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在 OpenClaw 中安装
/install agent-browser-clawdbot-disabled
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 guide for using the external agent-browser CLI — it does not embed or install code itself. Before using: 1) install agent-browser only from the official project (verify the npm package and GitHub repo) because the skill expects that external binary; 2) be cautious with state save/load files (e.g., auth.json) — they can contain cookies and tokens that effectively act as credentials; 3) the agent will be able to run the CLI (and thus visit and interact with websites), so avoid allowing it to run automation against sensitive accounts or pages unless you trust the CLI and have isolated session/state files; 4) note the small inconsistency: the SKILL.md metadata references the required command 'agent-browser' while the registry metadata lists no required binaries — you must ensure the CLI is actually installed on your system. If you want to reduce risk, run the agent-browser CLI in an isolated environment, review the package source before installing, and avoid loading saved state files from untrusted sources.
功能分析
Type: OpenClaw Skill
Name: agent-browser-clawdbot-disabled
Version: 1.0.1
The skill bundle provides documentation and usage instructions for 'agent-browser', a legitimate CLI tool developed by Vercel Labs for headless browser automation. The instructions in SKILL.md are well-structured for AI agents, focusing on deterministic element selection via accessibility trees and session management. No evidence of malicious intent, data exfiltration, or prompt injection was found; the capabilities described (e.g., cookie handling, network mocking) are standard for browser automation tools.
能力评估
Purpose & Capability
Name/description match the runtime instructions: the SKILL.md consistently documents using the agent-browser CLI for headless browser automation and snapshots. The commands and features described (snapshots, refs, sessions, state save/load) align with a browser automation tool.
Instruction Scope
Instructions stay within the browser-automation domain (open, snapshot, click, state save/load, network routing). They do reference reading/writing state files (e.g., auth.json, state save/load) and using an environment variable example (AGENT_BROWSER_SESSION). Those are expected for this purpose but imply local file I/O that can store sensitive cookies/auth tokens — the agent running these commands could therefore access and persist credentials from pages it automates.
Install Mechanism
No install spec is included in the skill bundle (instruction-only). The SKILL.md recommends installing the external agent-browser CLI via npm and running its own installer to download Chromium; this is a normal, proportional ask for a CLI-based browser tool. There are no opaque download URLs in the skill itself.
Credentials
The skill does not require any environment variables or credentials in the registry metadata. It shows optional use of AGENT_BROWSER_SESSION and references state files; those are reasonable for session isolation but can lead to storage of sensitive cookies/credentials. No unrelated secrets are requested.
Persistence & Privilege
always is false and the skill is user-invocable. The skill does not request permanent platform privileges or modify other skills' configs. Autonomy (model invocation) is allowed by default — appropriate for agent-invoked CLI workflows — but keep in mind this lets the agent run the external CLI if enabled.
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-disabled - 安装完成后,直接呼叫该 Skill 的名称或使用
/agent-browser-clawdbot-disabled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
v1.0.1
- Initial release of agent-browser-clawdbot-disabled skill.
- Added SKILL.md and _meta.json files.
- No changes to functionality; documentation and metadata only.
v1.0.0
- Initial release of the Agent Browser skill for headless browser automation.
- Provides CLI commands for navigation, interaction, state persistence, multi-session support, and accessibility tree snapshots with ref-based element selection.
- Includes best practices and example workflows for AI-powered browser automation.
- Features network, storage, tab, and frame control, along with advanced waiting and state management options.
- Optimized for performance and determinism, suitable for complex web automation tasks.
元数据
常见问题
Agent Browser Clawdbot.Disabled 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 105 次。
如何安装 Agent Browser Clawdbot.Disabled?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-browser-clawdbot-disabled」即可一键安装,无需额外配置。
Agent Browser Clawdbot.Disabled 是免费的吗?
是的,Agent Browser Clawdbot.Disabled 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Agent Browser Clawdbot.Disabled 支持哪些平台?
Agent Browser Clawdbot.Disabled 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 Agent Browser Clawdbot.Disabled?
由 quanfuda(@quanfuda)开发并维护,当前版本 v1.0.1。
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