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Get Started Free →You are an expert in Browser Use, the Python library that lets AI agents control a web browser. You help developers build agents that can navigate websites, fill forms, click buttons, extract data, and complete multi-step web tasks — using vision and DOM understanding to interact with any website like a human would.
.claude/skills/terminalskills-browser-use/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -11% | 0% |
You are an expert in Browser Use, the Python library that lets AI agents control a web browser. You help developers build agents that can navigate websites, fill forms, click buttons, extract data, and complete multi-step web tasks — using vision and DOM understanding to interact with any website like a human would.
pythonfrom browser_use import Agent from langchain_openai import ChatOpenAI agent = Agent( task="Go to amazon.com, search for 'mechanical keyboard', and find the best-rated one under $100", llm=ChatOpenAI(model="gpt-4o"), ) result = await agent.run() print(result) # "The best-rated mechanical keyboard under $100 is..." # Multi-step tasks agent = Agent( task=""" 1. Go to github.com/myorg/myrepo 2. Click on Issues tab 3. Create a new issue with title 'Update dependencies' and body 'Run npm audit fix' 4. Add the label 'maintenance' """, llm=ChatOpenAI(model="gpt-4o"), ) await agent.run() # With custom browser config from browser_use import BrowserConfig config = BrowserConfig( headless=True, proxy="http://proxy:8080", cookies=[{"name": "session", "value": "abc123", "domain": ".example.com"}], ) agent = Agent(task="...", llm=llm, browser_config=config) # Extract structured data from pydantic import BaseModel class Product(BaseModel): name: str price: float rating: float agent = Agent( task="Go to bestbuy.com and find the top 5 laptops. Return structured data.", llm=ChatOpenAI(model="gpt-4o"), output_model=list[Product], ) result = await agent.run() # result is list[Product] — validated Pydantic objects
bashpip install browser-use playwright install
output_model for typed extraction; Pydantic validation on resultsheadless=True for server/CI; False for debugging to watch the agent| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 12,196 | 10,499 | -14% | 1 | 1 | 0% | 2,611 | 3,011 | +15% | 0 | 0 | — |
case-03 | pass→pass | 12,580 | 8,353 | -34% | 1 | 1 | 0% | 2,481 | 2,301 | -7% | 0 | 0 | — |
case-01 | fail→pass | 10,799 | 8,640 | -20% | 1 | 1 | 0% | 2,342 | 2,531 | +8% | 0 | 0 | — |
case-04 | fail→pass | 13,632 | 6,624 | -51% | 1 | 1 | 0% | 2,416 | 2,049 | -15% | 0 | 0 | — |
case-05 | pass→pass | 12,225 | 8,171 | -33% | 1 | 1 | 0% | 2,111 | 2,241 | +6% | 0 | 0 | — |
case-06 | fail→fail | 8,537 | 6,474 | -24% | 1 | 1 | 0% | 1,748 | 2,100 | +20% | 0 | 0 | — |
case-07 | fail→pass | 12,614 | 10,226 | -19% | 1 | 1 | 0% | 2,450 | 2,833 | +16% | 0 | 0 | — |
case-08 | fail→fail | 10,439 | 7,110 | -32% | 1 | 1 | 0% | 2,470 | 2,061 | -17% | 0 | 0 | — |
case-09 | pass→pass | 10,055 | 9,226 | -8% | 1 | 1 | 0% | 1,994 | 2,396 | +20% | 0 | 0 | — |
case-10 | pass→pass | 4,711 | 4,634 | -2% | 1 | 1 | 0% | 906 | 1,600 | +77% | 0 | 0 | — |
case-11 | pass→pass | 7,150 | 5,635 | -21% | 1 | 1 | 0% | 1,584 | 2,069 | +31% | 0 | 0 | — |
case-20 | pass→pass | 3,831 | 2,179 | -43% | 1 | 1 | 0% | 728 | 1,081 | +48% | 0 | 0 | — |
case-12 | fail→pass | 12,955 | 2,107 | -84% | 1 | 1 | 0% | 835 | 1,070 | +28% | 0 | 0 | — |
case-13 | pass→pass | 3,698 | 2,918 | -21% | 1 | 1 | 0% | 739 | 1,095 | +48% | 0 | 0 | — |
case-14 | fail→fail | 7,122 | 5,751 | -19% | 1 | 1 | 0% | 1,589 | 2,058 | +30% | 0 | 0 | — |
case-15 | pass→pass | 6,376 | 4,320 | -32% | 1 | 1 | 0% | 1,282 | 1,552 | +21% | 0 | 0 | — |
case-16 | fail→pass | 10,103 | 5,471 | -46% | 1 | 1 | 0% | 2,136 | 1,894 | -11% | 0 | 0 | — |
case-17 | pass→pass | 10,595 | 7,146 | -33% | 1 | 1 | 0% | 1,874 | 2,016 | +8% | 0 | 0 | — |
case-18 | pass→pass | 10,213 | 5,514 | -46% | 1 | 1 | 0% | 1,726 | 1,750 | +1% | 0 | 0 | — |
case-19 | pass→pass | 12,665 | 11,998 | -5% | 1 | 1 | 0% | 2,241 | 2,799 | +25% | 0 | 0 | — |
case-21 | pass→pass | 14,429 | 12,685 | -12% | 1 | 1 | 0% | 2,582 | 2,820 | +9% | 0 | 0 | — |
case-22 | fail→pass | 8,713 | 6,036 | -31% | 1 | 1 | 0% | 1,622 | 1,864 | +15% | 0 | 0 | — |
case-23 | pass→pass | 3,408 | 3,372 | -1% | 1 | 1 | 0% | 741 | 1,258 | +70% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 22 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +26 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.