Azsdk Common Pipeline Troubleshooting
Azure/azure-sdk-for-android
Diagnose and resolve failures in Azure SDK CI and generation pipelines.
Automates design review, token extraction, component mapping, and implementation planning.
$ npx skills add qf-studio/navigator --skill product-design -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install qf-studio/navigator product-design --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "product-design" agent skill from https://github.com/qf-studio/navigator/tree/main/skills/product-design into .claude/skills/product-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-design", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/qf-studio/navigator/tree/main/skills/product-designType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add qf-studio/navigator --skill product-design -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install qf-studio/navigator product-design --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/product-design .agents/skills/product-design && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "product-design" agent skill from https://github.com/qf-studio/navigator/tree/main/skills/product-design into .agents/skills/product-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-design", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add qf-studio/navigator --skill product-design -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install qf-studio/navigator product-design --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/product-design .cursor/skills/product-design && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "product-design" agent skill from https://github.com/qf-studio/navigator/tree/main/skills/product-design into .cursor/skills/product-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-design", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/qf-studio/navigator.git --path skills/product-design--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add qf-studio/navigator --skill product-design -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install qf-studio/navigator product-design --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/product-design .gemini/skills/product-design && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "product-design" agent skill from https://github.com/qf-studio/navigator/tree/main/skills/product-design into .gemini/skills/product-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-design", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install qf-studio/navigator product-designInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add qf-studio/navigator --skill product-design -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/product-design .github/skills/product-design && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "product-design" agent skill from https://github.com/qf-studio/navigator/tree/main/skills/product-design into .github/skills/product-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-design", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add qf-studio/navigator --skill product-design -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install qf-studio/navigator product-design --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qf-studio/navigator.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/product-design .opencode/skills/product-design && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "product-design" agent skill from https://github.com/qf-studio/navigator/tree/main/skills/product-design into .opencode/skills/product-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-design", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
product-designAutomates 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. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 3bb9eac. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditGrepGlobBashTaskTodoWriteFrom allowed-tools in the SKILL.md frontmatter.
Ships script files (Python and Shell), which the agent can run.
Shell commands in SKILL.md call:
python3npxclaudeFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
figma.commcp.figma.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash, Task, TodoWriteAutomated 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.
The full file from qf-studio/navigator at commit 3bb9eac, republished under its MIT licence (© qf-studio). 1,167 words, ~5,057 tokens.
.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.Automate design handoff from Figma to code with design system intelligence. Extract tokens, map components, detect drift, generate implementation plans.
Auto-invoke when user says:
5-Step Workflow:
Time Savings: 6-10 hours → 15-20 minutes (95% reduction)
Python Dependencies
cd skills/product-design
./setup.sh # Automated installation
# OR manually: pip install -r requirements.txtFigma Desktop (for automated workflow)
Project Structure
.agent/design-system/ directory (created on first run)Quick start:
cd skills/product-design
./setup.shSee INSTALL.md for detailed installation guide and troubleshooting.
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
fiIf setup missing:
If setup complete:
Objective: Extract design patterns from Figma or manual description
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):
python3 functions/design_analyzer.py --figma-url <URL>Benefits:
Requirements:
./setup.sh)**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# 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.jsonAnalysis Output:
Objective: Compare design vs implementation, detect drift
python3 functions/token_extractor.py \
--figma-variables /tmp/figma_variables.json \
--existing-tokens .agent/design-system/design-tokens.json \
--output /tmp/token_extraction.jsonOutput: DTCG formatted tokens + diff summary
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.jsonOutput: Figma component → code component mappings with confidence scores
# 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.jsonAudit Results:
Objective: Generate phased implementation 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}}.mdTask Document Includes:
Use template: templates/design-review-report.md
Save to: .agent/design-system/reviews/YYYY-MM-DD-{{feature-name}}.md
Contents:
Objective: Create task and assign context for implementation
**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 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 phasesObjective: Present results and get user decision
✅ 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"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 implementationPurpose: 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.jsonInput 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
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.jsonOutput Formats:
full: DTCG tokens + diff + summarytokens-only: Just DTCG tokensdiff-only: Just diff and summaryDTCG Format (W3C Design Tokens spec):
{
"color": {
"primary": {
"500": {
"$value": "#3B82F6",
"$type": "color",
"$description": "Primary brand color"
}
}
}
}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.jsonMapping Strategy:
Output: Mappings with confidence scores + variant prop mapping
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.jsonAudit Checks:
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.mdOutput: Complete Navigator task document with:
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]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.jsondesign-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": {}
}Never load:
Always load when skill active:
ui-kit-inventory.json (~3k tokens)design-tokens.json (~2k tokens)Total: ~10k tokens vs 150k+ (93% reduction)
On skill invocation:
Local Server (Recommended):
http://127.0.0.1:3845/mcpRemote Server (Fallback):
https://mcp.figma.com/mcpProblem: 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=100000get_metadata: Always first for large designs
get_variable_defs: Extract all design tokens
get_code_connect_map: Get component mappings
get_design_context: Extract component code
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.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}Load on demand:
Use Task agent for codebase searches:
Compact after completion:
Issue: MCP server not available
Solutions:
claude mcp add --transport http figma-desktop http://127.0.0.1:3845/mcpIssue: get_design_context response too large
Solutions:
get_metadata first, then fetch components individuallyMAX_MCP_OUTPUT_TOKENS=100000Issue: component_mapper.py finds no matches
Solutions:
--project-root points to correct directoryIssue: Existing tokens use legacy format
Solutions:
token_extractor.py with --format tokens-only to convertBefore: 6-10 hours per design handoff After: 15-20 minutes Savings: 95% time reduction
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 doneLast 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
SKILL.md and 14 other files in skills/product-design of qf-studio/navigator.
Open the folder on GitHubat commit 3bb9eac
Product Design next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Product Design this skillqf-studio/navigator | 355 | — | ~5.1k | Automated safety check: Notes | MIT | |
| Azsdk Common Pipeline TroubleshootingAzure/azure-sdk-for-android | 121 | — | ~496 | Automated safety check: Pass | MIT | |
| FcpxmlDareDev256/fcp-mcp-server | 121 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Designohad6k/emulo | 294 | 1 repos | ~209 | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT |
Azure/azure-sdk-for-android
Diagnose and resolve failures in Azure SDK CI and generation pipelines.
DareDev256/fcp-mcp-server
Work with Final Cut Pro timelines and .fcpxml/.fcpxmld files.
ohad6k/emulo
A skill your agent uses for UI, UX, visual hierarchy, frontend-design judgment, references, redesigns, and design critique when the user's Emulo taste should guide the task.
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
MemPalace/mempalace
Installs and configures MemPalace as a private local palace, a shared-brain hub or a client of an existing hub, including MCP registration and version-correct initialization.
qf-studio/navigator
Load Navigator documentation navigator when starting development session, resuming work, or beginning new feature.
qf-studio/navigator
Create REST/GraphQL API endpoint with validation, error handling, and tests.
qf-studio/navigator
Generate backend tests (unit, integration, mocks) for existing code.
qf-studio/navigator
Create database migration with schema changes and rollback. An agent skill from qf-studio/navigator.
qf-studio/navigator
Create React/Vue component with TypeScript, tests, and styles.
qf-studio/navigator
Generate frontend component tests (React Testing Library, Vue Test Utils, snapshot) for existing components.
Works with
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.
Product Design fits situations like: mentions design review; tasks that involve Design review and critique.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.