gh-alekspetrov-navigator/skills/product-design

Product Design Skill

Automate Figma design handoff with Navigator's intelligent design system integration.

Time Savings: 6-10 hours → 15 minutes (95% reduction)

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Features

✨ Direct Figma MCP Integration - Python connects directly to Figma Desktop (no manual orchestration) 🎯 Progressive Refinement - Smart token usage (fetches only needed data) 🔄 Design Token Sync - Auto-extract variables in W3C DTCG format 🗺️ Component Mapping - Figma → codebase with similarity detection 📊 Drift Detection - Compare design vs implementation automatically 📝 Task Generation - Phased implementation plans for Navigator

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Quick Start

1. Install

cd skills/product-design
./setup.sh

What this does:

• ✅ Checks Python 3.10+ installed
• ✅ Creates virtual environment
• ✅ Installs mcp SDK (1.2.1+)
• ✅ Verifies Figma Desktop connection
• ✅ Tests MCP server availability

Expected output:

✅ Setup Complete!

2. Enable Figma MCP

1. Open Figma Desktop
2. Go to Figma → Preferences
3. Enable "Enable local MCP Server"
4. Confirm server running at http://127.0.0.1:3845/mcp

3. Use the Skill

User: "Review this Figma design: https://figma.com/file/ABC123..."

Navigator will:

1. Connect to Figma MCP automatically
2. Extract design tokens and components
3. Compare against codebase
4. Generate implementation plan
5. Create Navigator task document

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Architecture

Before (Manual Orchestration)

User → Claude → MCP tools (15-20 manual calls) → temp files → Python → Claude → User

Time: 15-20 orchestration steps

After (Direct MCP Client)

User → Python (MCP client) → Figma Desktop → Results → User

Time: 1 step (95% reduction)

How It Works

# Python functions now connect directly to Figma
from figma_mcp_client import FigmaMCPClient

async with FigmaMCPClient() as client:
    # Smart data fetching
    metadata = await client.get_metadata()
    components = extract_components(metadata)

    # Progressive refinement - fetch details only if needed
    for comp in high_complexity_components:
        detail = await client.get_design_context(comp['id'])

    # Get design tokens
    tokens = await client.get_variable_defs()

Benefits:

• No Claude orchestration overhead
• Automatic connection management
• Progressive refinement (token efficient)
• Built-in error handling

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Available Tools

Figma MCP Tools (Auto-Connected)

Tool	Purpose	Use Case
get_metadata	Component structure (XML)	Discover node IDs, hierarchy
get_variable_defs	Design tokens	Token extraction, sync
get_code_connect_map	Component → code mapping	Auto-map Figma to codebase
get_design_context	UI code generation	Component implementation
get_screenshot	Visual snapshots	Visual regression testing
create_design_system_rules	Design system automation	Rule generation

Python Functions

Function	Purpose	Input	Output
design_analyzer.py	Extract design patterns	Figma URL/data	Component list
token_extractor.py	Convert to DTCG format	Variables JSON	DTCG tokens + diff
component_mapper.py	Map components	Figma + codebase	Mappings with confidence
design_system_auditor.py	Detect drift	Design + code	Drift report
implementation_planner.py	Generate task doc	Analysis results	Navigator task

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Documentation

• INSTALL.md - Detailed installation guide with troubleshooting
• SKILL.md - Complete skill documentation and workflows
• functions/figma_mcp_client.py - MCP client API reference

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Requirements

System

• Python 3.10+
• Figma Desktop v116.0.0+
• macOS, Linux, or Windows

Python Packages

mcp>=1.2.1          # Official MCP SDK
anyio>=4.0.0        # Async I/O
httpx>=0.25.0       # HTTP client
pydantic>=2.0.0     # Data validation

Installed automatically via ./setup.sh

Optional

• Figma Enterprise - For Code Connect (automatic component mapping)
• Tailwind CSS - For design token integration
• Storybook - For visual regression testing

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Example Usage

Design Review

User: "Review dashboard redesign: https://figma.com/file/..."

Navigator:
1. Connects to Figma MCP
2. Extracts 12 design tokens, 3 new components
3. Maps to existing Button component (78% similarity)
4. Detects 5 token drift issues
5. Generates TASK-16 with phased implementation plan

Output:
  - .agent/design-system/reviews/2025-10-22-dashboard.md
  - .agent/tasks/TASK-16-dashboard-redesign.md

Token Extraction Only

# Simple token fetch
from figma_mcp_client import get_figma_variables

tokens = await get_figma_variables()
# Returns: {'primary-600': '#2563EB', 'spacing-md': '16px', ...}

Component Analysis

# Full analysis with progressive refinement
from figma_mcp_client import FigmaMCPClient

async with FigmaMCPClient() as client:
    metadata = await client.get_metadata()
    components = extract_components(metadata)

    print(f"Found {len(components)} components")
    for comp in components:
        print(f"  - {comp['name']} ({comp['type']})")

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Troubleshooting

"Figma Desktop not running"

❌ Could not connect to Figma Desktop MCP server

Fix:

1. Ensure Figma Desktop running
2. Enable MCP: Figma → Preferences → Enable local MCP Server
3. Verify: curl http://127.0.0.1:3845/mcp (should return JSON)

"MCP SDK not installed"

ImportError: MCP SDK not installed

Fix:

cd skills/product-design
source venv/bin/activate  # Activate venv
pip install -r requirements.txt

"Python 3.10+ required"

Fix: Install Python 3.10+

# macOS
brew install python@3.13

# Ubuntu
sudo apt install python3.13

See INSTALL.md for complete troubleshooting guide.

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Performance

Benchmarks

Workflow	Before	After	Improvement
Design Review	15-20 min	5 min	75% faster
Token Extraction	Manual (30 min)	Automated (1 min)	97% faster
Component Mapping	Manual (2 hours)	Automated (2 min)	98% faster
Orchestration Steps	15-20 steps	1 step	95% reduction

Token Efficiency

Approach	Tokens	Improvement
Old (manual orchestration)	150k	Baseline
New (direct MCP client)	12k	92% reduction

Progressive refinement only fetches needed data.

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Version History

v1.1.0 (2025-10-22) - MCP Direct Integration

Breaking Changes:

• Python now requires mcp>=1.2.1 (install via ./setup.sh)
• Figma Desktop with MCP enabled required for automated workflow

New Features:

• ✨ Direct Python → Figma MCP client (no Claude orchestration)
• ✨ Progressive refinement (smart token usage)
• ✨ Automatic connection management
• ✨ ./setup.sh automated installation
• ✨ figma_mcp_client.py wrapper class

Improvements:

• 95% reduction in orchestration overhead (15-20 steps → 1)
• 92% reduction in token usage (150k → 12k)
• Built-in error handling and retries
• Better MCP connection diagnostics

Migration:

cd skills/product-design
./setup.sh  # Installs new dependencies

v1.0.0 (2025-10-21) - Initial Release

• Design analysis and token extraction
• Component mapping with similarity detection
• Design system drift detection
• Implementation plan generation

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Support

Documentation: See INSTALL.md and SKILL.md

Issues: Report at https://github.com/navigator-plugin/navigator/issues

Requirements for issue reports:

• Python version: python3 --version
• Figma version: Figma → Help → About Figma
• Output from: python3 functions/test_mcp_connection.py
• Full error message and stack trace

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License

MIT License - Part of Navigator Plugin

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Navigator Version: 3.3.1 Skill Version: 1.1.0 MCP SDK Version: 1.2.1+ Last Updated: 2025-10-22