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版本数
在 OpenClaw 中安装
/install agent-browser-clawdbot
功能描述
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection
使用说明 (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 \x3Curl>
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
安全使用建议
This skill is a set of instructions for using the agent-browser CLI (no code shipped). Before installing or using it: 1) Confirm you trust the agent-browser binary/source (SKILL.md points to a GitHub repo) — installing the CLI will download Chromium and run code on your machine. 2) Treat saved state files (auth.json) as sensitive — they contain cookies/storage that can authenticate as you; avoid loading state files from untrusted sources. 3) Be aware the tool can run JS in page context (wait --fn) and mock network requests — useful for automation but could be abused to exfiltrate data if run on sensitive sites. 4) Limit the agent's permissions and only allow invocation when necessary. If you need extra assurance, review the agent-browser project's repository and npm package before installing.
功能分析
Type: OpenClaw Skill
Name:
Developer:
Version:
Description: OpenClaw Agent Skill
The skill bundle provides documentation for the 'agent-browser' CLI tool, a legitimate browser automation utility developed by Vercel Labs. The instructions in SKILL.md cover standard web automation tasks such as navigation, element interaction, and session management without any evidence of malicious intent, data exfiltration, or prompt injection.
能力评估
Purpose & Capability
The name/description claim headless browser automation; the SKILL.md only instructs the agent to run an agent-browser CLI (navigation, snapshot, refs, sessions, state save/load, network controls). All required capabilities align with that stated purpose.
Instruction Scope
Instructions stay within browser automation scope (open, snapshot, click, fill, state save/load, network routing, execute wait functions). Note: state save/load writes/reads auth JSON (cookies/storage) and the wait/--fn mechanism implies arbitrary JS evaluation in page context — both are expected for automation but can expose credentials or data if misused.
Install Mechanism
This is instruction-only (no install spec in registry). The SKILL.md documents installing agent-browser via npm and that agent-browser can download Chromium; that is reasonable documentation but outside the skill bundle. If you install the CLI, its installer will fetch large browser binaries — review that tool's source before installing.
Credentials
The skill declares no required env vars or credentials. It mentions an optional AGENT_BROWSER_SESSION env var and uses file paths for state (e.g., auth.json) — these are proportional to a browser automation tool but are locations where sensitive tokens/cookies may be stored.
Persistence & Privilege
Skill is not always-enabled and does not request persistence or cross-skill config changes. It instructs using session/state files for browser contexts, which is normal and limited to the browser tool's scope.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install agent-browser-clawdbot - 安装完成后,直接呼叫该 Skill 的名称或使用
/agent-browser-clawdbot触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
- Initial release of agent-browser-clawdbot skill.
- Enables fast, headless browser automation via CLI, optimized for AI agent workflows.
- Provides accessibility tree snapshots with ref-based deterministic element selection.
- Supports session isolation, state persistence, and robust automation commands (navigation, interaction, data extraction, network control, and more).
- Includes example workflows and best practices for efficient automation.
元数据
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