swimlanes, hub-center strategy
ML / Deep Learning Transformer, CNN, LSTM, GRU Tensor shape annotations, layer-type color coding
Flowcharts business processes, workflows, decision trees, state machines Semantic shapes (parallelogram I/O, diamond decisions)
UML class diagrams, sequence diagrams Inheritance / composition / aggregation arrows; lifelines + activation boxes
Data ER diagrams, data flow diagrams (DFD) Table containers, PK/FK notation
Other org charts, mind maps, wireframes

🔍 Shape Search

Need a real AWS / Azure / GCP / Cisco / Kubernetes / UML / BPMN icon? The skill searches 10,000+ official draw.io shapes for the exact style string — so vendor icons render correctly instead of falling back to a blank box from a guessed shape=mxgraph.* name.

"Add an AWS Lambda wired to an S3 bucket" · "Use the real Kubernetes pod icon"

python3 scripts/shapesearch.py "aws lambda" --limit 5
# → Lambda (77x93)
#   outlineConnect=0;...;shape=mxgraph.aws3.lambda;fillColor=#F58534;...

Serverless AWS architecture built from official draw.io icons resolved by shapesearch.py

↑ A serverless AWS architecture — every icon is the real official draw.io shape resolved by shapesearch.py, not a hand-guessed shape= string.

Covers AWS / Azure / GCP / Cisco / Kubernetes / UML / BPMN / ER / electrical / P&ID and the general shape sets. Hand-writable style cheatsheet + search usage in references/shapes.md.

🤖 AI / LLM Brand Logos

draw.io ships no modern AI/LLM logos, so an LLM-app diagram renders as generic boxes. aiicons.py resolves a brand name to a draw.io image style for any of 321 logos (OpenAI, Claude, Gemini, Mistral, Llama, Cohere, DeepSeek, Qwen, Ollama, LangChain, HuggingFace…) from lobe-icons (MIT).

python3 scripts/aiicons.py "claude" --json      # CDN-referenced (default)
python3 scripts/aiicons.py "openai" --embed     # self-contained data URI

Multi-provider LLM app diagram with real AI brand logos resolved by aiicons.py

↑ A multi-provider LLM app — every brand logo resolved by aiicons.py. Icons are referenced from the unpkg CDN by default (network needed at render time); --embed inlines them for offline use. Logos are trademarks of their owners, used for identification only.

🎨 Style Presets

Capture a visual style once, reuse it everywhere. Three presets are built in — default, corporate, handdrawn — and you can teach the skill your own style from a .drawio file or a flat image:

Draw a microservices architecture using my "corporate" style
Learn my style from ~/diagrams/brand.drawio as "mybrand"

The skill extracts colors, shapes, fonts, and edge style, renders a preview, and only saves the preset after you approve. Full preset-management commands in docs/STYLE_PRESETS.md.

🔄 How it works

Internal workflow

Behind the scenes: check dependencies → plan layout → generate .drawio XML → export draft PNG → self-check + auto-fix (up to 2 rounds) → show to user → 5-round feedback loop until approved → final export.

🆚 Comparison

vs Native Agent (no skill)

Feature Native agent drawio-skill
Self-check after export ✅ reads PNG, auto-fixes 6 issue types
Iterative review loop ❌ manual re-prompt ✅ targeted edits, 5-round safety valve
Diagram type presets ✅ 6 presets (ERD, UML, Seq, Arch, ML, Flow)
Visualize a codebase ✅ import graphs (Py/JS/Go/Rust) + class diagrams
Auto-layout for large graphs ❌ hand-places, overlaps ✅ Graphviz placement, ortho routing, nested containers
Structural validation ✅ deterministic .drawio linter
Official shape search ❌ guesses, blank boxes ✅ exact style for 10k+ AWS/Azure/GCP/UML shapes
AI/LLM brand logos ❌ none ✅ 321 logos (OpenAI/Claude/Gemini/…) via aiicons.py
Grid-aligned layout ✅ 10px snap, routing corridors
Color palette random / inconsistent ✅ 7-color semantic system
Style presets ✅ learn from .drawio file or image

vs Other draw.io Skills & Tools

Feature drawio-skill jgraph/drawio-mcp (official)
stars
bahayonghang/drawio-skills
stars
GBSOSS/ai-drawio
stars
Approach Pure SKILL.md SKILL.md / MCP / Project YAML DSL + CLI (MCP optional) Claude Code plugin
Dependencies draw.io desktop only draw.io desktop draw.io desktop (MCP optional) draw.io plugin + browser
Multi-agent ✅ 6 platforms ❌ Claude apps only ✅ Claude / Gemini / Codex ❌ Claude Code only
Self-check + auto-fix ✅ 2-round (reads PNG) ✅ validation + strict mode ❌ screenshot only
Iterative review ✅ 5-round loop ❌ generate once ✅ 3 workflows
Diagram presets ✅ 6 types ✅ paper-mode classifier
ML/DL diagrams ✅ tensor shapes, layer colors
Color system ✅ 7-color semantic ✅ 6 themes
Official shape search ✅ 10k+ shapes (local) ✅ 10k+ shapes (MCP)
AI/LLM brand logos ✅ 321 (lobe-icons)
Browser fallback ✅ diagrams.net URL (viewer + editable) ❌ inline preview only ✅ via optional MCP ✅ diagrams.net viewer (primary)
Zero-config ✅ copy skills/drawio-skill/ ✅ desktop-only mode ❌ needs plugin install

Full comparison + key-advantages summary in docs/COMPARISON.md (with audit timestamp).

🎯 When to use (and when not to)

Good fit:

  • Polished, precise diagrams — stakeholder decks, architecture, network topology, strict UML, ER diagrams
  • Solid opaque fills, 10,000+ official shapes, branded icons (AWS / Azure / GCP / Cisco / Kubernetes + AI/LLM logos), swimlanes, and custom geometry
  • Anything you'll export to PNG / SVG / PDF and keep editable

Reach for a sibling skill instead when you need:

🔗 Related Skills

Part of the Agents365-ai diagram-skill family — pick the right tool for the job:

Skill Style Best for
excalidraw-skill Hand-drawn / sketchy Whiteboard mockups, informal diagrams
mermaid-skill Text-based, auto-layout README-embeddable, version-control friendly
plantuml-skill UML-focused Class / sequence diagrams in CI pipelines
tldraw-skill Whiteboard collaboration Casual sketches, FigJam-style boards

💬 Community

WeChat Community Group

❤️ Support

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👤 Author

Agents365-ai

📄 License

MIT