Skill: cnxufei-tech/browser-automation-zero-token (version 1.0.0)

--- name: browser-automation-zero-token description: > 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”. metadata: { "openclaw": { "requires": { "bins": ["agent-browser", "npm"] }, "install": [ { "id": "node", "kind": "node", "package": "agent-browser", "bins": ["agent-browser"], "label": "Install agent-browser CLI (npm)", }, ], }, } --- # Browser Automation Zero Token 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.

path: references/source-notes.md
content: # Source Notes

This skill was derived from a WeChat article about using `agent-browser` CLI plus OpenClaw skills to build low-code / zero-token browser automation.

## Key extracted ideas

- Traditional browser automation hurts because of code complexity, selector fragility, and maintenance cost.
- OpenClaw skill + `agent-browser` CLI changes the unit of automation from code-heavy scripts to reusable markdown-guided workflows.
- The core interaction model is semantic page refs (`@e1`, `@e2`, etc.) obtained from snapshots.
- The main loop is:
  - OPEN
  - SNAPSHOT
  - INTERACT
  - VERIFY
  - REPEAT
  - CLOSE
- Once a flow is stabilized, execution can be near-zero-token because the CLI can run deterministically without repeated model reasoning.
- Session/auth state save-load is critical for repeated sign-in style workflows.

## Commands highlighted by the source article

```bash
agent-browser open <url>
agent-browser snapshot -i
agent-browser click @e<n>
agent-browser fill @e<n> "text"
agent-browser type @e<n> "text"
agent-browser screenshot debug.png
agent-browser get url
agent-browser get title
agent-browser wait --load networkidle
agent-browser state save auth.json
agent-browser state load auth.json
agent-browser close
```

## Reusable task archetypes

- auto sign-in
- repeated form submission
- repeated dashboard opening/checking
- click-through admin workflows
- stable browser routines that do not require reasoning every run

## Failure patterns worth remembering

- stale refs after navigation
- page re-render without re-snapshot
- expired auth state
- silent login failure
- no explicit wait before next step

## Implementation note

The article emphasized “0 Token” as a strategic positioning idea. In practice, this means minimizing model involvement during execution after the workflow has already been discovered and encoded.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)