Agent skill

Product Design

by qf-studio in qf-studio/navigator

Automates design review, token extraction, component mapping, and implementation planning.

MITAuto-check: notesAgent Workflows

Install Product Design

skills CLI
$ npx skills add qf-studio/navigator --skill product-design -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install qf-studio/navigator product-design --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/product-design .claude/skills/product-design && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
product-design
GitHub stars
355
Token cost
~5.1k tokens
SKILL.md length
1,167 words
Files
15
Skills in repo
32
Repo updated
First seen
Licence
MIT

At a glance

Automates design review, token extraction, component mapping, and implementation planning.

  • Works in 6 steps: Check Setup (Auto-Run) → Design Analysis → Codebase Audit → …
  • Mentions design review
  • SKILL.md covers When to Invoke, What This Does, Prerequisites and Workflow Protocol, plus 5 more sections
  • Runs Python and Shell scripts from its folder; calls python3, npx and claude; reaches figma.com and mcp.figma.com

What it does

Product Design is an agent skill from qf-studio/navigator. 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.

Its SKILL.md is about 5.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files (for example `GETTING-STARTED.md`, `INSTALL.md` and `README.md`).

It sits in Agent Workflows, covering Design review and critique. It works with Figma, Model Context Protocol and Python. The repository describes itself as: Finish What You Start — Context engineering for Claude Code. Sessions last 20+ exchanges instead of crashing at 7. The licence is MIT.

When your agent uses it

  • Mentions design review
  • Tasks that involve Design review and critique

Example prompts

  • “Use the product-design skill to automate design review, token extraction, component mapping, and implementation planning”
  • “/product-design”

Requirements

  • Python 3
  • A Bash shell
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash, Task, TodoWrite

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Check Setup (Auto-Run)
  2. Design Analysis
  3. Codebase Audit
  4. Implementation Planning
  5. Task Assignment
  6. Implementation Handoff

What it can do on your machine

Read from SKILL.md and the folder at commit 3bb9eac. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash
    • Task
    • TodoWrite

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • npx
    • claude

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • figma.com
    • mcp.figma.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Design loads about 5.1k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 1,167 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~5.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Grep, Glob, Bash, Task, TodoWrite

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from qf-studio/navigator at commit 3bb9eac, republished under its MIT licence (© qf-studio). 1,167 words, ~5,057 tokens.

Download SKILL.mdSave it as .claude/skills/product-design/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
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

    bash
    cd skills/product-design
    ./setup.sh  # Automated installation
    # OR manually: pip install -r requirements.txt
  2. Figma Desktop (for automated workflow)

  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:

bash
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:

bash
# 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
# 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)
markdown
**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
bash
# 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
bash
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
bash
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
bash
# 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
bash
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)
markdown
**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
markdown
**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
markdown
✅ 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":

markdown
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]:

markdown
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]:

markdown
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:

bash
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):

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:

bash
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):

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

functions/component_mapper.py

Purpose: Map Figma components to codebase components

Usage:

bash
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:

bash
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:

bash
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:

markdown
# 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)
bash
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):

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

ui-kit-inventory.json:

json
{
  "components": [
    {
      "name": "Button",
      "path": "src/components/ui/Button.tsx",
      "category": "atom",
      "variants": ["primary", "secondary", "ghost"],
      "figma_link": "..."
    }
  ],
  "tokens": {}
}
Show full SKILL.md (477 more words)Show less
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:

markdown
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:

bash
# 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

© qf-studio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 14 other files in skills/product-design of qf-studio/navigator.

  • SKILL.md
  • GETTING-STARTED.md
  • INSTALL.md
  • README.md
  • examples/dashboard-redesign-review.md
  • functions/check_mcp_connection.py
  • functions/component_mapper.py
  • functions/design_analyzer.py
  • functions/design_system_auditor.py
  • functions/figma_mcp_client.py
  • functions/implementation_planner.py
  • functions/token_extractor.py
  • requirements.txt
  • setup.sh
  • templates/design-review-report.md

Open the folder on GitHubat commit 3bb9eac

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More from qf-studio/navigator

All 32 skills in this repo
  • Nav Start

    qf-studio/navigator

    Load Navigator documentation navigator when starting development session, resuming work, or beginning new feature.

    355 GitHub stars~4.7k tokensUpdated yesterday
    Auto-check: notes
  • Backend Endpoint

    qf-studio/navigator

    Create REST/GraphQL API endpoint with validation, error handling, and tests.

    355 GitHub stars~4.5k tokensUpdated yesterday
    Auto-check: notes
  • Backend Test

    qf-studio/navigator

    Generate backend tests (unit, integration, mocks) for existing code.

    355 GitHub stars~1.5k tokensUpdated yesterday
    Auto-check: notes
  • Database Migration

    qf-studio/navigator

    Create database migration with schema changes and rollback. An agent skill from qf-studio/navigator.

    355 GitHub stars~3.7k tokensUpdated yesterday
    Auto-check: notes
  • Frontend Component

    qf-studio/navigator

    Create React/Vue component with TypeScript, tests, and styles.

    355 GitHub stars~4.5k tokensUpdated yesterday
    Auto-check: notes
  • Frontend Test

    qf-studio/navigator

    Generate frontend component tests (React Testing Library, Vue Test Utils, snapshot) for existing components.

    355 GitHub stars~1.6k tokensUpdated yesterday
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Questions about Product Design

What does Product Design do?

Automates design review, token extraction, component mapping, and implementation planning. Product Design is an agent skill from qf-studio/navigator. Automates design review, token extraction, component mapping, and implementation planning.

When should I use Product Design?

Product Design fits situations like: mentions design review; tasks that involve Design review and critique.

How do I install Product Design in Claude Code?

Run `npx skills add qf-studio/navigator --skill product-design -a claude-code`. Or copy the skill folder (skills/product-design in qf-studio/navigator) into .claude/skills/product-design in your project. Claude Code loads it when a task matches its description.

How do I install Product Design in Codex?

Run `npx skills add qf-studio/navigator --skill product-design -a codex`. Or copy the skill folder (skills/product-design in qf-studio/navigator) into .agents/skills/product-design in your project. Codex loads it when a task matches its description.

Can I use Product Design in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qf-studio/navigator --skill product-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-design, .gemini/skills/product-design, .github/skills/product-design and .opencode/skills/product-design in your project.

What does Product Design need to run?

Going by SKILL.md and its folder, Product Design needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (python3, npx and claude). Our summary lists: Python 3; A Bash shell. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash, Task, TodoWrite.

Does Product Design access the network?

SKILL.md names 2 domains. In commands or code: figma.com and mcp.figma.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Product Design safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Product Design use?

Product Design is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Design use?

About 5.1k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Product Design?

Skills that share tags, products or a category with Product Design: Azsdk Common Pipeline Troubleshooting (Azure/azure-sdk-for-android, 121 stars), Fcpxml (DareDev256/fcp-mcp-server, 121 stars), Design (ohad6k/emulo, 294 stars) and MCP Server Builder (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Design?

qf-studio (a GitHub organization) maintains it in qf-studio/navigator, which has 355 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 8, 2026.

Source: qf-studio/navigator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.