Agent Browser Automation logo

Agent Browser Automation

Organization
reason-machines
agent-browser-automation

Headless browser automation CLI for AI agents using native Rust binary with Chrome DevTools Protocol

Overview

Publisherreason-machines
Repositorytrending-skills
Skill nameagent-browser-automation
Stars
80
Forks
15
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by reason-machines on GitHub. Read the source before you install it.

Installation

Install the Agent Browser Automation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/reason-machines/trending-skills.git /tmp/trending-skills
mkdir -p .claude/skills
cp -r /tmp/trending-skills/skills/agent-browser-automation .claude/skills/agent-browser-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Browser Automation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Browser Automation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Browser Automation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

agent-browser

Skill by ara.so — Daily 2026 Skills collection.

agent-browser is a headless browser automation CLI built in Rust, designed for AI agents. It wraps Chrome via the Chrome DevTools Protocol (CDP) and exposes a fast, ergonomic command-line interface for navigation, interaction, accessibility snapshots, screenshots, network interception, and more — with no Node.js or Playwright runtime required.

Installation

Recommended (npm global)

bash
npm install -g agent-browser
agent-browser install  # Download Chrome for Testing (first time only)

macOS (Homebrew)

bash
brew install agent-browser
agent-browser install

Rust / Cargo

bash
cargo install agent-browser
agent-browser install

Local project dependency

bash
npm install agent-browser
# Add to package.json scripts or invoke via npx

Linux (with system dependencies)

bash
agent-browser install --with-deps

Quick Start

bash
agent-browser open https://example.com
agent-browser snapshot                        # Accessibility tree with @refs (best for AI)
agent-browser click @e2                       # Click by ref from snapshot
agent-browser fill @e3 "hello@example.com"   # Fill by ref
agent-browser get text @e1                    # Get text content
agent-browser screenshot page.png
agent-browser close

Core Commands

Navigation

bash
agent-browser open <url>           # Navigate (aliases: goto, navigate)
agent-browser get url              # Get current URL
agent-browser get title            # Get page title
agent-browser close                # Close browser (aliases: quit, exit)

Accessibility Snapshot (recommended for AI agents)

bash
agent-browser snapshot             # Returns accessibility tree with @ref IDs
agent-browser snapshot -i          # Interactive / compact mode

Snapshot output includes @eN refs you can use directly:

@e1 [button] "Submit"
@e2 [textbox] "Email" value=""
@e3 [link] "Sign in"

Then act on them:

bash
agent-browser fill @e2 "user@example.com"
agent-browser click @e1

Interaction

bash
agent-browser click <sel>                     # Click element
agent-browser dblclick <sel>                  # Double-click
agent-browser fill <sel> <text>               # Clear and fill input
agent-browser type <sel> <text>               # Type into element
agent-browser press <key>                     # Press key (Enter, Tab, Control+a)
agent-browser keyboard type <text>            # Type at current focus (real keystrokes)
agent-browser keyboard inserttext <text>      # Insert text without key events
agent-browser hover <sel>                     # Hover element
agent-browser select <sel> <value>            # Select dropdown option
agent-browser check <sel>                     # Check checkbox
agent-browser uncheck <sel>                   # Uncheck checkbox
agent-browser scroll down 500                 # Scroll (up/down/left/right, optional px)
agent-browser scroll down --selector "#feed"  # Scroll within element
agent-browser scrollintoview <sel>            # Scroll element into view
agent-browser drag <src> <target>             # Drag and drop
agent-browser upload <sel> /path/file.pdf     # Upload file

Screenshots & PDF

bash
agent-browser screenshot                          # Save to temp dir, print path
agent-browser screenshot page.png                 # Save to path
agent-browser screenshot --full page.png          # Full-page screenshot
agent-browser screenshot --annotate               # Numbered element labels overlay
agent-browser screenshot --screenshot-dir ./shots # Custom output directory
agent-browser screenshot --screenshot-format jpeg --screenshot-quality 80
agent-browser pdf output.pdf                      # Save page as PDF

Getting Element Info

bash
agent-browser get text <sel>           # Text content
agent-browser get html <sel>           # innerHTML
agent-browser get value <sel>          # Input value
agent-browser get attr <sel> <attr>    # Attribute value
agent-browser get count <sel>          # Count matching elements
agent-browser get box <sel>            # Bounding box
agent-browser get styles <sel>         # Computed styles
agent-browser get cdp-url              # CDP WebSocket URL

State Checks

bash
agent-browser is visible <sel>
agent-browser is enabled <sel>
agent-browser is checked <sel>

Semantic Locators (find)

bash
agent-browser find role button click --name "Submit"
agent-browser find text "Sign In" click
agent-browser find label "Email" fill "test@example.com"
agent-browser find placeholder "Search..." fill "rust"
agent-browser find testid "login-btn" click
agent-browser find first ".item" click
agent-browser find nth 2 "a" text
agent-browser find role textbox fill "hello" --name "Username"

Actions: click, fill, type, hover, focus, check, uncheck, text

Waiting

bash
agent-browser wait "#modal"                          # Wait for element visible
agent-browser wait 2000                              # Wait N milliseconds
agent-browser wait --text "Welcome back"             # Wait for text
agent-browser wait --url "**/dashboard"              # Wait for URL pattern
agent-browser wait --load networkidle                # Wait for load state
agent-browser wait --fn "window.appReady === true"   # Wait for JS condition
agent-browser wait "#spinner" --state hidden         # Wait for element to disappear

Load states: load, domcontentloaded, networkidle

JavaScript Eval

bash
agent-browser eval "document.title"
agent-browser eval "JSON.stringify(window.__STATE__)"
agent-browser eval -b "BASE64_ENCODED_JS"
echo "return document.body.innerHTML" | agent-browser eval --stdin

Batch Execution (efficient multi-step)

bash
echo '[
  ["open", "https://example.com"],
  ["snapshot", "-i"],
  ["fill", "@e2", "user@example.com"],
  ["click", "@e1"],
  ["screenshot", "result.png"]
]' | agent-browser batch --json

# Stop on first failure
agent-browser batch --bail < commands.json

Tabs & Frames

bash
agent-browser tab                    # List tabs
agent-browser tab new https://...    # New tab with URL
agent-browser tab 2                  # Switch to tab 2
agent-browser tab close              # Close current tab
agent-browser frame "#my-iframe"     # Switch into iframe
agent-browser frame main             # Return to main frame

Cookies & Storage

bash
agent-browser cookies
agent-browser cookies set session_id "abc123"
agent-browser cookies clear

agent-browser storage local
agent-browser storage local set theme dark
agent-browser storage local clear
agent-browser storage session set cart '{"items":[]}'

Network

bash
agent-browser network route "**/api/users" --body '{"users":[]}'  # Mock response
agent-browser network route "**/ads/**" --abort                    # Block requests
agent-browser network unroute                                       # Remove all routes
agent-browser network requests --filter api                        # View requests
agent-browser network har start
agent-browser network har stop recording.har

Browser Settings

bash
agent-browser set viewport 1280 800
agent-browser set viewport 375 812 2        # With device pixel ratio (retina)
agent-browser set device "iPhone 14"
agent-browser set geo 37.7749 -122.4194
agent-browser set offline on
agent-browser set headers '{"X-Custom":"value"}'
agent-browser set credentials admin secret
agent-browser set media dark

Auth State

bash
agent-browser state save ./auth.json    # Save cookies + localStorage
agent-browser state load ./auth.json    # Restore auth state
agent-browser state list                # List saved states
agent-browser state show auth.json      # Summary of saved state

Dialogs

bash
agent-browser dialog accept             # Accept alert/confirm/prompt
agent-browser dialog accept "My input"  # Accept prompt with text
agent-browser dialog dismiss

Clipboard

bash
agent-browser clipboard read
agent-browser clipboard write "Hello, World!"
agent-browser clipboard copy           # Ctrl+C current selection
agent-browser clipboard paste          # Ctrl+V

Diff & Visual Testing

bash
agent-browser diff snapshot                                  # vs last snapshot
agent-browser diff snapshot --baseline before.txt            # vs saved file
agent-browser diff snapshot --selector "#main" --compact
agent-browser diff screenshot --baseline before.png
agent-browser diff screenshot --baseline b.png -o diff.png
agent-browser diff url https://v1.example.com https://v2.example.com
agent-browser diff url https://v1.example.com https://v2.example.com --screenshot
agent-browser diff url https://v1.example.com https://v2.example.com --selector "#content"

Debug & Profiling

bash
agent-browser trace start trace.zip
agent-browser trace stop
agent-browser profiler start
agent-browser profiler stop profile.json
agent-browser console                  # View console messages
agent-browser errors                   # View uncaught JS exceptions
agent-browser highlight "#button"      # Visually highlight element
agent-browser inspect                  # Open Chrome DevTools
agent-browser connect 9222             # Connect to existing browser via CDP port

Common Patterns

Login flow and save session

bash
#!/bin/bash
agent-browser open https://app.example.com/login
agent-browser fill "#email" "$LOGIN_EMAIL"
agent-browser fill "#password" "$LOGIN_PASSWORD"
agent-browser click "[type=submit]"
agent-browser wait --url "**/dashboard"
agent-browser state save ./session.json

AI agent loop with snapshot-driven interaction

bash
#!/bin/bash
agent-browser open https://app.example.com
agent-browser state load ./session.json

# Get snapshot, parse @refs, act
SNAPSHOT=$(agent-browser snapshot)
echo "$SNAPSHOT"

# Agent determines @e5 is the search box
agent-browser fill @e5 "quarterly report"
agent-browser press Enter
agent-browser wait --load networkidle
agent-browser snapshot
agent-browser screenshot results.png

Batch commands from a script (JSON)

bash
cat > commands.json << 'EOF'
[
  ["open", "https://news.ycombinator.com"],
  ["wait", "--load", "networkidle"],
  ["get", "title"],
  ["snapshot"],
  ["screenshot", "hn.png"]
]
EOF

agent-browser batch --json < commands.json

Scrape with mocked network

bash
agent-browser open https://api-heavy-app.example.com
agent-browser network route "**/api/slow-endpoint" --body '{"data":"mocked"}'
agent-browser snapshot
agent-browser network unroute

Full-page screenshot with annotations

bash
agent-browser open https://example.com
agent-browser wait --load networkidle
agent-browser screenshot --full --annotate annotated.png

Connect to already-running Chrome

bash
# Start Chrome with remote debugging
google-chrome --remote-debugging-port=9222 &

agent-browser connect 9222
agent-browser open https://example.com
agent-browser snapshot

Emulate mobile device

bash
agent-browser set device "iPhone 14"
agent-browser open https://example.com
agent-browser screenshot mobile.png

HAR recording for network analysis

bash
agent-browser open https://example.com
agent-browser network har start
agent-browser click "#load-data"
agent-browser wait --load networkidle
agent-browser network har stop session.har

Selector Reference

FormatExampleNotes
@ref@e1, @e12From snapshot output — preferred for AI
CSS#id, .class, [attr=val]Standard CSS selectors
Text"Sign In"Exact text match
XPath//button[@type='submit']Full XPath

Troubleshooting

Chrome not found

bash
agent-browser install              # Downloads Chrome for Testing
agent-browser install --with-deps  # Linux: also installs system libs

Element not found / timing issues

bash
agent-browser wait "#my-element"              # Wait for visibility first
agent-browser wait --load networkidle         # Wait for page to settle
agent-browser wait --fn "!!document.querySelector('#app')"

Selector issues — use snapshot refs instead

bash
# Instead of fragile CSS:
agent-browser click ".btn.btn-primary.submit-form"

# Use snapshot refs:
agent-browser snapshot  # Find @e7 = [button] "Submit"
agent-browser click @e7

Debug what's on the page

bash
agent-browser screenshot debug.png        # Visual check
agent-browser snapshot                    # Accessibility tree
agent-browser console                     # JS console output
agent-browser errors                      # Uncaught exceptions
agent-browser eval "document.readyState"

Auth issues between sessions

bash
agent-browser state save ./auth.json   # After successful login
agent-browser state load ./auth.json   # At start of next session

Handling alerts/dialogs

bash
# Set up handler BEFORE the action that triggers dialog
agent-browser dialog accept
agent-browser click "#delete-button"

Performance — use batch for multi-step workflows

bash
# Slow: one process per command
agent-browser open https://example.com
agent-browser fill "#q" "search"
agent-browser click "#submit"

# Fast: single process, multiple commands
echo '[["open","https://example.com"],["fill","#q","search"],["click","#submit"]]' \
  | agent-browser batch --json

Frequently asked questions

What does the Agent Browser Automation AI skill do?

Headless browser automation CLI for AI agents using native Rust binary with Chrome DevTools Protocol

Why use Agent Browser Automation on TypingMind?

Because you install it once and use it with any model. Agent Browser Automation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Browser Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reason-machines/trending-skills/tree/main/skills/agent-browser-automation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Browser Automation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Browser Automation?

As many as you like. As long as a model supports skills, you can use Agent Browser Automation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Browser Automation AI skill free?

It is published on GitHub by reason-machines. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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