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在 OpenClaw 中安装
/install handy01-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 instruction-only skill appears to do what it says: automate browsers via the 'agent-browser' CLI. Before using: (1) Confirm you or your platform install the official 'agent-browser' package (npm and GitHub repo) — the skill does not auto-install it. (2) Be cautious with state files (auth.json) because they can contain session cookies and tokens; don't load state from untrusted sources. (3) Network routing and request inspection features can expose or modify web traffic — only use against sites you trust and for which you have permission. (4) If you need higher assurance, verify the upstream project repository and release artifacts referenced in SKILL.md (the GitHub homepage) before installing.
功能分析
Type: OpenClaw Skill
Name: handy01-agent-browser
Version: 0.1.2
The skill bundle provides documentation and usage examples for the 'agent-browser' CLI tool, a legitimate headless browser automation utility developed by Vercel Labs. The instructions in SKILL.md are focused on standard automation tasks such as navigation, element interaction, and session management, with no evidence of malicious intent, data exfiltration, or prompt injection attacks.
能力评估
Purpose & Capability
SKILL.md describes a headless browser CLI (agent-browser) and includes commands for navigation, snapshots, network routing, and state save/load — all coherent with the stated purpose. However, the skill registry metadata lists no required binaries while the SKILL.md metadata and instructions explicitly require the 'agent-browser' command and recommend 'npm install -g agent-browser'. This is a mismatch: the CLI is required for the skill to work but the package/binary is not declared in registry requirements/install spec.
Instruction Scope
The runtime instructions focus on browser automation (open, snapshot, click, fill, network routing, session/state management). They do instruct reading/writing state files (e.g., auth.json) and using network routing/mocking — functionality that can handle sensitive tokens/cookies but is within the normal scope for a browser automation tool. The instructions do not direct the agent to read unrelated system files or external endpoints beyond interacting with target web pages.
Install Mechanism
There is no formal install spec in the skill bundle; it's instruction-only. SKILL.md advises 'npm install -g agent-browser' and a subsequent 'agent-browser install' to download Chromium. Because there is no install spec, the platform won't automatically install the binary; the operator must install the CLI themselves. Verify the npm package and Chromium download come from the official repository before installing.
Credentials
The skill declares no required environment variables or credentials. The docs show an example AGENT_BROWSER_SESSION env var and describe saving/loading state (cookies/storage) to files. While those are appropriate for session isolation and skipping logins, saved state files can contain sensitive cookies/tokens — users should avoid loading untrusted auth files and be mindful where state files are stored.
Persistence & Privilege
The skill is not marked 'always' and does not request persistent platform-wide privileges. It does instruct saving/loading its own state files (auth.json) which is normal for a browser automation CLI and does not indicate modification of other skills or system-wide settings.
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 handy01-agent-browser - 安装完成后,直接呼叫该 Skill 的名称或使用
/handy01-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
v0.1.2
- Internal metadata file updated (_meta.json).
- No user-facing changes to documentation or feature set.
v0.1.1
Update
元数据
常见问题
Handy01 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 121 次。
如何安装 Handy01 Agent Browser?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install handy01-agent-browser」即可一键安装,无需额外配置。
Handy01 Agent Browser 是免费的吗?
是的,Handy01 Agent Browser 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Handy01 Agent Browser 支持哪些平台?
Handy01 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 Handy01 Agent Browser?
由 handy01(@handy01)开发并维护,当前版本 v0.1.2。
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