
agent-browser
by XcluEzy7
AG4ONE - Unified agentic engineering workflow combining GWD methodology, Serena semantic analysis, and Ralph autonomous looping for next-generation AI pair programming across multiple platforms
SKILL.md
name: agent-browser description: Agent Browser Skill
Agent Browser Skill
Overview
Agent Browser is a headless browser automation CLI designed specifically for AI agents. It provides fast, deterministic browser control with an AI-friendly interface using element references (refs) for reliable interaction.
Key Features
- AI-First Design: Uses accessibility tree with refs for deterministic element selection
- Fast Performance: Native Rust CLI with Node.js daemon architecture
- Comprehensive: 50+ commands for complete browser automation
- Cross-Platform: Works on macOS, Linux, Windows
- Session Management: Multiple isolated browser instances
- JSON Output: Machine-readable responses for AI integration
Installation
npm install -g agent-browser
Core Workflow
1. Basic AI Agent Pattern
# Navigate to page
agent-browser open example.com
# Get accessibility tree with refs (AI-friendly)
agent-browser snapshot -i --json
# Parse output to identify target refs
# Example output:
# - heading "Welcome" [ref=e1]
# - link "Click me" [ref=e2]
# - input "Email" [ref=e3]
# Execute actions using refs
agent-browser click @e2
agent-browser fill @e3 "user@example.com"
# Get updated snapshot after page changes
agent-browser snapshot -i --json
# Close browser
agent-browser close
2. Traditional Selectors (Alternative)
# CSS selectors
agent-browser click "#submit-button"
agent-browser fill "#email-input" "user@example.com"
# Semantic locators
agent-browser find role button click --name "Submit"
agent-browser find label "Email" fill "user@example.com"
Essential Commands
Navigation
agent-browser open <url> # Navigate to URL
agent-browser back # Go back
agent-browser forward # Go forward
agent-browser reload # Reload page
agent-browser close # Close browser
Page Interaction
agent-browser click <selector> # Click element
agent-browser fill <sel> <text> # Clear and fill input
agent-browser type <sel> <text> # Type without clearing
agent-browser press <key> # Press key (Enter, Tab, etc)
agent-browser scroll <dir> [px] # Scroll direction
agent-browser screenshot [path] # Take screenshot
Information Gathering
agent-browser snapshot # Get accessibility tree with refs
agent-browser get text <sel> # Get element text
agent-browser get html <sel> # Get innerHTML
agent-browser get title # Get page title
agent-browser get url # Get current URL
State Checking
agent-browser is visible <sel> # Check if visible
agent-browser is enabled <sel> # Check if enabled
agent-browser wait <selector> # Wait for element
agent-browser wait --text "Welcome" # Wait for text to appear
Integration Patterns
With AG4ONE Workflow
- Discovery Phase: Use browser automation to research web applications
- Requirements Gathering: Extract data from web forms and interfaces
- Testing: Automate browser-based testing workflows
- Documentation: Capture screenshots and gather information
Error Handling
# Check if element exists before interaction
if agent-browser is visible @e5 --json | grep -q "true"; then
agent-browser click @e5
else
echo "Element not found"
fi
# Wait for page load before proceeding
agent-browser wait --load networkidle
agent-browser wait --text "Page loaded"
Session Management
# Save authentication state
agent-browser state save auth-state.json
# Load state in new session
agent-browser open example.com
agent-browser state load auth-state.json
Best Practices
- Always use refs from snapshot for reliable interaction
- Wait for page stability before taking actions
- Use JSON output for machine-readable responses
- Save state when dealing with authentication
- Handle errors gracefully with state checking
- Close browsers to free resources
Advanced Features
Network Control
# Block requests
agent-browser network route "ads/*" --abort
# Mock responses
agent-browser network route "/api/data" --body '{"mock": true}'
Device Emulation
# Mobile device
agent-browser set device "iPhone 14"
# Custom viewport
agent-browser set viewport 375 812
Multi-tab Management
agent-browser tab new https://example.com
agent-browser tab 1 # Switch to second tab
agent-browser tab close 1
Example Use Cases
Web Form Automation
agent-browser open "https://example.com/login"
agent-browser snapshot -i --json
# Parse refs for username, password, submit
agent-browser fill @e3 "username"
agent-browser fill @e4 "password"
agent-browser click @e5
agent-browser wait --text "Dashboard"
Data Extraction
agent-browser open "https://example.com/data"
agent-browser snapshot -i --json
# Extract all links with specific text
agent-browser get text "[data-row]" --json
Visual Testing
agent-browser open "https://example.com"
agent-browser set viewport 1920 1080
agent-browser screenshot desktop-view.png
agent-browser set device "iPhone 14"
agent-browser screenshot mobile-view.png
Troubleshooting
Common Issues
- Element not found: Use updated snapshot after page changes
- Timing issues: Add explicit waits before interactions
- Selector conflicts: Use unique refs from snapshot
- Authentication: Save and load state for logged-in sessions
Debug Commands
agent-browser highlight @e5 # Highlight element
agent-browser console # View console messages
agent-browser errors # View page errors
This skill enables AI agents to reliably interact with web browsers for testing, automation, and data gathering tasks within the AG4ONE workflow.
Score
Total Score
Based on repository quality metrics
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
3ヶ月以内に更新がある
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
Reviews
Reviews coming soon