drawio-skill

Name: drawio-skill
Author: Agents365-ai

Verified

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.

5,031stars

375forks

Python

Added 5/18/2026

View on GitHub Download ZIP Scan for vulnerabilities

30 days in the Featured rail · terms & refunds

AI Agentsagent-skillagent-skillsarchitecture-diagramclaude-codeclaude-code-skillclaude-skillsdiagramdrawioerdflowchartopenclawopenclaw-skillsskill-mdskillsmpuml

Installation

# Add to your Claude Code skills
git clone https://github.com/Agents365-ai/drawio-skill

Getting Started

Guides for using ai agents skills like drawio-skill.

Security ReportVerified

Last scanned: 5/18/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-18T08:03:28.842Z",
  "semgrepRan": false,
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

drawio-skill — From Text to Professional Diagrams

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), Terraform / Kubernetes / docker-compose infrastructure, or a SQL schema into an auto-laid-out diagram. Works with Claude Code, Cursor, Copilot, OpenClaw, Codex, Autohand Code, Hermes, and any agent compatible with the Agent Skills format.

✨ Highlights

7 diagram type presets — ERD, UML Class, Sequence, C4, Architecture, ML/Deep Learning, Flowchart
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
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
IaC → architecture diagram — turn Terraform configs, Kubernetes manifests, or docker-compose files into an architecture diagram where every resource renders as its official AWS / Azure / GCP / K8s icon, edges derived from actual references (role ARNs, selectors, volume mounts)
SQL DDL → ER diagram — parse CREATE TABLE statements into per-table nodes with PK/FK markers and crow's-foot foreign-key edges
Deterministic sequence diagrams — describe participants + messages as JSON; lifelines, auto-tracked activation bars, and arrows are computed, never hand-placed
C4 model with drill-down — one command generates the multi-page System Context → Container → Component set with official C4 shapes; parent elements click through to their child page
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, plus 18 data-store brands (Redis, Postgres, Qdrant, Milvus…) for LLM/RAG 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:

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. 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.

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

# 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 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 Code & Infrastructure

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:

# 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

# Infrastructure as Code — official cloud icons resolved automatically
python3 scripts/tfimports.py   ./infra      -o graph.json   # Terraform → AWS/Azure/GCP icons
python3 scripts/k8simports.py  ./manifests  -o graph.json   # K8s YAML/JSON → kind icons
python3 scripts/composeimports.py compose.yml -o graph.json # services + named volumes

# Live infrastructure — draw what's ACTUALLY runn

Frequently Asked Questions

What is drawio-skill?

drawio-skill is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Agents365-ai. 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. It has 5,031 GitHub stars.

Is drawio-skill safe to use?

Yes. drawio-skill passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install drawio-skill?

Clone the repository with "git clone https://github.com/Agents365-ai/drawio-skill" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is drawio-skill written in?

drawio-skill is primarily written in Python. It is open-source under Agents365-ai on GitHub, so you can review or fork the full source.

Are there alternatives to drawio-skill?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh drawio-skill against similar tools.

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drawio-skill — From Text to Professional Diagrams

English · 中文 · 📖 Online Docs

✨ Highlights

7 diagram type presets — ERD, UML Class, Sequence, C4, Architecture, ML/Deep Learning, Flowchart
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
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
IaC → architecture diagram — turn Terraform configs, Kubernetes manifests, or docker-compose files into an architecture diagram where every resource renders as its official AWS / Azure / GCP / K8s icon, edges derived from actual references (role ARNs, selectors, volume mounts)
SQL DDL → ER diagram — parse CREATE TABLE statements into per-table nodes with PK/FK markers and crow's-foot foreign-key edges
Deterministic sequence diagrams — describe participants + messages as JSON; lifelines, auto-tracked activation bars, and arrows are computed, never hand-placed
C4 model with drill-down — one command generates the multi-page System Context → Container → Component set with official C4 shapes; parent elements click through to their child page
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, plus 18 data-store brands (Redis, Postgres, Qdrant, Milvus…) for LLM/RAG 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:

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

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

# 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 and ClawHub.

⚡ 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 Code & Infrastructure

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:

# 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

# Infrastructure as Code — official cloud icons resolved automatically
python3 scripts/tfimports.py   ./infra      -o graph.json   # Terraform → AWS/Azure/GCP icons
python3 scripts/k8simports.py  ./manifests  -o graph.json   # K8s YAML/JSON → kind icons
python3 scripts/composeimports.py compose.yml -o graph.json # services + named volumes

# Live infrastructure — draw what's ACTUALLY runn