---

name: product-design description: Automates design review, token extraction, component mapping, and implementation planning. Reduces design handoff from 6-10 hours to 5 minutes via direct Figma MCP integration. Auto-invoke when user mentions design review, Figma mockup, or design handoff. allowed-tools: Read, Write, Edit, Grep, Glob, Bash, Task, TodoWrite version: 1.1.0

Product Design Skill

Automate design handoff from Figma to code with design system intelligence. Extract tokens, map components, detect drift, generate implementation plans.

When to Invoke

Auto-invoke when user says:

• "Review this design"
• "Analyze Figma mockup"
• "Design handoff for [feature]"
• "Check design system impact"
• "Plan implementation for design"
• "Extract tokens from Figma"
• "What changed in the design?"

What This Does

5-Step Workflow:

1. Design Analysis: Extract patterns, components, tokens from Figma
2. Codebase Audit: Compare design vs implementation, find drift
3. Implementation Planning: Generate phased task breakdown
4. Task Assignment: Create Navigator task document
5. Handoff: Ask user to review or start implementation

Time Savings: 6-10 hours → 15-20 minutes (95% reduction)

Prerequisites

Required

1. Python Dependencies

   cd skills/product-design
   ./setup.sh  # Automated installation
   # OR manually: pip install -r requirements.txt
   

2. Figma Desktop (for automated workflow)

   • Download: https://www.figma.com/downloads/
   • Enable MCP: Figma → Preferences → Enable local MCP Server
   • Must be running during design reviews

3. Project Structure

   • .agent/design-system/ directory (created on first run)
   • Project with components (React/Vue/Svelte)

Optional (Enhanced Features)

• Figma Enterprise: Code Connect for automatic component mapping
• Tailwind CSS: Design token integration via @theme
• Storybook: Component documentation and visual regression

Installation

Quick start:

cd skills/product-design
./setup.sh

See INSTALL.md for detailed installation guide and troubleshooting.

Workflow Protocol

Step 0: Check Setup (Auto-Run)

Before starting, verify Python dependencies installed:

# Get Navigator plugin path
PLUGIN_PATH=$(dirname "$(dirname "$(dirname "$PWD")")")

# Check if venv exists
if [ ! -d "$PLUGIN_PATH/skills/product-design/venv" ]; then
  echo "❌ product-design skill not set up"
  echo ""
  echo "Run setup (30 seconds):"
  echo "  cd $PLUGIN_PATH/skills/product-design && ./setup.sh"
  echo ""
  echo "Or use manual workflow (no Python needed)"
  exit 1
fi

If setup missing:

• Show setup instructions
• Offer manual workflow as alternative
• Do not proceed with automated Figma workflow

If setup complete:

• Continue to Step 1 (Design Analysis)

---

Step 1: Design Analysis

Objective: Extract design patterns from Figma or manual description

With Figma MCP (Automated) ✨ SIMPLIFIED

New Architecture (v1.1.0+): Python directly connects to Figma MCP - no manual orchestration!

# Python functions now handle MCP connection automatically
from figma_mcp_client import FigmaMCPClient

async with FigmaMCPClient() as client:
    # Progressive refinement - fetch only what's needed
    metadata = await client.get_metadata()
    components = extract_components(metadata)

    # Fetch details only for complex components
    for comp in components:
        if comp['complexity'] == 'high':
            comp['detail'] = await client.get_design_context(comp['id'])

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

Workflow (fully automated):

1. User provides Figma URL
2. Run python3 functions/design_analyzer.py --figma-url <URL>
3. Python connects to Figma MCP (http://127.0.0.1:3845/mcp)
4. Fetches metadata → analyzes → fetches details only if needed
5. Returns complete analysis

Benefits:

• ✅ No manual MCP tool calls by Claude
• ✅ Progressive refinement (smart token usage)
• ✅ Automatic connection management
• ✅ Built-in error handling

Requirements:

• Figma Desktop running
• MCP enabled in preferences
• Python dependencies installed (./setup.sh)

Manual Workflow (No MCP)

**Ask user for design information**:

What is the feature name? [e.g., "Dashboard Redesign"]

Figma link (optional): [figma.com/file/...]

**Design Tokens**:
List new or modified tokens:
- Colors (name: value, e.g., "primary-600: #2563EB")
- Spacing (e.g., "spacing-lg: 24px")
- Typography (e.g., "heading-xl: 36px/600")
- Other (radius, shadow, etc.)

**Components**:
List components in design:
- Component name
- Type (atom, molecule, organism)
- Variants (if any, e.g., "Button: primary/secondary, sm/md/lg")
- Similar to existing component? (name if known)

**Proceed to Step 2** after gathering information

Run design_analyzer.py

# Prepare input (MCP or manual JSON)
# MCP: Already have /tmp/figma_metadata.json
# Manual: Create JSON from user input

python3 functions/design_analyzer.py \
  --figma-data /tmp/figma_combined.json \
  --ui-kit-inventory .agent/design-system/ui-kit-inventory.json \
  --output /tmp/analysis_results.json

Analysis Output:

• New components not in UI kit
• Similar components (reuse opportunities)
• New design tokens
• Breaking changes (if any)

---

Step 2: Codebase Audit

Objective: Compare design vs implementation, detect drift

Token Extraction

python3 functions/token_extractor.py \
  --figma-variables /tmp/figma_variables.json \
  --existing-tokens .agent/design-system/design-tokens.json \
  --output /tmp/token_extraction.json

Output: DTCG formatted tokens + diff summary

Component Mapping

python3 functions/component_mapper.py \
  --figma-components /tmp/analysis_results.json \
  --code-connect-map /tmp/figma_code_connect.json \
  --project-root . \
  --output /tmp/component_mappings.json

Output: Figma component → code component mappings with confidence scores

Design System Audit

# Combine data for auditor
python3 functions/design_system_auditor.py \
  --figma-data /tmp/combined_figma.json \
  --code-data /tmp/combined_code.json \
  --output /tmp/audit_results.json

Audit Results:

• Token alignment (in sync, drift, missing, unused)
• Component reuse opportunities
• Tailwind config recommendations
• Priority level (critical, high, medium, low)

---

Step 3: Implementation Planning

Objective: Generate phased implementation task document

Generate Task Document

python3 functions/implementation_planner.py \
  --task-id "TASK-{{next_task_number}}" \
  --feature-name "{{feature_name}}" \
  --analysis-results /tmp/combined_analysis.json \
  --review-reference ".agent/design-system/reviews/{{date}}-{{feature-slug}}.md" \
  --output .agent/tasks/TASK-{{next_task_number}}-{{feature-slug}}.md

Task Document Includes:

• Phased implementation (tokens → atoms → molecules → organisms)
• Complexity estimates per phase
• Acceptance criteria checklist
• Files to modify
• Testing strategy
• Rollout plan

Create Design Review Report

Use template: templates/design-review-report.md

Save to: .agent/design-system/reviews/YYYY-MM-DD-{{feature-name}}.md

Contents:

• Design analysis summary
• Token changes (added/modified/removed)
• Component changes (new/extended/breaking)
• Design system impact
• Implementation recommendations

---

Step 4: Task Assignment

Objective: Create task and assign context for implementation

Create PM Ticket (if configured)

**If PM tool configured** (Linear, GitHub Issues, Jira):
- Create ticket with task summary
- Link to task document and design review
- Assign to frontend developer or team

**If no PM tool**:
- Skip ticket creation
- Task document serves as source of truth

Update Navigator Documentation

**Update files**:
1. `.agent/tasks/TASK-{{number}}-{{feature}}.md` (created in Step 3)
2. `.agent/design-system/reviews/{{date}}-{{feature}}.md` (design review)
3. `.agent/DEVELOPMENT-README.md` (add task to index)

**Use TodoWrite** to track implementation phases

---

Step 5: Implementation Handoff

Objective: Present results and get user decision

Present Summary

✅ Design review complete for {{Feature Name}}

**Generated Documentation**:
- Design review: `.agent/design-system/reviews/{{date}}-{{feature}}.md`
- Implementation plan: `.agent/tasks/TASK-{{number}}-{{feature}}.md`
{{#if pm_configured}}- PM ticket: {{ticket_id}} (status: ready for development){{/if}}

**Summary**:
- Design Tokens: {{new_count}} new, {{modified_count}} modified
- Components: {{new_components}} new, {{extend_components}} to extend
- Estimated Time: {{total_hours}} hours
- Complexity: {{complexity_level}}
{{#if breaking_changes}}- ⚠️  Breaking Changes: {{breaking_count}} component(s){{/if}}

**Next Steps**:
[1] Start implementation now
[2] Review plan first (load task document)
[3] Modify plan before starting

**Recommended**: After implementation, set up visual regression testing:
  "Set up visual regression for {{components}}"

This ensures pixel-perfect implementation and prevents future drift (15 min setup).

Reply with choice or "Start implementation"

User Decision Branches

If user chooses [1] or says "Start implementation":

1. Load task document: `Read .agent/tasks/TASK-{{number}}-{{feature}}.md`
2. Load design review: `Read .agent/design-system/reviews/{{date}}-{{feature}}.md`
3. Begin Phase 1 (typically design tokens)
4. Follow autonomous completion protocol when done
5. After completion, suggest: "Set up visual regression for {{components}}" (optional but recommended)

If user chooses [2]:

1. Load and display task document
2. Highlight key phases and acceptance criteria
3. Ask: "Ready to start or need changes?"

If user chooses [3]:

1. Load task document
2. Ask what modifications needed
3. Edit task document
4. Regenerate if major changes
5. Then proceed to implementation

---

Predefined Functions

functions/design_analyzer.py

Purpose: Extract design patterns from Figma MCP data or manual input

Usage:

python3 functions/design_analyzer.py \
  --figma-data /path/to/figma_mcp_combined.json \
  --ui-kit-inventory .agent/design-system/ui-kit-inventory.json \
  --output /tmp/analysis.json

Input Format (figma_mcp_combined.json):

{
  "metadata": { ... },  // get_metadata response
  "variables": { ... }, // get_variable_defs response
  "code_connect_map": { ... } // get_code_connect_map response (optional)
}

Output: Component analysis with categorization (atom/molecule/organism) + similarity scores

---

functions/token_extractor.py

Purpose: Convert Figma variables to DTCG format with diff

Usage:

python3 functions/token_extractor.py \
  --figma-variables /path/to/figma_variables.json \
  --existing-tokens .agent/design-system/design-tokens.json \
  --format full \
  --output /tmp/tokens.json

Output Formats:

• full: DTCG tokens + diff + summary
• tokens-only: Just DTCG tokens
• diff-only: Just diff and summary

DTCG Format (W3C Design Tokens spec):

{
  "color": {
    "primary": {
      "500": {
        "$value": "#3B82F6",
        "$type": "color",
        "$description": "Primary brand color"
      }
    }
  }
}

---

functions/component_mapper.py

Purpose: Map Figma components to codebase components

Usage:

python3 functions/component_mapper.py \
  --figma-components /path/to/analysis_results.json \
  --code-connect-map /path/to/code_connect.json \
  --project-root . \
  --output /tmp/mappings.json

Mapping Strategy:

1. Code Connect first (100% confidence)
2. Fuzzy name matching (70%+ confidence)
3. Unmapped = needs creation

Output: Mappings with confidence scores + variant prop mapping

---

functions/design_system_auditor.py

Purpose: Audit design system for drift and reuse opportunities

Usage:

python3 functions/design_system_auditor.py \
  --figma-data /path/to/combined_figma.json \
  --code-data /path/to/combined_code.json \
  --output /tmp/audit.json

Audit Checks:

• Token alignment (drift detection)
• Component reuse opportunities (similarity >70%)
• Unused tokens (cleanup candidates)
• Priority level assignment

---

functions/implementation_planner.py

Purpose: Generate Navigator task document with phased breakdown

Usage:

python3 functions/implementation_planner.py \
  --task-id "TASK-16" \
  --feature-name "Dashboard Redesign" \
  --analysis-results /path/to/combined_analysis.json \
  --review-reference ".agent/design-system/reviews/2025-10-21-dashboard.md" \
  --output .agent/tasks/TASK-16-dashboard-redesign.md

Output: Complete Navigator task document with:

• Phased implementation (atomic design order)
• Complexity estimates (Low/Medium/High)
• Acceptance criteria per phase
• Testing strategy
• Rollout plan

---

Templates

templates/design-review-report.md

When: Step 3 - Creating design review documentation

Structure:

# Design Review: {{Feature Name}}

**Date**: {{YYYY-MM-DD}}
**Figma**: [Link]({{figma_url}})
**Reviewer**: Navigator Product Design Skill

## New Design Tokens
[Token changes]

## New Components Required
[Component list with categories]

## Design System Impact
[High/Medium/Low impact analysis]

## Implementation Recommendations
[Phased approach]

---

Design System Documentation Structure

Initial Setup (First Run)

mkdir -p .agent/design-system/reviews

# Create initial files
touch .agent/design-system/design-tokens.json
touch .agent/design-system/ui-kit-inventory.json
touch .agent/design-system/component-mapping.json

design-tokens.json (DTCG format):

{
  "color": {},
  "spacing": {},
  "typography": {},
  "radius": {},
  "shadow": {}
}

ui-kit-inventory.json:

{
  "components": [
    {
      "name": "Button",
      "path": "src/components/ui/Button.tsx",
      "category": "atom",
      "variants": ["primary", "secondary", "ghost"],
      "figma_link": "..."
    }
  ],
  "tokens": {}
}

File Loading Strategy

Never load:

• All design review reports (50+ files = 250k+ tokens)
• Full Figma MCP responses (can be 350k+ tokens)

Always load when skill active:

• ui-kit-inventory.json (~3k tokens)
• design-tokens.json (~2k tokens)
• Specific design review for current task (~5k tokens)

Total: ~10k tokens vs 150k+ (93% reduction)

---

Figma MCP Integration

MCP Server Detection

On skill invocation:

1. Check for Figma MCP tools availability
2. Detect local vs remote server
3. Adjust workflow based on capabilities

Local Server (Recommended):

• URL: http://127.0.0.1:3845/mcp
• Tools: All (metadata, variables, code_connect, design_context)
• Requires: Figma Desktop app running

Remote Server (Fallback):

• URL: https://mcp.figma.com/mcp
• Tools: Limited (no code_connect, requires explicit URLs)
• Requires: Internet connection, explicit Figma links

Handling Token Limits

Problem: Large screens return >350k tokens (exceeds default 25k limit)

Solution:

1. Use `get_metadata` first (sparse XML, ~5k tokens)
2. Parse metadata to identify component node IDs
3. Fetch components individually via `get_design_context`
4. Aggregate results from multiple small calls

**Environment Variable** (recommended):
export MAX_MCP_OUTPUT_TOKENS=100000

MCP Tool Usage

get_metadata: Always first for large designs

• Returns sparse XML with node IDs, types, names
• Low token cost (~5-10k)
• Use to plan component extraction strategy

get_variable_defs: Extract all design tokens

• One call gets all variables
• Moderate token cost (~10-20k)
• Critical for token extraction

get_code_connect_map: Get component mappings

• Requires Figma Enterprise plan
• Returns node_id → code_path mappings
• Highest confidence mappings

get_design_context: Extract component code

• Use per-component (NOT full screen)
• Can generate React/Vue/HTML via prompting
• Highest token cost - use sparingly

---

Tailwind CSS Integration

Design Tokens → Tailwind @theme

Style Dictionary Pipeline:

# 1. Tokens extracted to design-tokens.json (DTCG format)
# 2. Run Style Dictionary build
npx style-dictionary build

# 3. Generates tailwind-tokens.css
# @theme {
#   --color-primary-500: #3B82F6;
#   --spacing-md: 16px;
# }

# 4. Tailwind auto-generates utilities
# .bg-primary-500, .p-md, etc.

Figma Auto Layout → Tailwind Classes

Translation Rules (apply during code generation):

Direction:
  Horizontal → flex-row
  Vertical → flex-col

Spacing:
  Gap → gap-{token}
  Padding → p-{token}, px-{token}, py-{token}

Alignment:
  Start → items-start, justify-start
  Center → items-center, justify-center
  Space Between → justify-between

Sizing:
  Hug → w-auto / h-auto
  Fill → flex-1
  Fixed → w-{value} / h-{value}

---

Token Optimization

Navigator Principles

Load on demand:

• Design review for current task only
• UI kit inventory (always needed)
• Design tokens (always needed)

Use Task agent for codebase searches:

• Finding all component files (60-80% token savings)
• Searching for token usage in Tailwind config
• Analyzing component variant patterns

Compact after completion:

• Clear context after design review
• Preserve task document in marker
• Clean slate for implementation

---

Troubleshooting

"Figma MCP tool not found"

Issue: MCP server not available

Solutions:

1. Check Figma Desktop app is running (for local server)
2. Verify MCP server added: claude mcp add --transport http figma-desktop http://127.0.0.1:3845/mcp
3. Fall back to manual workflow (still provides value)

"Token limit exceeded"

Issue: get_design_context response too large

Solutions:

1. Use get_metadata first, then fetch components individually
2. Set MAX_MCP_OUTPUT_TOKENS=100000
3. Break design into smaller selections in Figma

"No components found in codebase"

Issue: component_mapper.py finds no matches

Solutions:

1. Check --project-root points to correct directory
2. Verify component file extensions (tsx, jsx, vue)
3. Check components aren't in excluded directories (node_modules)

"Design tokens not in DTCG format"

Issue: Existing tokens use legacy format

Solutions:

1. Run token_extractor.py with --format tokens-only to convert
2. Backup existing tokens first
3. Update Style Dictionary config to read DTCG format

---

Success Metrics

Efficiency Gains

Before: 6-10 hours per design handoff After: 15-20 minutes Savings: 95% time reduction

Quality Metrics

• Design system drift detected automatically
• 100% token consistency via automated sync
• Component reuse rate tracked
• Implementation accuracy via acceptance criteria

---

Example Usage

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

Navigator:
1. Checks for Figma MCP availability
2. Extracts metadata, variables, code_connect_map
3. Runs design_analyzer.py → finds 3 new components, 12 new tokens
4. Runs token_extractor.py → generates DTCG tokens, finds 5 drift issues
5. Runs component_mapper.py → maps 2 components, 1 new needed
6. Runs design_system_auditor.py → priority: HIGH (drift detected)
7. Runs implementation_planner.py → generates TASK-17 with 3 phases
8. Creates design review report
9. Presents summary with [Start/Review/Modify] options

User: "Start implementation"

Navigator:
1. Loads TASK-17 document
2. Begins Phase 1: Design Tokens
3. Updates design-tokens.json with 12 new tokens
4. Runs Style Dictionary build
5. Updates Tailwind config
6. Commits changes
7. Moves to Phase 2: StatBadge component
8. ... continues through all phases
9. Autonomous completion when done

---

Last Updated: 2025-10-21 Navigator Version: 3.2.0 (target) Skill Version: 1.0.0