excalidrawing

Generate `.excalidraw` JSON files that **argue visually**, not just display information.

Setup: If the user asks you to set up this skill (renderer, dependencies, etc.), see README.md for instructions.

**All colors and brand-specific styles live in one file:** `references/color-palette.md`. Read it before generating any diagram and use it as the single source of truth for all color choices — shape fills, strokes, text colors, evidence artifact backgrounds, everything.

To make this skill produce diagrams in your own brand style, edit color-palette.md. Everything else in this file is universal design methodology and Excalidraw best practices.

<quick_start>

1. Assess depth — Simple/conceptual or comprehensive/technical?
2. Research (technical only) — Look up actual specs, formats, event names
3. Map concepts to visual patterns — See references/visual-patterns.md
4. Design before JSON — Sketch the flow mentally, ensure variety
5. Generate JSON — Section-by-section for large diagrams (see references/large-diagrams.md)
6. Render & validate — Run the render-view-fix loop until it's right

Pull colors from references/color-palette.md, element templates from references/element-templates.md, and JSON schema from references/json-schema.md.

</quick_start>

**Diagrams should ARGUE, not DISPLAY.**

A diagram isn't formatted text. It's a visual argument that shows relationships, causality, and flow that words alone can't express. The shape should BE the meaning.

The Isomorphism Test: If you removed all text, would the structure alone communicate the concept? If not, redesign.

The Education Test: Could someone learn something concrete from this diagram, or does it just label boxes? A good diagram teaches — it shows actual formats, real event names, concrete examples.

<depth_assessment> Before designing, determine what level of detail this diagram needs:

Simple/Conceptual Diagrams — Use abstract shapes when:

• Explaining a mental model or philosophy
• The audience doesn't need technical specifics
• The concept IS the abstraction (e.g., "separation of concerns")

Comprehensive/Technical Diagrams — Use concrete examples when:

• Diagramming a real system, protocol, or architecture
• The diagram will be used to teach or explain (e.g., YouTube video)
• The audience needs to understand what things actually look like
• You're showing how multiple technologies integrate

For technical diagrams, you MUST include evidence artifacts and do research first. </depth_assessment>

<research_mandate> Before drawing anything technical, research the actual specifications.

If you're diagramming a protocol, API, or framework:

1. Look up the actual JSON/data formats
2. Find the real event names, method names, or API endpoints
3. Understand how the pieces actually connect
4. Use real terminology, not generic placeholders

Bad: "Protocol" → "Frontend" Good: "AG-UI streams events (RUN_STARTED, STATE_DELTA, A2UI_UPDATE)" → "CopilotKit renders via createA2UIMessageRenderer()"

Research makes diagrams accurate AND educational. </research_mandate>

<evidence_artifacts> Evidence artifacts are concrete examples that prove your diagram is accurate and help viewers learn. Include them in technical diagrams.

Types of evidence artifacts (choose what's relevant to your diagram):

Artifact Type	When to Use	How to Render
Code snippets	APIs, integrations, implementation details	Dark rectangle + syntax-colored text (see color palette for evidence artifact colors)
Data/JSON examples	Data formats, schemas, payloads	Dark rectangle + colored text (see color palette)
Event/step sequences	Protocols, workflows, lifecycles	Timeline pattern (line + dots + labels)
UI mockups	Showing actual output/results	Nested rectangles mimicking real UI
Real input content	Showing what goes IN to a system	Rectangle with sample content visible
API/method names	Real function calls, endpoints	Use actual names from docs, not placeholders

The key principle: show what things actually look like, not just what they're called. </evidence_artifacts>

<multi_zoom> Comprehensive diagrams operate at multiple zoom levels simultaneously:

Level 1: Summary Flow — A simplified overview showing the full pipeline at a glance. Often placed at the top or bottom.

Level 2: Section Boundaries — Labeled regions that group related components. These create visual "rooms" that help viewers understand what belongs together.

Level 3: Detail Inside Sections — Evidence artifacts, code snippets, and concrete examples within each section. This is where the educational value lives.

For comprehensive diagrams, aim to include all three levels. The summary gives context, the sections organize, and the details teach.

Bad (Displaying)	Good (Arguing)
5 equal boxes with labels	Each concept has a shape that mirrors its behavior
Card grid layout	Visual structure matches conceptual structure
Icons decorating text	Shapes that ARE the meaning
Same container for everything	Distinct visual vocabulary per concept
Everything in a box	Free-floating text with selective containers

</multi_zoom>

<container_discipline> Not every piece of text needs a shape around it. Default to free-floating text. Add containers only when they serve a purpose.

Use a Container When...	Use Free-Floating Text When...
It's the focal point of a section	It's a label or description
It needs visual grouping with other elements	It's supporting detail or metadata
Arrows need to connect to it	It describes something nearby
The shape itself carries meaning (decision diamond, etc.)	Typography alone creates sufficient hierarchy
It represents a distinct "thing" in the system	It's a section title, subtitle, or annotation

Typography as hierarchy: Use font size, weight, and color to create visual hierarchy without boxes. A 28px title doesn't need a rectangle around it.

The container test: For each boxed element, ask "Would this work as free-floating text?" If yes, remove the container. </container_discipline>

<design_process> Do this BEFORE generating JSON:

Step 0: Assess Depth — Simple/conceptual or comprehensive/technical? If comprehensive, do research first.

Step 1: Understand Deeply — For each concept, ask:

• What does this concept DO? (not what IS it)
• What relationships exist between concepts?
• What's the core transformation or flow?
• What would someone need to SEE to understand this?

Step 2: Map Concepts to Patterns — See references/visual-patterns.md for the full pattern library and concept-to-pattern mapping table.

Step 3: Ensure Variety — Each major concept must use a different visual pattern. No uniform cards or grids.

Step 4: Sketch the Flow — Mentally trace how the eye moves through the diagram. There should be a clear visual story.

Step 5: Generate JSON — For large/comprehensive diagrams, build section-by-section (see references/large-diagrams.md). Use descriptive string IDs and namespace seeds by section.

Step 6: Render & Validate (MANDATORY) — Run the render-view-fix loop until the diagram looks right. See <render_validate> below. </design_process>

<color_usage> Colors encode information, not decoration. Every color choice should come from references/color-palette.md.

Key principles:

• Each semantic purpose (start, end, decision, AI, error, etc.) has a specific fill/stroke pair
• Free-floating text uses color for hierarchy (titles, subtitles, details — each at a different level)
• Evidence artifacts (code snippets, JSON examples) use their own dark background + colored text scheme
• Always pair a darker stroke with a lighter fill for contrast

Do not invent new colors. If a concept doesn't fit an existing semantic category, use Primary/Neutral or Secondary. </color_usage>

**Roughness:**

• roughness: 0 — Clean, crisp edges. Default for modern/technical diagrams.
• roughness: 1 — Hand-drawn, organic feel. Use for brainstorming/informal diagrams.

Stroke Width:

• strokeWidth: 1 — Thin, elegant. Good for lines, dividers, subtle connections.
• strokeWidth: 2 — Standard. Good for shapes and primary arrows.
• strokeWidth: 3 — Bold. Use sparingly for emphasis.

Opacity: Always opacity: 100. Use color, size, and stroke width for hierarchy instead of transparency.

Small markers: Use small dots (10-20px ellipses) instead of full shapes for timeline markers, bullet points, connection nodes, and visual anchors.

**Hierarchy Through Scale:**

• Hero: 300×150 — visual anchor, most important
• Primary: 180×90
• Secondary: 120×60
• Small: 60×40

Whitespace = Importance: The most important element has the most empty space around it (200px+).

Flow Direction: Left→right or top→bottom for sequences, radial for hub-and-spoke.

Connections Required: Position alone doesn't show relationships. If A relates to B, there must be an arrow.

<text_rules> CRITICAL: The JSON text property contains ONLY readable words.

{
  "id": "myElement1",
  "text": "Start",
  "originalText": "Start"
}

Settings: fontSize: 16, fontFamily: 3, textAlign: "center", verticalAlign: "middle" </text_rules>

<json_structure>

{
  "type": "excalidraw",
  "version": 2,
  "source": "https://excalidraw.com",
  "elements": [],
  "appState": {
    "viewBackgroundColor": "#ffffff",
    "gridSize": 20
  },
  "files": {}
}

See references/element-templates.md for copy-paste JSON templates for each element type (text, line, dot, rectangle, arrow). See references/json-schema.md for the complete element schema. Pull colors from references/color-palette.md based on each element's semantic purpose. </json_structure>

<render_validate> You cannot judge a diagram from JSON alone. After generating or editing the Excalidraw JSON, you MUST render it to PNG, view the image, and fix what you see — in a loop until it's right.

GATE: Before first render, verify the .excalidraw file is valid JSON:

python3 -m json.tool < path-to-file.excalidraw > /dev/null

If invalid, fix JSON syntax before wasting a render cycle.

How to render:

cd ${SKILL_DIR}/references && uv run python render_excalidraw.py <path-to-file.excalidraw>

This outputs a PNG next to the .excalidraw file. Then use the Read tool on the PNG to view it.

The loop:

1. Render & View — Run the render script, then Read the PNG.
2. Audit against your original vision — Does the visual structure match the conceptual structure you planned? Does each section use the intended pattern? Is the visual hierarchy correct?
3. Check for visual defects:
   • Text clipped by or overflowing its container
   • Text or shapes overlapping other elements
   • Arrows crossing through elements instead of routing around them
   • Arrows landing on the wrong element or pointing into empty space
   • Labels floating ambiguously
   • Uneven spacing, text too small, lopsided composition
4. Fix — Edit the JSON. Common fixes: widen containers, adjust x/y coordinates, add waypoints to arrow points arrays, reposition labels, resize elements.
5. Re-render & re-view — Repeat until the diagram passes both the vision check and the defect check. Typically 2-4 iterations.

When to stop: No text clipped or overlapping, arrows route cleanly, spacing is consistent, composition is balanced, you'd show it without caveats.

First-time setup:

cd ${SKILL_DIR}/references
uv sync
uv run playwright install chromium

</render_validate>

<failure_modes> Failures from actual usage:

Failure 1: JSON truncated mid-generation

• What happened: Entire diagram generated in one response, hit output token limit, produced invalid JSON
• Why it failed: Comprehensive diagrams exceed ~32,000 token output limit
• How to avoid: ALWAYS build section-by-section for large diagrams. See references/large-diagrams.md

Failure 2: Text clipped by container

• What happened: Text overflowed rectangle bounds but looked fine in JSON
• Why it failed: Container width/height not updated after changing text content
• How to avoid: After changing any text property, recalculate and update the container dimensions. Always verify with a render cycle.

Failure 3: Arrow bound to wrong element

• What happened: Arrow visually pointed at element B but was bound to element A
• Why it failed: IDs were reused or copy-pasted without updating startBinding/endBinding
• How to avoid: Use descriptive unique IDs (e.g., "auth_flow_arrow", not "arrow1"). After every arrow edit, verify both startBinding.elementId and endBinding.elementId reference the correct targets.

Failure 4: All boxes same size — "card grid" anti-pattern

• What happened: Diagram looked like a PowerPoint slide with equal rectangles
• Why it failed: Skipped Step 2 (map concepts to patterns) and defaulted to uniform containers
• How to avoid: Each major concept MUST use a different visual pattern. Run the Isomorphism Test before generating JSON.

</failure_modes>

<success_criteria>

Depth & Evidence (technical diagrams):

• Research done — actual specs, formats, event names looked up
• Evidence artifacts included — code snippets, JSON examples, or real data
• Multi-zoom — summary flow + section boundaries + detail
• Concrete over abstract — real content shown, not just labeled boxes

Conceptual:

• Isomorphism — each visual structure mirrors its concept's behavior
• Argument — diagram SHOWS something text alone couldn't
• Variety — each major concept uses a different visual pattern
• No card grids or uniform containers

Container Discipline:

• <30% of text elements inside containers
• Lines as structure for tree/timeline patterns
• Typography hierarchy via font size and color

Technical:

• text contains only readable words
• fontFamily: 3, roughness: 0, opacity: 100
• Valid JSON (verify: python3 -m json.tool < file.excalidraw > /dev/null)

Visual Validation (render required):

• Rendered to PNG and visually inspected
• No text overflow, no overlapping elements
• Arrows land correctly, even spacing, balanced composition

</success_criteria>

<reference_guides>

File	When to Read
references/color-palette.md	Before generating any diagram (colors and styles)
references/element-templates.md	When writing JSON (copy-paste element templates)
references/json-schema.md	When you need the complete element schema
references/visual-patterns.md	During Step 2 (concept-to-pattern mapping)
references/large-diagrams.md	When building comprehensive/multi-section diagrams

</reference_guides>