export default product-design

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