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agent-browser

0.0
D

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Marketplace
#Automates browser#references#web testing#templates#Automates#web#form filling#form

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Installation for Agentic Skill

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skilz install vercel-labs/agent-browser/agent-browser
skilz install vercel-labs/agent-browser/agent-browser --agent opencode
skilz install vercel-labs/agent-browser/agent-browser --agent codex
skilz install vercel-labs/agent-browser/agent-browser --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding agents

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Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/vercel-labs/agent-browser
2. Copy the agent skill directory:
cp -r agent-browser/skills/agent-browser ~/.claude/skills/

Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Repository
agent-browser
Type
Other
Meta-Domain
N/A
Primary Domain
N/A
Market Score
0.0

Agent Skill Grade

D
Score: 66/100 Click to see breakdown

Score Breakdown

Spec Compliance
12/15
PDA Architecture
20/30
Ease of Use
17/25
Writing Style
8/10
Utility
11/20
Modifiers: -2

Recommendations

  • Focus on improving Utility (currently 11/20)
  • Add trigger phrases to description for discoverability
  • Add table of contents for files over 100 lines

Graded: 1/23/2026

Developer Feedback

I was curious about your browser automation approach, but the missing README made it hard to understand the actual use cases—are you targeting web testing, data extraction, or something else entirely?

Links:

TL;DR

You're at 66/100, solidly in D territory—needs work before this is production-ready. The evaluation uses Anthropic's skill best practices, and your strongest area is Spec Compliance (12/15), which means your YAML frontmatter is solid. The weak spot is Utility (11/20) by a fair margin—your skill does the plumbing but doesn't show enough of why someone would use it or what they can actually do with it.

What's Working Well

  • Clean frontmatter: Your YAML metadata is valid and hits all required fields—no errors there
  • Proper naming: agent-browser follows hyphen-case conventions correctly
  • Structured references: You've got 5 reference files (authentication, proxy-support, video-recording, snapshot-refs, session-management) which shows you're thinking about layered documentation
  • Good examples: You've got code blocks sprinkled through, which nets a +2 bonus for exemplary examples

The Big One: Utility is Your Blocker (11/20 → Target 18+)

This is the killer. Your skill describes what it does but barely shows why I'd use it or what I can build with it. You've got 1/4 points for feedback loops and only 5/8 for problem-solving power.

The fix: Add concrete use-case sections to your main SKILL.md. Right now it's too API-reference-heavy. You need something like:

## Common Use Cases

- **Automated testing**: Verify UI behavior across browsers
- **Data extraction**: Scrape dynamic content that requires JavaScript
- **Screenshot monitoring**: Compare visual changes over time
- **Form testing**: Validate complex multi-step workflows

Then add 2-3 minimal working examples showing real problems solved. This alone could bump you from 11 to 15+ on Utility.

Other Things Worth Fixing

  1. Trigger phrases (Description Quality: 3/4) – You've got "1-2 trigger phrases" but LLM-based search needs 4-5. Add: "web automation", "browser testing", "screenshot", "visual regression", "headless browser"

  2. Navigation signals (PDA: 3/5) – Your SKILL.md is 1496 words but lacks a clear table of contents. Add headers with anchor links so users can jump to sections like "Getting Started", "Advanced Configuration", "Examples"

  3. Metadata quality (Ease of Use: 6/10) – You're missing optional fields. Add tags, categories, and requirements to your frontmatter for better discoverability in the marketplace

  4. Feedback loops (Utility: 1/4) – Zero validation keywords detected. Show what success looks like: "Expected output", "Common errors & fixes", "How to verify it works"

Quick Wins

  • Add 4-5 trigger phrases to your description (+1-2 points, 5 min)
  • Create a TOC with anchor links in SKILL.md (+2 points, 10 min)
  • Write 2-3 use-case examples with expected output (+4-5 points, 30 min)
  • Add optional metadata fields (tags, categories) (+1-2 points, 5 min)

These four changes could realistically get you to 76-78/100, solidly in C territory. Focus on Utility first—it's your biggest gap and the most impactful lever to pull.


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AI-Detected Topics

Extracted using NLP analysis

Automates browser references web testing templates Automates web form filling form screenshots web pages

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