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在 OpenClaw 中安装
/install clawdbot-agent-browser
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 an instruction-only wrapper for an external CLI (agent-browser). Before installing or using it: 1) Verify the agent-browser npm package and the GitHub repo (https://github.com/vercel-labs/agent-browser) are the official/trusted sources. 2) Be cautious when using state save/load (auth.json) — those files can contain cookies and session tokens; avoid loading untrusted state files. 3) Network routing/mocking features are powerful and can intercept or alter traffic — only route or mock endpoints you trust. 4) Note the small metadata mismatch (ownerId) in the skill bundle; confirm the publisher identity if provenance matters. If you control the CLI binary source and understand the effects of saving state and manipulating network traffic, the skill is coherent with its stated purpose.
功能分析
Type: OpenClaw Skill
Name: clawdbot-agent-browser
Version: 0.1.0
The skill bundle provides documentation and instructions for using the 'agent-browser' CLI tool, a legitimate browser automation utility from Vercel Labs. The commands described in SKILL.md are standard for web automation tasks (navigation, element interaction, session persistence), and the installation steps follow standard NPM procedures. No evidence of malicious intent, data exfiltration, or prompt injection was found.
能力评估
Purpose & Capability
The name/description (headless browser automation with accessibility snapshots) matches the SKILL.md commands and examples. The skill only expects an external 'agent-browser' CLI to be available, which is consistent with its stated purpose. (Minor metadata mismatch: registry ownerId and _meta.json ownerId differ, which is likely non-malicious but worth checking.)
Instruction Scope
SKILL.md explicitly instructs the agent to run an external CLI (agent-browser) and to save/load browser state files (auth.json), take snapshots, control network routing, and mock requests. These actions are within the domain of browser automation, but state save/load and network routing are sensitive operations because they can expose cookies, tokens, or allow interception/modification of network traffic.
Install Mechanism
There is no install spec in the skill bundle (instruction-only). The README suggests installing via 'npm install -g agent-browser' and running 'agent-browser install' to download Chromium. This is a standard, expected approach but carries normal supply-chain risks (npm package provenance and the Chromium download). The skill does not attempt to auto-install anything itself.
Credentials
The skill declares no required environment variables or credentials. Example uses of AGENT_BROWSER_SESSION and state file names are explanatory only. The lack of requested secrets is proportionate to the described functionality, though saved state files may contain sensitive cookies/session tokens if used.
Persistence & Privilege
always:false and no install or persistence actions embedded in the skill bundle. The skill does not request elevated privileges or modify other skills' configs. Autonomous invocation is allowed by default (platform behavior) but not exceptional for this skill.
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 clawdbot-agent-browser - 安装完成后,直接呼叫该 Skill 的名称或使用
/clawdbot-agent-browser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
元数据
常见问题
Clawdbot Agent Browser 是什么?
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 302 次。
如何安装 Clawdbot Agent Browser?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install clawdbot-agent-browser」即可一键安装,无需额外配置。
Clawdbot Agent Browser 是免费的吗?
是的,Clawdbot Agent Browser 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Clawdbot Agent Browser 支持哪些平台?
Clawdbot Agent Browser 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Clawdbot Agent Browser?
由 Viv888-AI(@viv888-ai)开发并维护,当前版本 v0.1.0。
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