Browser Automation Zero Token

Build and run low-code browser automation workflows with agent-browser CLI and reusable skills, especially for repetitive web tasks like 登录、签到、表单填写、固定点击流程、状态保存、 重复网页操作, or when the user says things like “做浏览器自动化”, “用 agent-browser 跑网页流程”, “0 token 浏览器自动化”, “把网页重复操作固化成 skill”, “做自动签到 skill”, or “用 CLI + Skill 搭建 AI 浏览器自动化框架”.

Use agent-browser plus OpenClaw skills to turn repeatable browser tasks into reusable, low-maintenance workflows.

When To Use

Use this skill for repeatable browser workflows such as:

• daily site sign-in
• repeated login + click flows
• dashboard checks
• fixed form-filling routines
• internal admin flows

Prefer this pattern when Playwright/Puppeteer feels too heavy, selectors are brittle, or repeated screenshot/tool loops waste tokens.

Core Workflow

Always think in this loop:

1. OPEN — open the target page
2. SNAPSHOT — inspect page structure and collect current @refs
3. INTERACT — click / fill / select using @refs
4. VERIFY — re-snapshot or check page state after each meaningful change
5. REPEAT — continue until the business task is done
6. CLOSE — close the browser session cleanly

Short form:

OPEN → SNAPSHOT → INTERACT → VERIFY → REPEAT → CLOSE

Preconditions

Before using this skill, verify:

• agent-browser is installed
• browser runtime/dependencies are installed
• the target site allows normal browser interaction
• credentials are available if login is required
• the user is authorized to automate the target site

Install CLI:

bash

npm install -g agent-browser
agent-browser install --with-deps
agent-browser --version

Optional ecosystem install:

bash

clawhub install openclaw-skills-browserautomation-skill

Base Command Set

Use this minimal loop:

bash

agent-browser open <url>
agent-browser snapshot -i
agent-browser click @e<n>
agent-browser fill @e<n> "text"
agent-browser state save auth.json
agent-browser state load auth.json
agent-browser close

Important rule: @refs come from the latest snapshot. After navigation or major DOM changes, snapshot again. More command notes live in references/source-notes.md.

Operating Rules

1. Snapshot before interacting

Do not guess refs. Always obtain fresh @refs from agent-browser snapshot -i before click/fill/select actions.

2. Re-snapshot after state changes

After login, route changes, modal opens, tab switches, or dynamic content loads, run snapshot again.

3. Prefer refs over brittle selectors

Use @e<n> from snapshots whenever possible. Fall back to complex selectors only when refs or semantic locators are insufficient.

4. Save auth state for recurring tasks

If the workflow requires login and will be reused:

bash

agent-browser state save auth.json
agent-browser state load auth.json

This is often the difference between “semi-automated” and “truly one-command repeatable.”

5. Verify, don’t assume

After key actions, confirm progress using one or more of:

• another snapshot
• agent-browser get url
• agent-browser get title
• visible text checks
• screenshots for debugging

Zero-Token Execution Pattern

Use zero-token mode when the workflow is already known and stable:

1. discover the workflow once
2. capture the working CLI sequence
3. store it in a skill or task markdown
4. rerun it directly without repeated AI reasoning

Example:

bash

agent-browser open https://example.com/login
agent-browser snapshot -i
agent-browser fill @e3 "username"
agent-browser fill @e4 "password"
agent-browser click @e5
agent-browser snapshot -i
agent-browser click @e21
agent-browser close

Build A Reusable Site Skill

When the user wants to turn one website flow into a reusable skill:

1. identify the business goal
2. map the page flow once
3. note where refs must be refreshed
4. decide whether auth state should be saved/loaded
5. write the repeatable steps into a concise skill
6. document failure points and re-snapshot requirements

A good site skill should capture:

• target site / task
• prerequisites
• ordered browser steps
• verification points
• state save/load strategy
• caveats about changing refs

Example: Daily Sign-In Flow

md

---
name: auto-signin-example
description: Automatically sign in to example.com using agent-browser CLI.
---

# Auto Sign-In Example

## Workflow
1. Open the login page.
2. Snapshot interactive elements.
3. Fill username and password using current refs.
4. Click the login button.
5. Re-snapshot after navigation.
6. Click the sign-in button.
7. Save state if reuse is needed.
8. Close the browser.

Debugging

If the automation breaks, check in this order:

1. was a fresh snapshot taken?
2. did the page navigate or re-render?
3. did login fail silently?
4. did the saved state expire?
5. did a ref change?
6. does the flow need an explicit wait?

For command examples, see references/source-notes.md.

When Not To Use This Pattern

Avoid overcommitting to zero-token browser automation when:

• the task requires heavy judgment each run
• the page changes unpredictably every time
• anti-bot controls block normal automation
• the target workflow includes sensitive steps that should not be automated without explicit approval
• direct API integration would be cleaner and more reliable

References

If you need the distilled source rationale, read references/source-notes.md.

Output Expectations

Depending on the request, this skill should help produce one of:

• a repeatable CLI command sequence
• a site-specific automation skill
• a debugging checklist for a broken browser flow
• a saved-state based recurring automation routine

Common Failure Modes

Avoid these:

• using stale refs after navigation
• storing hardcoded assumptions without verification steps
• skipping auth-state management for recurring tasks
• claiming zero-token while still relying on repeated AI interpretation each run

Fast Heuristic

If the workflow can be discovered once, re-run many times, and verified through snapshots/state checks, it is a strong candidate for this skill.