roberto-mello/lavra/agent-browser

Claude Code and agent skill by roberto-mello/lavra

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Browser automation via Vercel's agent-browser CLI. Use when browsing websites, filling forms, taking screenshots, or scraping data.

Install

npx vskill@latest install roberto-mello/lavra/agent-browser

SKILL.md

agent-browser: CLI Browser Automation

Vercel's headless browser automation CLI designed for AI agents. Uses ref-based selection (@e1, @e2) from accessibility snapshots.

Setup Check

bash
# Check installation
command -v agent-browser >/dev/null 2>&1 && echo "Installed" || echo "NOT INSTALLED - run: npm install -g agent-browser && agent-browser install"
Install if needed
bash
npm install -g agent-browser
agent-browser install  # Downloads Chromium

Core Workflow

The snapshot + ref pattern is optimal for LLMs:

  1. Navigate to URL
  2. Snapshot to get interactive elements with refs
  3. Interact using refs (@e1, @e2, etc.)
  4. Re-snapshot after navigation or DOM changes
bash
# Step 1: Open URL
agent-browser open https://example.com
 
# Step 2: Get interactive elements with refs
agent-browser snapshot -i --json
 
# Step 3: Interact using refs
agent-browser click @e1
agent-browser fill @e2 "search query"
 
# Step 4: Re-snapshot after changes
agent-browser snapshot -i

Key Commands

Navigation
bash
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
Snapshots (Essential for AI)
bash
agent-browser snapshot              # Full accessibility tree
agent-browser snapshot -i           # Interactive elements only (recommended)
agent-browser snapshot -i --json    # JSON output for parsing
agent-browser snapshot -c           # Compact (remove empty elements)
agent-browser snapshot -d 3         # Limit depth
Interactions
bash
agent-browser click @e1                    # Click element
agent-browser dblclick @e1                 # Double-click
agent-browser fill @e1 "text"              # Clear and fill input
agent-browser type @e1 "text"              # Type without clearing
agent-browser press Enter                  # Press key
agent-browser hover @e1                    # Hover element
agent-browser check @e1                    # Check checkbox
agent-browser uncheck @e1                  # Uncheck checkbox
agent-browser select @e1 "option"          # Select dropdown option
agent-browser scroll down 500              # Scroll (up/down/left/right)
agent-browser scrollintoview @e1           # Scroll element into view
Get Information
bash
agent-browser get text @e1          # Get element text
agent-browser get html @e1          # Get element HTML
agent-browser get value @e1         # Get input value
agent-browser get attr href @e1     # Get attribute
agent-browser get title             # Get page title
agent-browser get url               # Get current URL
agent-browser get count "button"    # Count matching elements
Screenshots & PDFs
bash
agent-browser screenshot                      # Viewport screenshot
agent-browser screenshot --full               # Full page
agent-browser screenshot output.png           # Save to file
agent-browser screenshot --full output.png    # Full page to file
agent-browser pdf output.pdf                  # Save as PDF
Wait
bash
agent-browser wait @e1              # Wait for element
agent-browser wait 2000             # Wait milliseconds
agent-browser wait "text"           # Wait for text to appear

Semantic Locators (Alternative to Refs)

bash
agent-browser find role button click --name "Submit"
agent-browser find text "Sign up" click
agent-browser find label "Email" fill "user@example.com"
agent-browser find placeholder "Search..." fill "query"

Sessions (Parallel Browsers)

bash
# Run multiple independent browser sessions
agent-browser --session browser1 open https://site1.com
agent-browser --session browser2 open https://site2.com
 
# List active sessions
agent-browser session list

Examples

Login Flow
bash
agent-browser open https://app.example.com/login
agent-browser snapshot -i
# Output shows: textbox "Email" [ref=e1], textbox "Password" [ref=e2], button "Sign in" [ref=e3]
agent-browser fill @e1 "user@example.com"
agent-browser fill @e2 "password123"
agent-browser click @e3
agent-browser wait 2000
agent-browser snapshot -i  # Verify logged in
Search and Extract
bash
agent-browser open https://news.ycombinator.com
agent-browser snapshot -i --json
# Parse JSON to find story links
agent-browser get text @e12  # Get headline text
agent-browser click @e12     # Click to open story
Form Filling
bash
agent-browser open https://forms.example.com
agent-browser snapshot -i
agent-browser fill @e1 "John Doe"
agent-browser fill @e2 "john@example.com"
agent-browser select @e3 "United States"
agent-browser check @e4  # Agree to terms
agent-browser click @e5  # Submit button
agent-browser screenshot confirmation.png
Debug Mode
bash
# Run with visible browser window
agent-browser --headed open https://example.com
agent-browser --headed snapshot -i
agent-browser --headed click @e1

JSON Output

Add --json for structured output:

bash
agent-browser snapshot -i --json

Returns:

json
{
  "success": true,
  "data": {
    "refs": {
      "e1": {"name": "Submit", "role": "button"},
      "e2": {"name": "Email", "role": "textbox"}
    },
    "snapshot": "- button \"Submit\" [ref=e1]\n- textbox \"Email\" [ref=e2]"
  }
}

vs Playwright MCP

Featureagent-browser (CLI)Playwright MCP
InterfaceBash commandsMCP tools
SelectionRefs (@e1)Refs (e1)
OutputText/JSONTool responses
ParallelSessionsTabs
Best forQuick automationTool integration

Use agent-browser when:

  • You prefer Bash-based workflows
  • You want simpler CLI commands
  • You need quick one-off automation

Use Playwright MCP when:

  • You need deep MCP tool integration
  • You want tool-based responses
  • You're building complex automation

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

See the full security report

Quality evaluation

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Source

Popularity

Repository stars
47
Repository forks
2
7-day trend score
19

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