GitHub - Agents365-ai/drawio-skill: Agent skill that turns natural language, code, Terraform/K8s, SQL, OpenAPI, AsyncAPI, Protobuf and Graph

drawio-skill — From Text to Professional Diagrams

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A skill that turns natural language and real system sources into maintainable .drawio architecture models.

Beyond generation and export, it incrementally synchronizes without discarding manual layout, projects multiple views from one model, enforces architecture contracts, queries dependencies, simulates failure propagation, and publishes dependency-free interactive walkthroughs.

Works with Claude Code, Cursor, Copilot, OpenClaw, Codex, Autohand Code, Hermes , and any agent compatible with the Agent Skills format.

✨ Highlights

From a prompt

• Describe it, get an editable .drawio — the skill plans the layout, writes the XML, exports, then self-checks its own PNG and auto-fixes overlaps, clipped labels, and stacked edges (up to 2 rounds), with up to 5 rounds of your feedback

• Mermaid → native .drawio (draw.io ≥ 30) — author 28 standard types as Mermaid text ( mindmap, gantt, timeline, journey, pie, sankey, kanban …) and the CLI converts them into a laid-out, editable .drawio : structure in, layout free

• Whiteboard photo / screenshot → editable diagram — snap a legacy PNG or a physical whiteboard, let vision extract the graph, and raster2drawio.py rebuilds it as a real, editable .drawio honouring the original layout

• 11 diagram type presets — ERD, UML Class, Sequence, C4, Architecture, ML/Deep Learning, Flowchart, SysML, BPMN, Network Topology, Cross-Functional Swimlane

From real sources

- Visualize a codebase — import graphs for Python / JS-TS / Go / Rust and Python class hierarchies, with Graphviz placement, transitive reduction, and nested module containers

- IaC and live infrastructure — Terraform, Kubernetes, and docker-compose configs become diagrams with official AWS / Azure / GCP / K8s icons; snapshot what's actually deployed from terraform show -json , docker inspect , or kubectl get -o json

- Schemas and pipelines — SQL DDL → ER diagram, OpenAPI/Swagger → API diagram coloured by HTTP method, AsyncAPI → event-driven architecture diagram, Protocol Buffers → message/service diagram, GraphQL SDL → entity type diagram, GitHub Actions / GitLab CI → pipeline DAG

- Deterministic engines — sequence diagrams with computed lifelines and activation bars; multi-page C4 models with click-to-drill-down

Keep it true over time

• Architecture digital twin / Diagram IR — separate meaning, provenance, and geometry; project executive, system, deployment, data-flow, and security views from one model

• Incremental sync without losing manual layout — diagramctl sync updates changed nodes/relations while preserving tuned coordinates, styles, and annotations; removals stay reviewable by default

• Diagram-as-Test, in CI — YAML/JSON architecture rules (Internet-to-database access, cycles, orphans, trust boundaries, contrast…) plus an official GitHub Action that enforces them on every PR, and a PR action that renders visual diffs

• Query, review, what-if — query components/owners/paths, spot articulation points and high coupling, simulate failure propagation, publish an accessible Story walkthrough

• Drift and history — colour-coded diffs between two diagrams or two live snapshots; a time-lapse player of how a codebase's architecture grew

Share and restyle

• Repurpose with one command — interactive HTML viewer (pan/zoom/search), PowerPoint deck, animated data-flow SVG, Mermaid or Markdown export, click-through runbook, exec-summary compression

• Restyle and enrich — style presets (yours or built-in dark / corporate /…), bilingual label variants with layout untouched, data-driven heat maps, white-to-metro tubemap mode

• 10,000+ official shapes + 321 AI/LLM logos — resolve exact AWS / Cisco / K8s / UML icon styles instead of guessing, plus brand logos draw.io itself lacks

• One CLI, optional MCP server — diagramctl doctor/build/sync/views/query/test/review/whatif/story/publish/transform , core workflows stdlib-only and offline; the MCP server exposes them to Claude Desktop, Cursor, VS Code, Codex, and any MCP host. Portable to any Agent Skills-compatible agent, no daemon

🗺️ Feature Map

A bird's-eye view of everything the skill does — diagram types, import sources, layout engines, styling, export formats, and repurposing — in one map. Fittingly, this map was itself drawn with drawio-skill.

🚀 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 . Version ≥ 30 recommended — it unlocks Mermaid → .drawio conversion and the ELK --layout pass (both unavailable on ≤ 29).

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 .

1. Install the skill

Any agent (Claude Code, Cursor, Copilot, ...)

npx skills add Agents365-ai/drawio-skill -g

Manual install

git clone https://github.com/Agents365-ai/drawio-skill.git \

~ /.claude/skills/drawio-skill

Autohand Code global install

git clone https://github.com/Agents365-ai/drawio-skill.git \

~ /.autohand/skills/drawio-skill

Autohand Code project-level install

git clone https://github.com/Agents365-ai/drawio-skill.git \

.autohand/skills/drawio-skill

Autohand Code also supports autohand --skill-install for cataloged skills, with --project for workspace-level installs. Until this skill is listed there, use the direct clone path above.

Also indexed on SkillsMP .

Updating: skills update drawio-skill (SkillsMP) 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.

🖼️ Examples

Tip The diagram 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 maintained Architecture Studio showcase covers code → IR → .drawio , conflict-aware synchronization that preserves a manually tuned layout, and architecture → policy/views/what-if/accessible Story. Every artifact is regenerated by one script and verified in the test suite.

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

Star · 7 nodes

Central message broker with 6 microservices radiating outward, no edge crossings on this example.

Layered · 10 nodes / 4 tiers

E-commerce stack with horizontal and diagonal cross-connections routed via corridors.

Ring · 8 nodes

CI/CD pipeline with a closed loop and 2 spur branches flowing along the perimeter.

It also speaks Mermaid — standard types (flowchart, mindmap, kanban , gitGraph, timeline…) convert straight to native, editable .drawio . Here's a Kanban board (this project's own roadmap) generated from a few lines of Mermaid:

Tube-Map Mode restyles a pipeline or journey as a London-Underground-style metro map — coloured lines, octilinear (H/V/45°) routing, and white interchange circles. Here's the skill's own flow (this map is assets/tubemap.json , ~20 lines):

Full walkthrough in docs/USAGE.md .

🗺️ From Real Sources to Diagrams

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

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

↑ 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:

source -> graph JSON -> placed, editable .drawio

python3 scripts/tfimports.py ./infra -o graph.json # Terraform -> official AWS icons

python3 scripts/autolayout.py graph.json -o architecture.drawio

drift between two states, then share as one interactive file

python3 scripts/drawiodiff.py v1.drawio v2.drawio -o drift.json

python3 scripts/drawiohtml.py architecture.drawio -o architecture.html

The full toolbox, grouped by stage:

Stage Tools

Import

16 extractors

Python · JS/TS · Go · Rust import graphs, Python class inheritance , Terraform / Kubernetes / docker-compose with official cloud icons, live infra from terraform show -json / docker inspect / kubectl get -o json , SQL DDL → ERD , OpenAPI → API diagram (coloured by HTTP method), AsyncAPI → event-driven architecture diagram , Protobuf → message/service diagram , GraphQL SDL → entity type diagram , GitHub Actions + GitLab CI → DAG

Compare & evolve drawiodiff.py colour-codes drift between two diagrams or two live snapshots (added=green, removed=red, changed=orange); timelapse.py replays git history as an HTML player; prdiff.py renders PR diffs in CI

Repurpose explain.py → Markdown, drawiohtml.py → pan/zoom/search HTML viewer, drawio2pptx.py → deck, svgflow.py → animated SVG, drawio2mermaid.py → diagrams-as-code, runbook.py → clickable triage app, compress.py → exec summary with drill-down, buildup.

py → self-drawing player, tubemap.py → metro map

Restyle & enrich restyle.py applies presets by hue remap, relabel.py produces translated twins with layout untouched, heatmap.py shades nodes from a metrics CSV/JSON, edgeports.py un-stacks edges at shape boundaries

Layout & lint autolayout.py (Graphviz placement, orthogonal routing, --tune direction picking, --group containers, transitive reduction: asyncio 149 → 46 edges), seqlayout.py , c4.py , and the deterministic validate.py linter ( --score / --strict )

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

Regenerate, validate ( --strict gate) and render headlessly in CI: docs/CI.md .

🧩 Supported Diagram Types

Category Examples Notable features

Architecture microservices, cloud (AWS/GCP/Azure), network topology, deployment Tier-based swimlanes, hub-center strategy

C4 model system context, containers, components Multi-page .drawio , click-to-drill-down links

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

SysML / MBSE block definition (bdd), internal block (ibd), requirement (req), parametric (par) «block» / «requirement» compartments, satisfy/derive/verify edges, native mxgraph.sysml.* ports & flows

BPMN business processes, pools & lanes Native mxgraph.bpmn.* events/tasks/gateways, sequence vs message flows

Network topology LAN/WAN, subnets, DMZ mxgraph.networks.* device shapes, zone containers, link labels; Cisco/rack via shape search

Cross-functional swimlane who-does-what processes, handoffs Pool + role lanes, flowchart vocabulary, orthogonal handoff edges

Data ER diagrams, data flow diagrams (DFD) Table containers, PK/FK notation

Mermaid-authored mind maps, gantt, timeline, journey, pie, sankey, kanban + 20 more Native CLI conversion (≥ v30) — structure only, layout free

Other org charts, 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;...

↑ 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), plus 18 data-store brands (Redis, Postgres, MongoDB, Qdrant, Milvus, Supabase…) via simple-icons (CC0) for RAG stacks.

python3 scripts/aiicons.py " claude " --json # CDN-referenced (default)

python3 scripts/aiicons.py " openai " --embed # self-contained data URI

↑ 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. Five presets are built in — default , corporate , handdrawn , colorblind-safe (Okabe-Ito palette), dark — 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

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 Other draw.io Skills & Tools

Feature drawio-skill jgraph/drawio-mcp (official)

bahayonghang/drawio-skills

GBSOSS/ai-drawio

Approach Pure SKILL.md + optional MCP server MCP servers / Claude Code plugin / 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 ⚠️ MCP hosts (Claude, Cursor, VS Code) ✅ 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 ✅ 7 types ❌ ✅ paper-mode classifier ❌

Mermaid authoring ✅ 28 types (CLI ≥ 30) ✅ ❌ ❌

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 + 18 data-store ❌ ❌ ❌

Browser fallback ✅ diagrams.net URL (viewer + editable) ✅ diagrams.net URL (plugin) + inline preview ✅ via optional MCP ✅ diagrams.net viewer (primary)

Zero-config ✅ copy skills/drawio-skill/ ✅ ✅ desktop-only mode ❌ needs plugin install

Using the official jgraph plugin? jgraph/drawio-mcp now ships an official Claude Code plugin ( /plugin install drawio@drawio ) that also generates .drawio and exports via the desktop CLI.

drawio-skill is complementary — reach for it when you want the code / IaC / SQL / OpenAPI / AsyncAPI / Protobuf importers, AI-brand logos, deterministic sequence & C4 generators, self-check + review loop, and the interactive HTML viewer, all from a single SKILL.

md with no MCP server.

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:

• A casual, hand-drawn / whiteboard look → excalidraw-skill or tldraw-skill

• Diagrams-as-code that live in git and render in Markdown → mermaid-skill (general) or plantuml-skill (UML)

• Freeform infinite-canvas sketching / freehand strokes → tldraw-skill

🔗 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

👤 Author

Agents365-ai

• GitHub: https://github.com/Agents365-ai

• Bilibili: https://space.bilibili.com/441831884

📄 License

MIT

— 本文由 AI 根据公开来源辅助整理，命令、版本与许可证请在使用前到原始页面复核。