Agent Browser Clawdbot Local
v1.0.0Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection
MIT-0
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
SKILL.md behavior (agent-browser CLI commands for snapshots, ref-based interaction, sessions, state save/load, network control) is coherent with the stated purpose (headless browser automation). However the registry metadata claims no required binaries/env vars while SKILL.md and its embedded metadata declare a required 'agent-browser' command and show use of npm for installation — a mismatch in declared requirements.
Instruction Scope
Instructions ask the agent to run many commands that will capture page content (snapshots JSON), save/load auth state (cookies/storage), and inspect or mock network requests. These are expected for a browser automation tool but give the agent access to potentially sensitive site content and account sessions. The SKILL.md does not instruct exfiltration, but it enables collection and local storage of credentials/session data and network traffic viewing.
Install Mechanism
Although the registry has no install spec, SKILL.md instructs using 'npm install -g agent-browser' and a subsequent 'agent-browser install' that downloads Chromium. npm packages and binary downloads introduce moderate-to-high risk if the package or its upstream downloads are unverified or malicious — the skill provides no pinned source URL or checksum.
Credentials
The skill does not require environment variables in registry metadata, but SKILL.md references an optional AGENT_BROWSER_SESSION and uses state save/load files (e.g., admin-auth.json) which can contain cookies/session tokens. No unrelated credentials are requested, but file-based session persistence can expose sensitive auth data if misused.
Persistence & Privilege
The skill is not always-enabled and does not request special platform privileges. It does instruct creating/loading local state files and downloading Chromium, which affects only the local environment and not other skills or system-wide settings.
What to consider before installing
This skill appears to be a legitimate CLI-driven browser automation tool, but take these precautions before installing:
- Verify the upstream package and repo: check the GitHub repo (https://github.com/vercel-labs/agent-browser) and the npm package owner/release history to ensure authenticity.
- Expect the CLI to download Chromium and write state files (cookies/localStorage). Treat saved state files as sensitive — they can contain session tokens.
- Run installs in a sandbox or isolated VM if possible; avoid installing globally on a critical host until verified.
- Review the npm package contents (and any download URLs) for unexpected code or network endpoints and prefer pinned checksums.
- Note the registry metadata mismatches (declared required binaries/env vars vs. SKILL.md embedded metadata, and differing _meta.json owner/slug); ask the publisher to clarify these inconsistencies before trusting the skill.
- If you will use the skill with accounts, rotate credentials or use test accounts to limit exposure.Like a lobster shell, security has layers — review code before you run it.
latest
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🌐 Clawdis
SKILL.md
Agent Browser Skill
Fast browser automation using accessibility tree snapshots with refs for deterministic element selection.
Why Use This Over Built-in Browser Tool
Use agent-browser when:
- Automating multi-step workflows
- Need deterministic element selection
- Performance is critical
- Working with complex SPAs
- Need session isolation
Use built-in browser tool when:
- Need screenshots/PDFs for analysis
- Visual inspection required
- Browser extension integration needed
Core Workflow
# 1. Navigate and snapshot
agent-browser open https://example.com
agent-browser snapshot -i --json
# 2. Parse refs from JSON, then interact
agent-browser click @e2
agent-browser fill @e3 "text"
# 3. Re-snapshot after page changes
agent-browser snapshot -i --json
Key Commands
Navigation
agent-browser open <url>
agent-browser back | forward | reload | close
Snapshot (Always use -i --json)
agent-browser snapshot -i --json # Interactive elements, JSON output
agent-browser snapshot -i -c -d 5 --json # + compact, depth limit
agent-browser snapshot -s "#main" -i # Scope to selector
Interactions (Ref-based)
agent-browser click @e2
agent-browser fill @e3 "text"
agent-browser type @e3 "text"
agent-browser hover @e4
agent-browser check @e5 | uncheck @e5
agent-browser select @e6 "value"
agent-browser press "Enter"
agent-browser scroll down 500
agent-browser drag @e7 @e8
Get Information
agent-browser get text @e1 --json
agent-browser get html @e2 --json
agent-browser get value @e3 --json
agent-browser get attr @e4 "href" --json
agent-browser get title --json
agent-browser get url --json
agent-browser get count ".item" --json
Check State
agent-browser is visible @e2 --json
agent-browser is enabled @e3 --json
agent-browser is checked @e4 --json
Wait
agent-browser wait @e2 # Wait for element
agent-browser wait 1000 # Wait ms
agent-browser wait --text "Welcome" # Wait for text
agent-browser wait --url "**/dashboard" # Wait for URL
agent-browser wait --load networkidle # Wait for network
agent-browser wait --fn "window.ready === true"
Sessions (Isolated Browsers)
agent-browser --session admin open site.com
agent-browser --session user open site.com
agent-browser session list
# Or via env: AGENT_BROWSER_SESSION=admin agent-browser ...
State Persistence
agent-browser state save auth.json # Save cookies/storage
agent-browser state load auth.json # Load (skip login)
Screenshots & PDFs
agent-browser screenshot page.png
agent-browser screenshot --full page.png
agent-browser pdf page.pdf
Network Control
agent-browser network route "**/ads/*" --abort # Block
agent-browser network route "**/api/*" --body '{"x":1}' # Mock
agent-browser network requests --filter api # View
Cookies & Storage
agent-browser cookies # Get all
agent-browser cookies set name value
agent-browser storage local key # Get localStorage
agent-browser storage local set key val
Tabs & Frames
agent-browser tab new https://example.com
agent-browser tab 2 # Switch to tab
agent-browser frame @e5 # Switch to iframe
agent-browser frame main # Back to main
Snapshot Output Format
{
"success": true,
"data": {
"snapshot": "...",
"refs": {
"e1": {"role": "heading", "name": "Example Domain"},
"e2": {"role": "button", "name": "Submit"},
"e3": {"role": "textbox", "name": "Email"}
}
}
}
Best Practices
- Always use
-iflag - Focus on interactive elements - Always use
--json- Easier to parse - Wait for stability -
agent-browser wait --load networkidle - Save auth state - Skip login flows with
state save/load - Use sessions - Isolate different browser contexts
- Use
--headedfor debugging - See what's happening
Example: Search and Extract
agent-browser open https://www.google.com
agent-browser snapshot -i --json
# AI identifies search box @e1
agent-browser fill @e1 "AI agents"
agent-browser press Enter
agent-browser wait --load networkidle
agent-browser snapshot -i --json
# AI identifies result refs
agent-browser get text @e3 --json
agent-browser get attr @e4 "href" --json
Example: Multi-Session Testing
# Admin session
agent-browser --session admin open app.com
agent-browser --session admin state load admin-auth.json
agent-browser --session admin snapshot -i --json
# User session (simultaneous)
agent-browser --session user open app.com
agent-browser --session user state load user-auth.json
agent-browser --session user snapshot -i --json
Installation
npm install -g agent-browser
agent-browser install # Download Chromium
agent-browser install --with-deps # Linux: + system deps
Credits
Skill created by Yossi Elkrief (@MaTriXy)
agent-browser CLI by Vercel Labs
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