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Browser

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agentscope-ai
browser

Drive a live browser with async Python against QwenPaw's builtin Browser SDK. The full reference is below; re-load this browser skill after context compaction.

Overview

Publisheragentscope-ai
RepositoryQwenPaw
Skill namebrowser
Stars
35.6K
Forks
3.2K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by agentscope-ai on GitHub. Read the source before you install it.

Installation

Install the Browser AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentscope-ai/QwenPaw/tree/main/src/qwenpaw/agents/skills/browser-en
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentscope-ai/QwenPaw.git /tmp/QwenPaw
mkdir -p .claude/skills
cp -r /tmp/QwenPaw/src/qwenpaw/agents/skills/browser-en .claude/skills/browser
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Browser in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Browser on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Browser is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.")

Session state: this is a stateful session — variables you assign (browser, page) persist across calls, so connect once and reuse them. If a call reports the session was reset, re-run await Browser.connect().

Chrome backend caveat: with backend=chrome you operate inside the user's real browser. A session is a tab-ownership group — tabs are isolated per session, but identity (cookies, logins, storage) is shared with the user's profile and with every other session. Do not rely on session-level identity isolation on this backend.

browser (orchestration): await Browser.connect(*, identity: "auto"|"user"|"avatar"|"guest" = "auto") -> browser Connect as an identity: user, avatar, guest, or auto.

``auto`` picks ``user`` when Chrome is connected, otherwise ``guest``.
An unavailable explicit identity raises instead of substituting.

await browser.open(url: str | None = None) -> page Open a page at url and return it.

Reuses this session's active page when one exists; otherwise a
new page is created. Pages are released when the response cycle ends;
start each cycle by calling ``open(url)`` again.

await browser.pages() -> list of page ref (.id, .url, .title, .active) List open pages with URL, title, and active-state details. await browser.switch_page(page: page ref (.id, .url, .title, .active)) -> none Make the given page ref active for later operations. await browser.close_page(page: page ref (.id, .url, .title, .active)) -> none Close the given page ref in this session. await browser.session_status() -> session status (.owner, .variant, .context, .connected) Report the owner, variant, context, and connected state. await browser.handoff(reason: str, instructions: str = "") -> a result dict Hand a step back to a human (captcha, login, 2FA).

Pass a short reason and instructions; the run stops on this signal —
never automate these flows. The active cycle-scoped page is retained
for one extra response cycle after the handoff.

await browser.present(url: str | None = None) -> page Open a page retained for the chat lifetime. await browser.close() -> none Close this session's browser and release its context.

page (operation): await page.goto(url: str) -> a result dict Navigate this page to url and return raw navigation facts. await page.go_back() -> a result dict Navigate back to the previous page in history. await page.go_forward() -> a result dict Navigate forward to the next page in history. await page.reload() -> a result dict Reload the current page. await page.keep() -> none Retain this page across response cycles for the current chat. await page.wait_for_load_state(state: str = "load", *, timeout: float | None = None) -> none Wait until the page reaches the requested load state.

``networkidle`` semantics depend on the backend: the Playwright
backend waits for true network quiescence, while CDP-based
backends (cdp, chrome) degrade to ``document.readyState ==
"complete"`` plus a fixed 500 ms quiet delay and do NOT track
in-flight requests — content loaded by late XHR may still be
missing when this returns.

await page.wait_for_timeout(timeout: float) -> none Sleep unconditionally for timeout milliseconds (capped at 30 000).

Prefer :py:meth:`locator.wait_for(state, timeout)
<LocatorView.wait_for>` when waiting for a specific DOM condition
— it returns as soon as the condition is met and is both faster
and more reliable than an unconditional sleep.

await page.screenshot() -> a result dict Capture this page to a PNG file in the active workspace. page.get_by_role(role: str, *, name: str | None = None) -> locator Locate elements by accessible role and optional name. page.get_by_text(text: str) -> locator Locate elements by their visible text. page.get_by_label(text: str) -> locator Locate a form control by its associated label text. page.get_by_placeholder(text: str) -> locator Locate an input by its placeholder text. page.locator(selector: str) -> locator Locate elements by a CSS selector when no semantic locator fits. page.frame_locator(selector: str) -> locator Scope subsequent locators to the iframe matching selector. await page.snapshot(query: str | None = None) -> observation (read .text; .match_count when you pass a query) Perceive the page and return readable content in .text.

Pass ``query`` to also report ``.match_count``.

await page.current_surface() -> surface facts (.url, .title, .load_state) Return this page's current URL, title, and load facts. page.mouse -> coordinate/keyboard input surface (see methods below) Viewport-coordinate input surface.

click(x, y) -> a result dict; verify the effect with snapshot().

page.keyboard -> coordinate/keyboard input surface (see methods below) Keyboard input surface.

press(key) -> a result dict; verify the effect with snapshot().

page.get_by_* / page.locator(...) return a locator that mirrors a SUBSET of Playwright's Python locator API — the Playwright-shaped part of this SDK: compose/scope (chainable): get_by_role/get_by_text/get_by_label/ get_by_placeholder, locator(sel), filter(...), nth(i), first, last (properties) iframe scope: page.frame_locator(sel).locator(...) (one frame; no nested frames) read (await): count()->int, inner_text()->str, text_content()->str|None, all_text_contents()->list, get_attribute(name)->str|None, input_value()->str, is_visible()->bool, is_enabled()->bool act (await; returns a short evidence line — read .evidence): click(), fill(v), type(t), press(key), check(), uncheck(), set_checked(b), select_option(*v), hover(), dblclick(), scroll(), focus(), blur(), clear(), wait_for(state), screenshot(), bounding_box()->dict|None (viewport ["x"] ["y"] ["width"] ["height"]; None when the element is not visible) strict-mode uniqueness is enforced — act only when the locator resolves to exactly one element (use count() to check). element_handle / raw CDP unavailable.

Backend differences (chrome/cdp vs playwright): on chrome/cdp the accessible name is a heuristic (aria-labelledby > aria-label > alt > title > text content) - container elements may match get_by_role(name=) more broadly than under playwright, so strict-mode errors are more likely there; narrow with filter(has_text=) or a more specific role. is_enabled() reflects only the disabled property, not aria-disabled. press() supports a fixed key set: printable characters, Enter, Tab, Escape, Backspace, Delete, Arrow keys, Home/End/PageUp/PageDown, and Control/Shift/Alt/Meta combos - anything else fails with guidance. type() sets the value directly and fires an input event; editors that need real per-key events may not react - prefer fill() where possible.

Reading results (read these fields; the type names don't matter): snapshot() -> .text (page text), .match_count (when you pass query) current_surface() -> .url, .title, .load_state page refs -> .id, .url, .title, .active screenshot() -> result dict; read ["path"] bounding_box() -> viewport ["x"] ["y"] ["width"] ["height"]; None when the element is not visible mouse.click()/keyboard.press() -> result dict; fields depend on the backend, so verify with snapshot() actions -> .evidence (a short line saying what happened) locator reads return plain str/int/bool/list directly.

If a locator fails, step DOWN one rung (don't jump):

  1. semantic page.get_by_role/label/text first choice
  2. css page.locator(css) role missing/unstable
  3. coordinates use locator.bounding_box() first for an exact, low-cost viewport rectangle; use a screenshot to explore only when the element is absent from snapshot() For captcha/login/2FA or any human-only step: await browser.handoff(reason, instructions) and stop — never automate them.

Frequently asked questions

What does the Browser AI skill do?

Drive a live browser with async Python against QwenPaw's builtin Browser SDK. The full reference is below; re-load this browser skill after context compaction.

Why use Browser on TypingMind?

Because you install it once and use it with any model. Browser is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Browser in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentscope-ai/QwenPaw/tree/main/src/qwenpaw/agents/skills/browser-en. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Browser?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Browser?

As many as you like. As long as a model supports skills, you can use Browser with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Browser AI skill free?

Yes. It is published on GitHub by agentscope-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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