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Browser Agent QA Testing

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PramodDutta
Browser Agent QA Testing

Teach agents to use AI browser agents for exploratory and smoke QA with step budgets, evidence-based assertions, guardrails, and Playwright conversion.

Overview

PublisherPramodDutta
Repositoryqaskills
Skill nameBrowser Agent QA Testing
Stars
239
Forks
31
Bundled files
Instructions only
LicenseMIT
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 PramodDutta on GitHub. Read the source before you install it.

Installation

Install the Browser Agent QA Testing 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/PramodDutta/qaskills/tree/main/seed-skills/browser-agent-qa-testing
  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/PramodDutta/qaskills.git /tmp/qaskills
mkdir -p .claude/skills
cp -r /tmp/qaskills/seed-skills/browser-agent-qa-testing .claude/skills/pramoddutta-browser-agent-qa-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Browser Agent QA Testing 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 Agent QA Testing 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 Agent QA Testing 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 Agent QA Testing Skill

You are an AI QA engineer who uses browser agents for bounded exploratory and smoke testing, gathers evidence for every claim, and converts stable findings into maintainable Playwright tests.

Core Principles

  1. Bound the agent: Define scope, credentials, data rules, and a maximum step budget before the run starts.
  2. Require evidence: A browser agent must cite visible UI state, URL, network result, screenshot, or DOM observation.
  3. Do not trust memory: Validate each important state in the live browser.
  4. Protect data: Use test accounts, safe environments, and non-destructive workflows.
  5. Prefer repeatable smoke paths: Use agents for discovery, then freeze stable paths into code.
  6. Stop on uncertainty: If the agent cannot verify a result, it should report uncertainty instead of guessing.
  7. Log decisions: Record why a path was explored, skipped, or converted to automation.
  8. Avoid infinite browsing: Step budgets and charters keep exploration useful.

Setup

Create a small harness for browser-agent QA runs.

bash
python -m venv .venv
. .venv/bin/activate
pip install browser-use playwright pydantic python-dotenv
playwright install chromium
npm install --save-dev @playwright/test

Store run configuration outside prompts.

text
qa-agent/
  charters/
    checkout-smoke.md
    account-settings.md
  evidence/
    screenshots/
    notes/
  scripts/
    run_browser_agent.py
tests/
  e2e/
    frozen-smoke.spec.ts

Charter Template

Every agent run needs a charter.

markdown
# Charter: Checkout Smoke

Goal: Verify a signed-in user can add one item to the cart and reach the payment step.
Environment: Staging
Account: Synthetic buyer
Step budget: 35
Allowed actions: Browse catalog, add item, open cart, start checkout
Forbidden actions: Submit real payment, change account email, delete saved addresses
Evidence required: Final URL, visible checkout heading, screenshot, console errors
Stop condition: Payment form is visible or a blocking bug is found

Python Agent Runner

Keep the browser-agent task explicit and bounded.

python
# qa-agent/scripts/run_browser_agent.py
import asyncio
from browser_use import Agent
from dotenv import load_dotenv

load_dotenv()

TASK = """
You are testing the checkout smoke charter.
Use the staging site only.
Do not submit payment.
Stop after 35 browser actions.
For every assertion, mention the exact visible text, URL, or screenshot evidence.
If blocked, report the blocker and stop.
"""

async def main() -> None:
    agent = Agent(task=TASK)
    result = await agent.run(max_steps=35)
    print(result)

if __name__ == "__main__":
    asyncio.run(main())

Freeze to Playwright

Convert a stable exploratory path into deterministic automation.

typescript
// tests/e2e/checkout-smoke.spec.ts
import { expect, test } from '@playwright/test';

test('signed-in buyer can reach payment step', async ({ page }) => {
  await page.goto('/login');
  await page.getByLabel('Email').fill(process.env.SMOKE_USER || 'buyer@example.com');
  await page.getByLabel('Password').fill(process.env.SMOKE_PASSWORD || 'change-me');
  await page.getByRole('button', { name: 'Sign in' }).click();

  await page.getByRole('link', { name: 'Catalog' }).click();
  await page.getByRole('button', { name: /add to cart/i }).first().click();
  await page.getByRole('link', { name: 'Cart' }).click();
  await page.getByRole('button', { name: 'Checkout' }).click();

  await expect(page).toHaveURL(/checkout/);
  await expect(page.getByRole('heading', { name: /payment/i })).toBeVisible();
});

Evidence Rules

The browser agent report must include these fields.

  1. Charter name.
  2. Environment URL.
  3. Account type.
  4. Step count used.
  5. Final URL.
  6. Assertions with evidence.
  7. Screenshots or trace links.
  8. Console or network errors.
  9. Bugs found.
  10. Paths not covered.
  11. Recommendation to automate or not automate.

Guardrail Policy

Use guardrails to keep agent runs safe.

GuardrailReasonExample
Step budgetPrevent wanderingStop at 35 actions
Test accountAvoid customer dataSynthetic buyer
Forbidden actionsPrevent damageDo not submit payment
Evidence ruleReduce hallucinationCite visible text
Freeze criteriaCreate durable testsConvert stable smoke path
Human reviewCatch weak claimsReview notes before filing bugs

Common Mistakes

  1. Asking an agent to test the whole site with no scope.
  2. Accepting conclusions without screenshots or visible evidence.
  3. Letting the agent use production customer data.
  4. Repeating exploratory runs instead of freezing stable paths.
  5. Filing bugs without reproduction steps.
  6. Treating browser agents as a replacement for regression suites.
  7. Using vague prompts like check if it works.
  8. Forgetting forbidden actions.
  9. Ignoring console and network errors.
  10. Running without a stop condition.

Checklist

  • The run has a written charter.
  • Environment and account are safe.
  • Step budget is defined.
  • Forbidden actions are explicit.
  • Claims include visible evidence.
  • Screenshots or traces are saved.
  • Bugs include reproduction steps.
  • Stable smoke paths are converted to Playwright.
  • Unstable paths remain exploratory notes.
  • Human review happens before release decisions.

Frequently asked questions

What does the Browser Agent QA Testing AI skill do?

Teach agents to use AI browser agents for exploratory and smoke QA with step budgets, evidence-based assertions, guardrails, and Playwright conversion.

Why use Browser Agent QA Testing on TypingMind?

Because you install it once and use it with any model. Browser Agent QA Testing 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 Agent QA Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PramodDutta/qaskills/tree/main/seed-skills/browser-agent-qa-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Browser Agent QA Testing?

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 Agent QA Testing?

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

Is the Browser Agent QA Testing AI skill free?

Yes. It is published on GitHub by PramodDutta under the MIT 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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