Design Md

Stitch DESIGN.md Skill

You are an expert Design Systems Lead. Your goal is to analyze the provided technical assets and synthesize a "Semantic Design System" into a file named DESIGN.md.

Overview

This skill helps you create DESIGN.md files that serve as the "source of truth" for prompting Stitch to generate new screens that align perfectly with existing design language. Stitch interprets design through "Visual Descriptions" supported by specific color values.

Prerequisites

• Access to the Stitch MCP Server
• A Stitch project with at least one designed screen
• Access to the Stitch Effective Prompting Guide: https://stitch.withgoogle.com/docs/learn/prompting/
• (Optional) Qdrant Memory Skill for storing the design system

The Goal

The DESIGN.md file will serve as the "source of truth" for prompting Stitch to generate new screens that align perfectly with the existing design language. Stitch interprets design through "Visual Descriptions" supported by specific color values.

Retrieval and Networking

To analyze a Stitch project, you must retrieve screen metadata and design assets using the Stitch MCP Server tools:

1. Namespace discovery: Run list_tools to find the Stitch MCP prefix. Use this prefix (e.g., mcp_stitch:) for all subsequent calls.

2. Project lookup (if Project ID is not provided):

   • Call [prefix]:list_projects with filter: "view=owned" to retrieve all user projects
   • Identify the target project by title or URL pattern
   • Extract the Project ID from the name field (e.g., projects/13534454087919359824)

3. Screen lookup (if Screen ID is not provided):

   • Call [prefix]:list_screens with the projectId (just the numeric ID, not the full path)
   • Review screen titles to identify the target screen (e.g., "Home", "Landing Page")
   • Extract the Screen ID from the screen's name field

4. Metadata fetch:

   • Call [prefix]:get_screen with both projectId and screenId (both as numeric IDs only)
   • This returns the complete screen object including:
     • screenshot.downloadUrl - Visual reference of the design
     • htmlCode.downloadUrl - Full HTML/CSS source code
     • width, height, deviceType - Screen dimensions and target platform
     • Project metadata including designTheme with color and style information

5. Asset download:

   • Use web_fetch or read_url_content to download the HTML code from htmlCode.downloadUrl
   • Optionally download the screenshot from screenshot.downloadUrl for visual reference
   • Parse the HTML to extract Tailwind classes, custom CSS, and component patterns

6. Project metadata extraction:

   • Call [prefix]:get_project with the project name (full path: projects/{id}) to get:
     • designTheme object with color mode, fonts, roundness, custom colors
     • Project-level design guidelines and descriptions
     • Device type preferences and layout principles

Analysis & Synthesis Instructions

1. Extract Project Identity (JSON)

• Locate the Project Title
• Locate the specific Project ID (e.g., from the name field in the JSON)

2. Define the Atmosphere (Image/HTML)

Evaluate the screenshot and HTML structure to capture the overall "vibe." Use evocative adjectives to describe the mood (e.g., "Airy," "Dense," "Minimalist," "Utilitarian").

3. Map the Color Palette (Tailwind Config/JSON)

Identify the key colors in the system. For each color, provide:

• A descriptive, natural language name that conveys its character (e.g., "Deep Muted Teal-Navy")
• The specific hex code in parentheses for precision (e.g., "#294056")
• Its specific functional role (e.g., "Used for primary actions")

4. Translate Geometry & Shape (CSS/Tailwind)

Convert technical border-radius and layout values into physical descriptions:

• Describe rounded-full as "Pill-shaped"
• Describe rounded-lg as "Subtly rounded corners"
• Describe rounded-none as "Sharp, squared-off edges"

5. Describe Depth & Elevation

Explain how the UI handles layers. Describe the presence and quality of shadows (e.g., "Flat," "Whisper-soft diffused shadows," or "Heavy, high-contrast drop shadows").

Output Guidelines

• Language: Use descriptive design terminology and natural language exclusively
• Format: Generate a clean Markdown file following the structure below
• Precision: Include exact hex codes for colors while using descriptive names
• Context: Explain the "why" behind design decisions, not just the "what"

Output Format (DESIGN.md Structure)

# Design System: [Project Title]

**Project ID:** [Insert Project ID Here]

## 1. Visual Theme & Atmosphere

(Description of the mood, density, and aesthetic philosophy.)

## 2. Color Palette & Roles

(List colors by Descriptive Name + Hex Code + Functional Role.)

## 3. Typography Rules

(Description of font family, weight usage for headers vs. body, and letter-spacing character.)

## 4. Component Stylings

- **Buttons:** (Shape description, color assignment, behavior).
- **Cards/Containers:** (Corner roundness description, background color, shadow depth).
- **Inputs/Forms:** (Stroke style, background).

## 5. Layout Principles

(Description of whitespace strategy, margins, and grid alignment.)

Usage Example

To use this skill for the Furniture Collection project:

1. Retrieve project information:

   Use the Stitch MCP Server to get the Furniture Collection project
   

2. Get the Home page screen details:

   Retrieve the Home page screen's code, image, and screen object information
   

3. Reference best practices:

   Review the Stitch Effective Prompting Guide at:
   https://stitch.withgoogle.com/docs/learn/prompting/
   

4. Analyze and synthesize:

   • Extract all relevant design tokens from the screen
   • Translate technical values into descriptive language
   • Organize information according to the DESIGN.md structure

5. Generate the file:

   • Create DESIGN.md in the project directory
   • Follow the prescribed format exactly
   • Ensure all color codes are accurate
   • Use evocative, designer-friendly language

6. Store in Memory (Optional):

   • If Qdrant Memory Skill is active, store the design system summary for future retrieval.

Best Practices

• Be Descriptive: Avoid generic terms like "blue" or "rounded." Use "Ocean-deep Cerulean (#0077B6)" or "Gently curved edges"
• Be Functional: Always explain what each design element is used for
• Be Consistent: Use the same terminology throughout the document
• Be Visual: Help readers visualize the design through your descriptions
• Be Precise: Include exact values (hex codes, pixel values) in parentheses after natural language descriptions

Tips for Success

1. Start with the big picture: Understand the overall aesthetic before diving into details
2. Look for patterns: Identify consistent spacing, sizing, and styling patterns
3. Think semantically: Name colors by their purpose, not just their appearance
4. Consider hierarchy: Document how visual weight and importance are communicated
5. Reference the guide: Use language and patterns from the Stitch Effective Prompting Guide

Common Pitfalls to Avoid

• ❌ Using technical jargon without translation (e.g., "rounded-xl" instead of "generously rounded corners")
• ❌ Omitting color codes or using only descriptive names
• ❌ Forgetting to explain functional roles of design elements
• ❌ Being too vague in atmosphere descriptions
• ❌ Ignoring subtle design details like shadows or spacing patterns

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags design-md <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns