browser-debugging

# Browser Debugging

This Skill provides comprehensive browser-based UI testing, visual analysis, and debugging capabilities using Claude-in-Chrome Extension MCP tools and optional external vision models via Claudish.

## When to Use This Skill

Claude and agents (developer, reviewer, tester, ui-developer) should invoke this Skill when:

- **Validating Own Work**: After implementing UI features, agents should verify their work in a real browser
- **Design Fidelity Checks**: Comparing implementation screenshots against design references
- **Visual Regression Testing**: Detecting layout shifts, styling issues, or visual bugs
- **Console Error Investigation**: User reports console errors or warnings
- **Form/Interaction Testing**: Verifying user interactions work correctly
- **Pre-Commit Verification**: Before committing or deploying code
- **Bug Reproduction**: User describes UI bugs that need investigation

## Prerequisites

### Required: Claude-in-Chrome Extension

This skill requires Claude-in-Chrome Extension MCP. The extension provides browser automation tools directly through Claude.

**Check if available**:
The tools are available when the extension is installed and active. Look for `mcp__claude-in-chrome__*` tools in your available MCP tools.

### Optional: External Vision Models (via OpenRouter)

For advanced visual analysis, use external vision-language models via Claudish:

```bash
# Check OpenRouter API key
[[ -n "${OPENROUTER_API_KEY}" ]] && echo "OpenRouter configured" || echo "Not configured"

# Install claudish
npm install -g claudish
```

---

## Visual Analysis Models (Recommended)

For best visual analysis of UI screenshots, use these models via Claudish:

### Tier 1: Best Quality (Recommended for Design Validation)

| Model | Strengths | Cost | Best For |
|-------|-----------|------|----------|
| **qwen/qwen3-vl-32b-instruct** | Best OCR, spatial reasoning, GUI automation, 32+ languages | ~$0.06/1M input | Design fidelity, OCR, element detection |
| **google/gemini-2.5-flash** | Fast, excellent price/performance, 1M context | ~$0.05/1M input | Real-time validation, large pages |
| **openai/gpt-4o** | Most fluid multimodal, strong all-around | ~$0.15/1M input | Complex visual reasoning |

### Tier 2: Fast & Affordable

| Model | Strengths | Cost | Best For |
|-------|-----------|------|----------|
| **qwen/qwen3-vl-30b-a3b-instruct** | Good balance, MoE architecture | ~$0.04/1M input | Quick checks, multiple iterations |
| **google/gemini-2.5-flash-lite** | Ultrafast, very cheap | ~$0.01/1M input | High-volume testing |

### Tier 3: Free Options

| Model | Notes |
|-------|-------|
| **openrouter/polaris-alpha** | FREE, good for testing workflows |

### Model Selection Guide

```
Design Fidelity Validation → qwen/qwen3-vl-32b-instruct (best OCR & spatial)
Quick Smoke Tests → google/gemini-2.5-flash (fast & cheap)
Complex Layout Analysis → openai/gpt-4o (best reasoning)
High Volume Testing → google/gemini-2.5-flash-lite (ultrafast)
Budget Conscious → openrouter/polaris-alpha (free)
```

---

## Recipe 1: Agent Self-Validation (After Implementation)

**Use Case**: Developer/UI-Developer agent validates their own work after implementing a feature.

### Pattern: Implement → Screenshot → Analyze → Report

```markdown
## After Implementing UI Feature

1. **Save file changes** (Edit tool)

2. **Capture implementation screenshot**:
   \`\`\`
   mcp__claude-in-chrome__navigate(url: "http://localhost:5173/your-route")
   # Wait for page load
   mcp__claude-in-chrome__computer(action: "screenshot")
   \`\`\`

3. **Analyze with embedded Claude** (always available):
   - Describe what you see in the screenshot
   - Check for obvious layout issues
   - Verify expected elements are present

4. **Optional: Enhanced analysis with vision model**:
   \`\`\`bash
   # Use Qwen VL for detailed visual analysis
   npx claudish --model qwen/qwen3-vl-32b-instruct --stdin --quiet <<EOF
   Analyze this UI screenshot and identify any visual issues:

   IMAGE: [screenshot from previous step]

   Check for:
   - Layout alignment issues
   - Spacing inconsistencies
   - Typography problems (font sizes, weights)
   - Color contrast issues
   - Missing or broken elements
   - Responsive design problems

   Provide specific, actionable feedback.
   EOF
   \`\`\`

5. **Check console for errors**:
   \`\`\`
   mcp__claude-in-chrome__read_console_messages()
   # Filter for errors in response
   \`\`\`

6. **Check network for failures**:
   \`\`\`
   mcp__claude-in-chrome__read_network_requests()
   # Look for failed requests (status >= 400)
   \`\`\`

7. **Report results to orchestrator**
```

### Quick Self-Check (5-Point Validation)

Agents should perform this quick check after any UI implementation:

```markdown
## Quick Self-Validation Checklist

□ 1. Screenshot shows expected UI elements
□ 2. No console errors (check: mcp__claude-in-chrome__read_console_messages)
□ 3. No network failures (check: mcp__claude-in-chrome__read_network_requests)
□ 4. Interactive elements respond correctly
□ 5. Visual styling matches expectations
```

---

## Recipe 2: Design Fidelity Validation

**Use Case**: Compare implementation against Figma design or design reference.

### Pattern: Design Reference → Implementation → Visual Diff

```markdown
## Design Fidelity Check

### Step 1: Capture Implementation
\`\`\`
mcp__claude-in-chrome__navigate(url: "http://localhost:5173/component")
mcp__claude-in-chrome__resize_window(width: 1440, height: 900)
mcp__claude-in-chrome__computer(action: "screenshot")
\`\`\`

### Step 2: Visual Analysis with Vision Model

\`\`\`bash
npx claudish --model qwen/qwen3-vl-32b-instruct --stdin --quiet <<EOF
Compare these two UI screenshots and identify design fidelity issues:

DESIGN REFERENCE: /tmp/design-reference.png
IMPLEMENTATION: [screenshot from step 1]

Analyze and report differences in:

## Colors & Theming
- Background colors (exact hex values)
- Text colors (headings, body, muted)
- Borde