Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Zaoqu-Liu/ScienceClaw --skill browser-automationgit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zaoqu-liu/scienceclaw/browser-automation)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/browser-automation"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/browser-automation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.01659 |
| Opus 5 | $0.00049 | $0.00830 |
| Sonnet 5 | $0.00020 | $0.00332 |
| Haiku 4.5 | $0.00010 | $0.00166 |
Grade A, and why
browser-automation scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
description: Browser automation for accessing scientific databases that lack REST APIs. Uses the browser-use Python framework (81k+ GitHub stars) to control a real browser via LLM vision. Enables data extraction from web How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Browser Automation for Scientific Data Collection
Access scientific databases that have no REST API by controlling a real browser programmatically. Uses the browser-use framework (vision-based LLM browser automation).
When to Use
- Target database has no REST API (e.g., GEPIA2, some COSMIC pages)
- curl returns 403/captcha/login required and the data is publicly viewable in a browser
- Need to navigate multi-step web forms (e.g., TIMER2.0 correlation analysis)
- Need to download files from web interfaces (e.g., GEO supplementary data)
- API exists but is severely rate-limited and web access is faster
When NOT to use:
- REST API is available and working → use
curl - Data requires paid subscription → do not circumvent paywalls
- Data can be obtained from an alternative open API → prefer the API
Installation Check
Before using browser automation, verify the environment:
bash: python3 -c "
try:
import browser_use
print('✅ browser-use installed')
except ImportError:
print('❌ browser-use not installed')
print(' Install: pip install browser-use')
import shutil
if shutil.which('chromium') or shutil.which('chromium-browser') or shutil.which('google-chrome'):
print('✅ Chromium/Chrome found')
else:
print('⚠️ No Chromium/Chrome found')
print(' Install: apt-get install chromium-browser (Linux)')
print(' Or: brew install --cask chromium (macOS)')
try:
import playwright
print('✅ Playwright installed')
except ImportError:
print('❌ Playwright not installed')
print(' Install: pip install playwright && python -m playwright install chromium')
"
If not installed:
pip install -q browser-use playwright && python -m playwright install chromium
Usage Pattern
Basic: Extract data from a web page
from browser_use import Agent, Browser, BrowserConfig
from langchain_openai import ChatOpenAI
import asyncio
async def extract_gepia2_data(gene: str, cancer: str):
"""Extract gene expression data from GEPIA2 (no API available)."""
browser = Browser(config=BrowserConfig(headless=True))
llm = ChatOpenAI(model="gpt-4o", api_key=os.environ["OPENAI_API_KEY"])
agent = Agent(
task=f"""Go to http://gepia2.cancer-pku.cn/#analysis
1. Click on 'Expression DIY' in the left menu
2. In the gene input box, type '{gene}'
3. Select '{cancer}' from the cancer type dropdown
4. Click 'Plot' button
5. Wait for the plot to load
6. Extract the median expression values for Tumor and Normal from the plot
7. Return the values as JSON: {{"gene": "{gene}", "cancer": "{cancer}", "tumor_median": X, "normal_median": Y}}
""",
llm=llm,
browser=browser,
)
result = await agent.run()
await browser.close()
return result
result = asyncio.run(extract_gepia2_data("THBS2", "PAAD"))
print(result)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 215 lines · 99 tokens per session scan A ca004f4a4d5d
browser-automation is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 99 tokens to every session and 1,659 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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