excalidraw-canvas

name: excalidraw_canvas description: Advanced Visual Drawing Skill for Antigravity Agents. version: 1.0.0 author: Antigravity

Operational Instructions: Excalidraw Canvas 🎨

Overview

This skill allows the agent to create and manipulate visual diagrams on the Excalidraw canvas (Port 3000). It acts as a visual reasoning tool for architecture, flows, and planning.

Triggers

• When the user asks to "Draw", "Visualize", or "Diagram" a concept.
• When explaining complex architectural changes (Architecture Gate).
• When converting Mermaid documentation to a high-fidelity visual format.

⚠️ Prerequisites (External Dependency)

[!IMPORTANT] This skill requires the mcp_excalidraw server, which is NOT bundled with Antigravity. You must install it separately before using this skill.

Option A: Local Installation

# Clone the MCP server
git clone https://github.com/yctimlin/mcp_excalidraw ~/mcp_excalidraw
cd ~/mcp_excalidraw && npm install && npm run build

# Set environment variable (add to ~/.bashrc or ~/.zshrc)
export EXCALIDRAW_MCP_PATH="$HOME/mcp_excalidraw/dist/index.js"

# Start Canvas Server (keep running in separate terminal)
npm run canvas  # Opens http://localhost:3000

Option B: Docker Installation (Recommended for Production)

# Pull and run Canvas Server
docker pull ghcr.io/yctimlin/mcp_excalidraw-canvas:latest
docker run -d -p 3000:3000 --name mcp-canvas ghcr.io/yctimlin/mcp_excalidraw-canvas:latest

# For MCP server in Docker, add to your IDE's MCP config:
# { "command": "docker", "args": ["run", "-i", "--rm", "--network", "host", 
#   "-e", "EXPRESS_SERVER_URL=http://localhost:3000", "ghcr.io/yctimlin/mcp_excalidraw:latest"] }

Environment Variables

Zenith V5 Upgrade (Empirically Validated) 💎🔬

This skill has been battle-tested with empirical validation to ensure maximum visual impact.

Critical Visual Parameters (TESTED)

Arrow Rendering:

• ✅ strokeWidth: 2 (Confirmed visible in Excalidraw UI)
• ✅ Vivid Palette (High saturation colors for white backgrounds):
  • node_agent: #f77f00 (Vivid Orange)
  • node_tool: #118ab2 (Ocean Blue)
  • node_data: #06d6a0 (Aqua Green)
  • node_audit: #e63946 (Vivid Red)
• ✅ opacity: 100 (Not 1.0 - Excalidraw uses 0-100 scale)
• ✅ roughness: 0 (Sharp, professional lines)

Legend & Text:

• ✅ fontSize: 18 for headers (readable)
• ✅ Simplified Text: Use "LEGEND" not long descriptive strings
• ✅ No fontFamily Override: Let Excalidraw use defaults to avoid validation errors

Empirical Testing Methodology

When uncertain about visual parameters, create a test script:

1. Generate 5+ variants of the element with different parameters
2. Use vivid, contrasting colors (#e63946, #f77f00, #fcbf49, #06d6a0, #118ab2)
3. Label each variant clearly
4. Ask user for visual confirmation of which is visible
5. Apply confirmed parameters to production code

Example Test (see verifiche_test/test_arrow_rendering.py):

widths = [1, 2, 4, 8, 16]
colors = ["#e63946", "#f77f00", "#fcbf49", "#06d6a0", "#118ab2"]
# Render all, user confirms which is visible

Advanced Features

Steps

1. Analyze Demand: Understand the diagram type (Flowchart, ERD, System Arch).
2. Define Elements: Create a list of elements (rectangles, text, arrows).
3. Invoke Bridge: Use the mcp_bridge.py script to call create_element or batch_create_elements.
4. Iterative Refinement: If the user provides feedback, use update_element or delete_element.

Tool Usage (via Bridge)

# Create a Rectangle
python3 .agent/skills/excalidraw_canvas/scripts/mcp_bridge.py create_element '{
    "type": "rectangle",
    "x": 100,
    "y": 100,
    "width": 150,
    "height": 100,
    "backgroundColor": "#f1faee",
    "strokeColor": "#e63946"
}'

# Create a Text element
python3 .agent/skills/excalidraw_canvas/scripts/mcp_bridge.py create_element '{
    "type": "text",
    "x": 110,
    "y": 140,
    "text": "Antigravity Node",
    "fontSize": 20
}'

# Create an Arrow
python3 .agent/skills/excalidraw_canvas/scripts/mcp_bridge.py create_element '{
    "type": "arrow",
    "x": 100,
    "y": 100,
    "points": [[0, 0], [100, 50]],
    "strokeColor": "#e63946",
    "endArrowhead": "arrow"
}'

### Geometric Rules for Arrows
- **Base Coordinate (x, y)**: The starting point of the arrow.
- **Points Array**: An array of relative offsets from (x, y). 
- **Format**: MUST be an array of tuples `[[0,0], [dx, dy]]`.
- **Logic**: Target coordinate = `(x + dx, y + dy)`.
- **Horizontal**: `[[0,0], [dx, 0]]`
- **Vertical**: `[[0,0], [0, dy]]`

## Advanced: Mermaid Conversion
To convert a Mermaid snippet:
```bash
python3 .agent/skills/excalidraw_canvas/scripts/mcp_bridge.py create_from_mermaid '{"mermaid": "graph TD; A-->B"}'

Special Bridge Commands

The Python bridge includes helper functions for scene management:

• Clear Canvas: python3 .agent/skills/excalidraw_canvas/scripts/mcp_bridge.py clear_canvas
• Get Elements: python3 .agent/skills/excalidraw_canvas/scripts/mcp_bridge.py get_elements

Operational Workflow (V3)

1. Always Clear: Before starting a major new architecture diagram, run clear_canvas. This is now atomic.
2. Geometric Arrows: Always use the create_arrow helper or follow the relative offset formula: Points[1] = (TargetX - StartX, TargetY - StartY).
3. Build First: If the UI returns 404, run npm run build in ~/mcp_excalidraw.
4. Z-Order: Place background grouping rectangles first, then components, then labels.

Examples & Templates

Pre-configured JSON payloads are available in the examples/ directory for common architectural patterns:

• examples/connected_components.json: Simple node-link structure.
• examples/insight_labeling.json: Labeling and pointing to existing elements.
• examples/vivid_infrastructure.json: High-contrast vivid palette demo.