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Excalidraw Generator

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luongnv89
excalidraw-generator

Generate diagrams as valid Excalidraw JSON — flowcharts, architecture, ER diagrams, mind maps, sequence diagrams, wireframes, C4 models, and more. Don't use for draw.io/Mermaid output, slide decks, or pixel-perfect brand graphics.

Overview

Publisherluongnv89
Repositoryskills
Skill nameexcalidraw-generator
Stars
124
Forks
18
Bundled files
9
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Excalidraw Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/diagram-generator/excalidraw-generator .claude/skills/excalidraw-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Excalidraw Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Excalidraw Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Excalidraw Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Excalidraw Diagram Generator

Generate professional diagrams as valid Excalidraw JSON. Every diagram goes through four phases: Understand the request, Propose options, Generate the JSON, and Validate before writing the file.

Part of the diagram-generator suite. For precise, editable-in-draw.io output, use drawio-generator instead; the diagram-generator umbrella routes between the two.

This SKILL.md is intentionally compact to fit the agent's context budget (token-efficient body). Long-form details live in references/ — read the linked file when you need depth.

When to Use

Triggers/exclusions are set by the frontmatter description above. Output is an Excalidraw file, or embedded in Markdown via the excalidraw fenced block if the user asks for that.

Environment Check

If the Agent tool is available, use the subagent review loop described in references/style-and-iteration.md (Subagent Architecture). It provides fresh-context validation and avoids single-pass context overflow.

If the Agent tool is unavailable (e.g., Claude.ai), execute every phase inline and self-review against the 10 checks (less rigorous, but functional).

Core Workflow

Phase 1: Understand

Confirm what to draw before generating anything.

  • Clear description ("draw a flowchart of user authentication"): restate your understanding in one sentence and propose the visualization type.
  • Ambiguous input: ask targeted questions — main entities/nodes, relationships, flow direction, style preference.
  • Code, data, schemas, or files: extract structure (code → class/dependency/architecture; SQL → ER; JSON/YAML → architecture/deployment; steps → flowchart/sequence; org data → org chart/tree).

Phase 2: Propose

Present a plan with selectable options:

  1. Diagram type — see references/diagram-types.md for the catalogue. If multiple fit, present numbered options.
  2. Key elements — list the nodes/shapes you'll include.
  3. Layout — propose 2–3 choices (e.g., (A) Top-to-bottom, (B) Left-to-right, (C) Radial).
  4. Style — see references/style-and-iteration.md. Pick rendering style (clean/hand-drawn/sketchy) and color scheme that fits the diagram's purpose. No fixed palette.
  5. Complexity — small (<10 elements), medium (10–30), large (30+).

Wait for confirmation. If the user says "just do it" or the request is straightforward, use sensible defaults (hand-drawn, roughness 1, Virgil font, best-fit layout) and proceed.

Phase 3: Generate

Write a .excalidraw file (raw JSON, no wrapper) in the current working directory. Use kebab-case names (auth-flow.excalidraw). The required envelope:

json
{
  "type": "excalidraw",
  "version": 2,
  "source": "https://excalidraw.com",
  "elements": [],
  "appState": { "theme": "light", "viewBackgroundColor": "#ffffff" },
  "files": {}
}

Set theme and viewBackgroundColor to whatever suits the diagram. For full element schema and field defaults, see references/excalidraw-format.md.

Embedding in Markdown: if the user asks to embed the diagram in a .md file, write the .excalidraw first, then a companion .md that references it via a fenced block tagged excalidraw containing the same JSON.

Phase 4: Validate

Before writing the file, run all 10 checks on the in-memory JSON. Fix and re-check until all pass. Full check definitions and fix recipes are in references/validation-checks.md. Quick summary:

  1. Valid JSON — top-level keys present.
  2. Required fields — every element has the full field list (id, type, geometry, styling, boundElements, seed, etc.). Text adds text/fontSize/fontFamily/textAlign/verticalAlign/containerId/originalText/lineHeight/autoResize. Arrows add points/startBinding/endBinding/startArrowhead/endArrowhead.
  3. Unique IDs — no duplicates.
  4. Two-way text bindingscontainerIdboundElements {type:"text"}.
  5. Two-way arrow bindingsstartBinding/endBindingboundElements {type:"arrow"}.
  6. Arrow pointspoints length ≥ 2 and points[0] === [0,0].
  7. No unintentional overlaps — bounding boxes don't overlap by more than 10px (unless grouped or inside a frame).
  8. Semantic completeness — every entity/relationship the user mentioned is represented.
  9. Readable textfontSize ≥ 16, visible strokeColor, lineHeight: 1.25 (never 1.35), autoResize: true.
  10. Shape-to-text size fit — see below; this is the #1 failure mode.
Check 10 — shape sizing (always verify)

For every text element with containerId:

  1. line_count = text.split('\n').length
  2. min_text_height = line_count * fontSize * 1.25
  3. min_shape_height = min_text_height + 40 (20px padding top/bottom)
  4. The container shape's height must be >= min_shape_height.
  5. The container shape's width must be >= longest_line_pixel_width + 20.

Boundary/container labels (e.g., "System Boundary") must be standalone text with containerId: null, positioned near the top-left of the container — never bound to it.

Fix: increase the shape's height/width to fit the text and shift elements below it to keep spacing. Always set lineHeight: 1.25 and autoResize: true on text. Never patch individual outputs — fix this skill's instructions if a new failure pattern emerges.

After all checks pass, emit the validation summary (10/10 passed, element counts, binding counts, "all shapes sized to fit", any auto-fixes applied).

Step Completion Reports

After each phase, output a status block. Templates and check labels for each phase live in references/step-reports.md. Result line is PASS | FAIL | PARTIAL.

Expected Output

For "draw a flowchart of the user login process": file login-flow.excalidraw containing valid Excalidraw JSON, plus a validation summary in the response. Example fragment:

json
{
  "type": "excalidraw", "version": 2, "source": "https://excalidraw.com",
  "elements": [
    {"id": "start-1", "type": "ellipse", "x": 300, "y": 40, "width": 120, "height": 56,
     "boundElements": [{"id": "txt-start", "type": "text"}]},
    {"id": "txt-start", "type": "text", "text": "Start", "fontSize": 18, "fontFamily": 1,
     "containerId": "start-1", "lineHeight": 1.25, "autoResize": true}
  ],
  "appState": {"theme": "light", "viewBackgroundColor": "#ffffff"}, "files": {}
}

Expected response includes:

Validation: 10/10 checks passed
- Elements: 6 shapes, 6 text labels, 5 arrows
- Bindings: 6 text bindings, 10 arrow bindings (all two-way)
- Text fits: all shapes sized to fit their bound text
- No overlaps, no missing fields

Edge Cases

  • 30+ elements — spawn the subagent review loop in references/style-and-iteration.md; cap at 3 fix cycles.
  • Text-heavy nodes — apply Check 10 strictly; bigger shapes, never smaller text.
  • Ambiguous relationships — ask before guessing; saves a regeneration cycle.
  • "Just do it" — defaults (hand-drawn, roughness 1, Virgil, best-fit layout); skip the proposal.
  • Iteration on an existing file — read the file, preserve unchanged IDs, modify only the requested parts, rewrite.

See references/style-and-iteration.md for extended edge cases, iteration patterns, and style variants.

Acceptance Criteria

  • Output file is valid JSON with top-level type, version, elements, appState, files.
  • Every element has all required fields (per references/validation-checks.md Check 2).
  • Text elements have fontSize >= 16, lineHeight: 1.25, autoResize: true.
  • All element IDs are unique.
  • Text-to-shape bindings are two-way (containerIdboundElements).
  • Arrow bindings are two-way (startBinding/endBindingboundElements).
  • Every arrow has points[0] === [0,0] and length ≥ 2.
  • No bounding boxes overlap by more than 10px (outside groups/frames).
  • Every entity/relationship from the user's request is represented.
  • Every container shape is sized to fit its bound text (Check 10).
  • Validation report (10/10 summary) is included in the response.

Supported Diagram Types

Full catalogue with layout guidance: references/diagram-types.md. Categories: Flow & Process, Architecture, Data & Relationships, Planning, Comparison, Data Viz, UX/Design, Custom. If the request doesn't map cleanly to one type, propose the closest fit and explain why.

References

  • references/validation-checks.md — full text of all 10 Phase-4 checks plus fix recipes.
  • references/excalidraw-format.md — Excalidraw JSON schema and field defaults.
  • references/diagram-types.md — diagram-type catalogue with layout guidance.
  • references/style-and-iteration.md — style variants, iteration patterns, subagent architecture, extended edge cases.
  • references/step-reports.md — step completion report templates per phase.
  • agents/json-generator.md, agents/json-validator.md, agents/json-fixer.md — subagent specs for the large-diagram review loop.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Excalidraw Generator AI skill do?

Generate diagrams as valid Excalidraw JSON — flowcharts, architecture, ER diagrams, mind maps, sequence diagrams, wireframes, C4 models, and more. Don't use for draw.io/Mermaid output, slide decks, or pixel-perfect brand graphics.

Why use Excalidraw Generator on TypingMind?

Because you install it once and use it with any model. Excalidraw Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Excalidraw Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/diagram-generator/excalidraw-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Excalidraw Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Excalidraw Generator?

As many as you like. As long as a model supports skills, you can use Excalidraw Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Excalidraw Generator AI skill free?

Yes. It is published on GitHub by luongnv89 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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