Turn natural-language descriptions into editable `.drawio` diagrams and export them to PNG / SVG / PDF / JPG via the native draw.io desktop CLI, or turn an existing codebase (Python / JS-TS / Go / Rust) into an auto-laid-out structure diagram. Wraps Agents365-ai/drawio-skill: 6 diagram presets (ERD, UML class, sequence, architecture, ML/DL, flowchart), search across 10,000+ official AWS/Azure/GCP/Cisco/K8s/UML/ BPMN shapes, 321 AI/LLM brand logos, vision self-check + auto-fix, and a 5-round iterative refinement loop. No MCP server, no daemon — runs from a single SKILL.md and the draw.io CLI. Use when the user wants polished, precise, exportable diagrams or wants to visualize code structure. Triggers on: drawio, draw.io, drawio diagram, architecture diagram, ERD, UML diagram, sequence diagram, flowchart, network diagram, visualize codebase, code structure diagram, class hierarchy, export diagram png/svg/pdf, AWS/Azure/GCP icon, draw.io shapes.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Turn natural-language descriptions into editable `.drawio` diagrams and export them to PNG / SVG / PDF / JPG via the native draw.io desktop CLI, or turn an existing codebase (Python / JS-TS / Go / Rust) into an auto-laid-out structure diagram. Wraps Agents365-ai/drawio-skill: 6 diagram presets (ERD, UML class, sequence, architecture, ML/DL, flowchart), search across 10,000+ official AWS/Azure/GCP/Cisco/K8s/UML/ BPMN shapes, 321 AI/LLM brand logos, vision self-check + auto-fix, and a 5-round iterative refinement loop. No MCP server, no daemon — runs from a single SKILL.md and the draw.io CLI. Use when the user wants polished, precise, exportable diagrams or wants to visualize code structure. Triggers on: drawio, draw.io, drawio diagram, architecture diagram, ERD, UML diagram, sequence diagram, flowchart, network diagram, visualize codebase, code structure diagram, class hierarchy, export diagram png/svg/pdf, AWS/Azure/GCP icon, draw.io shapes.
allowed-tools
Bash Read Write Edit Glob Grep WebFetch
compatibility
Wraps the Agent-Skills-format drawio-skill (Agents365-ai/drawio-skill), usable from Claude Code, Cursor, Copilot, OpenClaw, Codex, Gemini CLI, and any agent compatible with the Agent Skills format. Requires the draw.io desktop CLI for export and optional Graphviz for codebase auto-layout. Routes hand-drawn/whiteboard looks to excalidraw/tldraw, diagrams-as-code-in-git to mermaid/plantuml, deck artifacts to `presentation-builder`, and generic plugin browsing to `ccpi-marketplace`.
drawio-skill (Agents365-ai,
MIT) converts a natural-language description into editable .drawio XML and
exports it to PNG / SVG / PDF / JPG through the native draw.io desktop
CLI — no MCP server, no background daemon. It can also turn an existing
codebase into an auto-laid-out structure diagram. This skill is the
jeo-skills wrapper: it documents when to reach for draw.io, how to install
the CLI + skill (including as a plugin), and how to drive the
plan → generate → self-check → iterate → export loop.
When to use this skill
The user wants a polished, precise diagram — architecture, network
topology, microservices, cloud (AWS/Azure/GCP), strict UML class/sequence,
ER diagram, flowchart, mind map, org chart, ML/DL model
The user wants to visualize an existing codebase — import graphs for
Python / JS-TS / Go / Rust, or a Python class-inheritance hierarchy, with
no manual coordinates
The user needs real official vendor icons (AWS Lambda, Kubernetes pod,
Cisco, BPMN, …) instead of guessed shape=mxgraph.* blank boxes
The user wants AI/LLM brand logos (OpenAI, Claude, Gemini, Mistral,
Llama, Ollama, LangChain, …) that draw.io ships none of
The user wants the output exported to PNG/SVG/PDF and kept editable,
optionally self-checked and refined over several rounds
The user explicitly asks for draw.io / .drawio files
When not to use this skill
The user wants a casual, hand-drawn / whiteboard look → use
excalidraw-skill or tldraw-skill (sibling upstream skills)
The user wants diagrams-as-code that live in git and render in Markdown
→ use mermaid (general) or plantuml (UML in CI)
The user wants a slide deck / presentation artifact →
presentation-builder
The user only wants to browse/install Claude plugins generally →
ccpi-marketplace
The user needs AI image/video generation (not diagrams) →
god-tibo-imagen / video-production
Prerequisites
Requirement
Notes
draw.io desktop CLI
Needed for export. drawio --version must work
A vision-capable agent
The self-check step reads the rendered PNG to auto-fix layout
Graphviz (optional)
Only for codebase auto-layout (brew install graphviz / apt install graphviz)
Node.js (optional)
Only the npx skills installer needs it; the skill itself does not
For the jeo-skills plugin install of this wrapper, see Step 8.
Step 3 — Generate a diagram from text
After installation, just describe what you want — the skill plans the
layout, generates .drawio XML, exports a draft, self-checks, and iterates:
Create a microservices e-commerce architecture with Mobile/Web/Admin
clients, an API Gateway (auth + rate limiting + routing), Auth/User/Order/
Product/Payment services, a Kafka queue, a Notification service, and
User DB / Order DB / Product DB / Redis Cache / Stripe API.
Draw a Transformer encoder-decoder: 6-layer encoder with self-attention,
6-layer decoder with cross-attention, input embeddings (batch × 512 × 768),
positional encoding, final output projection. Annotate tensor shapes and
color-code by layer type.
Step 4 — Visualize an existing codebase
Turn code into structure diagrams with the bundled
extract → auto-layout → validate pipeline (no manual coordinates):
Auto-layout uses Graphviz placement + orthogonal routing, transitive
reduction (drops edges implied by a longer path), and --group for nested
module containers. validate.py lints the .drawio (dangling edges,
duplicate ids, overlaps) before the visual self-check.
Step 5 — Resolve real shapes and AI/LLM logos
# Search 10,000+ official AWS/Azure/GCP/Cisco/K8s/UML/BPMN shapes
python3 scripts/shapesearch.py "aws lambda" --limit 5
# Resolve one of 321 AI/LLM brand logos (lobe-icons, MIT)
python3 scripts/aiicons.py "claude" --json # CDN-referenced (default)
python3 scripts/aiicons.py "openai" --embed # self-contained data URI
Use these instead of guessing shape= strings, so vendor icons render
correctly rather than falling back to a blank box.
Step 6 — Self-check, iterate, and style
The workflow runs check deps → plan layout → generate XML → export draft
PNG → self-check + auto-fix (up to 2 rounds) → show user → 5-round feedback
loop → final export. Built-in style presets are default, corporate,
handdrawn, and the skill can learn a new style from a .drawio file or a
flat image (saved only after you approve):
Draw a microservices architecture using my "corporate" style
Learn my style from ~/diagrams/brand.drawio as "mybrand"
Step 7 — Choose the right diagram type
Category
Examples
Notable features
Architecture
microservices, cloud, network, deployment
Tier swimlanes, hub-center strategy
ML / Deep Learning
Transformer, CNN, LSTM, GRU
Tensor-shape annotations, layer colors
Flowcharts
processes, workflows, decision trees, state machines
This wrapper folder is plugin-installable through the standard jeo-skills
flow so the routing guide, references, and installer land on disk for any
supported agent runtime:
# Project install (writes into .agents/skills/drawio/)
npx skills add https://github.com/akillness/jeo-skills --skill drawio
# Global install for every detected agent
npx skills add -g https://github.com/akillness/jeo-skills --skill drawio
# Target specific agents
npx skills add -g https://github.com/akillness/jeo-skills --skill drawio -a claude-code -a codex -y
The skill also ships scripts/install.sh — a one-shot
helper that detects/installs the draw.io CLI per platform and pulls the
upstream drawio-skill bundle. See references/usage.md
for the full command/flag reference.
Output format
When the user asks drawio for help, return a compact brief:
# drawio Routing Brief## Scope- Task: text-to-diagram | visualize-codebase | shape/logo-resolve | restyle
- Diagram type: architecture | uml | sequence | flowchart | erd | ml-dl | other
- Export target: png | svg | pdf | jpg | drawio-only
## Recommended next move- install-cli | install-skill | generate | extract+autolayout | shapesearch | aiicons | refine
## Why- 2-3 bullets grounded in the user's request
## Route-outs-`mermaid` / `plantuml` for diagrams-as-code in git
-`excalidraw` / `tldraw` for hand-drawn/whiteboard looks
-`presentation-builder` for slide decks
Best practices
Confirm the CLI first — drawio --version before promising an export;
without it you can only emit .drawio XML, not PNG/SVG/PDF.
Search shapes, don't guess — shapesearch.py resolves the exact
official style string; guessed shape=mxgraph.* names render blank.
Use aiicons.py for LLM-app diagrams — draw.io ships no modern
AI/LLM logos; --embed inlines them for offline rendering.
Let the self-check run — reading the rendered PNG and auto-fixing
overlaps/clipped labels is the skill's main quality lever.
Reduce, then lay out — for codebases, transitive reduction turns a
dense hairball into a traceable graph before auto-layout.
Pick the right family — reach for mermaid/plantuml for
git-tracked, Markdown-embeddable diagrams; draw.io is for polished,
exportable, icon-rich artifacts.