QwenPaw is a personal AI assistant that runs on a local machine or in the cloud and connects to multiple chat applications. It provides memory, file workspaces, multiple agents, skills, plugins, and integrations with language-model providers and external tools. The catalogue entries are skills that extend its capabilities.
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 agentscope-ai/QwenPaw --skill browser-engit clone --depth 1 https://github.com/agentscope-ai/QwenPawWrote 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/agentscope-ai/qwenpaw/browser-en)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/browser-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/browser-en/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/browser-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/browser-en.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 29 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00035 | $0.02419 |
| Opus 5 | $0.00017 | $0.01210 |
| Sonnet 5 | $0.00007 | $0.00484 |
| Haiku 4.5 | $0.00003 | $0.00242 |
Grade A, and why
browser scanned grade A with 0 findings 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- browser — 89% identical, 27 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Browser
Work with discipline: perceive the current page, act through the documented surface, then re-perceive before claiming success. State only facts observed in this turn. When stuck, a complete delivery is the step you are stuck on plus the partial results you have verified — never fill in content you did not observe just to produce a full answer.
Respect the human boundary: for login, captcha, 2FA, or any human-only step,
call await browser.handoff(...) and stop. Never automate those flows.
When the browser cannot finish, do not substitute data from another channel
(such as web_search) and still call it a browser result — state each fact's
real source.
This is QwenPaw's builtin Browser SDK, not Playwright. Its surface is closed: anything not listed does not exist. The complete reference is below; re-load this browser skill with the Skill tool if it is no longer in context.
QwenPaw Browser SDK — complete reference. This is QwenPaw's OWN internal SDK and this is the ENTIRE API; these are all the entrypoints. The SDK is already in scope as Browser — call the methods below directly. Write async Python. Work in a loop: perceive → act → verify.
Copy this shape:
browser = await Browser.connect() # connect once; reused all session page = await browser.open("https://example.com") # open a page obs = await page.snapshot() # PERCEIVE — page text is obs.text if len(obs.text) < 6000: print(obs.text) else: # Large page: read selectively instead of dumping everything. lines = [line for line in obs.text.splitlines() if "keyword" in line] print(f"{len(obs.text)} chars total; {len(lines)} matching lines:") print("\n".join(lines[:80]))
For a focused count, use: await page.snapshot(query="keyword")
await page.get_by_role("textbox", name="Search").fill("laptop") # ACT await page.get_by_role("button", name="Search").click() # ACT obs = await page.snapshot() # VERIFY — re-perceive to confirm print("Verified; inspect obs.text with the selective pattern above.")
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.
- 11d ago First seen · 198 lines · 35 tokens per session scan A fc478c65898d
browser is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,741 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 2,419 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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