CLI-First End-to-End Testing

Ship confident releases with full-flow coverage in minutes.

Prowl QA helps QA engineers and developers catch regressions before users do. Write readable tests in YAML, detect visual changes with built-in screenshot comparison, and generate hunts with AI — all from the terminal.

Install the CLI How it Works

$ npm install -g prowlqa

release-check.hunt.yamlcritical-flow suite

name: checkout-flow
steps:
  - navigate: "/cart"
  - click: "Checkout"
  - fill: "Email" "{{TEST_EMAIL}}"
  - click: "Pay now"
  - assert: visible "Order confirmed"

PASS 1 hunt, 5 steps, 0 failures

Why end-to-end testing has to be first-class

Fast shipping only works when critical user flows are always verified. Prowl QA turns that discipline into a lightweight daily habit.

Most regressions happen between features

Unit tests can pass while user journeys fail. Prowl QA validates real flow behavior across routes, forms, and state changes.

Manual QA does not scale with release speed

As teams ship faster, checklist testing becomes inconsistent. Prowl QA makes critical paths repeatable on every commit.

New builders need guardrails, not complexity

Vibe-coding teams can still ship safely by defining readable hunts and running them in a predictable CLI workflow.

How it works

A practical workflow for QA engineers, product developers, and teams adopting test discipline.

Step 01

Install and initialize

Install the CLI globally and bootstrap your first hunt in one command.

$ npm install -g prowlqa
$ prowlqa init

Step 02

Describe real user flows in YAML

Capture login, onboarding, checkout, and other critical journeys with human-readable steps.

name: checkout-flow
steps:
  - navigate: "/cart"
  - click: "Checkout"

Step 03

Run locally and in CI

Execute hunts from terminal, then wire the same commands into your pull request pipeline.

$ prowlqa run critical-flows --json

Step 04

Debug with artifacts

Use screenshots, traces, and logs to resolve issues quickly without reproducing flaky states manually.

PASS critical-flows
artifacts: ./prowl-artifacts/latest

name: login-flowsteps:  - navigate: "/login"  - fill:      "Email": "{{TEST_EMAIL}}"  - fill:      "Password": "{{TEST_PASSWORD}}"  - click: "Sign In"  - assert:      visible: "Dashboard"

Terminal

  ● Running hunt: login-flow    ✓ navigate "/login" (120ms)    ✓ fill "Email" (85ms)    ✓ fill "Password" (62ms)    ✓ click "Sign In" (340ms)    ✓ assert visible "Dashboard" (15ms)  PASS login-flow (622ms) 5/5 steps

Built for AI agents

Analyze pages, generate tests, and execute hunts — all through the CLI. No MCP server, no per-step reasoning, no context tax.

MCP tool-call approach

~15,000 tokens

# login.hunt.yaml
name: login-flow
steps:
  - navigate: "/login"
  - fill: "Email" "{{TEST_EMAIL}}"
  - fill: "Password" "{{TEST_PASSWORD}}"
  - click: "Sign In"
  - assert: visible "Dashboard"

Token budget consumed per test run

-MCP tool discovery schemas are loaded every session
-Agent issues per-step MCP calls (navigate/fill/click/assert)
-Round-trip MCP responses multiply token cost
-Flaky when agent misinterprets selectors

Prowl QA CLI

~1,000 tokens

$ prowlqa analyze https://app.com/login --json \
  | prowlqa generate --intent "test login" --stdout

$ prowlqa run login --json
PASS login (622ms) 5/5 steps
exitCode: 0

~15x fewer

Token budget consumed per test run

+Analyze a page, generate a hunt, run it — three commands
+Zero agent reasoning about browser steps
+Deterministic — same YAML, same result
+Structured JSON output and exit codes

Made for agents, controlled by humans.

Page analysis

prowlqa analyze <url> --json extracts interactive elements, selectors, and form structure. Agents get a structured page map without browser interaction.

AI hunt generation

prowlqa generate turns a page analysis and natural language intent into a valid, runnable YAML hunt file.

Structured execution

Every run returns machine-readable JSON with pass/fail, step timings, and artifact paths. Exit codes let agents branch without parsing.

Library API

Import prowlqa as a Node module. Run hunts programmatically and get typed results — no shell exec needed.

Agent integration guide

Built for web testing

YAML-first

Write tests in readable YAML. No JavaScript required. 26 step types cover navigation, forms, assertions, conditionals, mocking, and more.

Playwright precision

Full Playwright selector engine under the hood. data-testid, ARIA roles, text matching, CSS — use whatever works.

Safety guardrails

Forbidden selectors, allowed domains, and max step limits prevent tests from going off the rails.

Rich artifacts

Every run generates screenshots, console logs, network data, and Playwright traces. Debug failures instantly.

Visual regression

Compare screenshots against baselines to catch unintended visual changes. Pixel-level diff images show exactly what shifted.

Network mocking

Intercept API requests and return custom responses. Test error states, loading states, and empty states without touching your backend.

Conditional logic

Handle cookie banners, optional modals, and dynamic UI with if/repeat steps. Real-world flows need real-world control flow.

CI & agent ready

JSON output, JUnit XML reports, structured exit codes, and parallel execution. Plugs into any CI pipeline or AI agent workflow.

Get started in seconds

Four commands. Zero config files. You're testing in under a minute.

Install the CLI

npm install -g prowlqa

Install browser engine

npx playwright install chromium

Initialize your project

prowlqa init

Run your first hunt

prowlqa run homepage

FAQ

Answers for teams adopting end-to-end testing as part of daily delivery.

Is this only for QA engineers?

No. Prowl QA is designed for QA, product engineers, and teams new to end-to-end testing who need a simple CLI path to reliable flow coverage.

Do I need to know Playwright APIs?

No. You write hunts in YAML while Prowl QA handles browser automation and artifacts under the hood.

Can we run this in CI?

Yes. Prowl QA is built for CI execution with deterministic exit codes and artifact outputs suitable for pipelines and pull requests.

Can AI agents use Prowl QA?

Yes. Prowl QA includes a complete agent toolkit. prowlqa analyze extracts page structure as JSON, prowlqa generate turns that into a runnable hunt from a natural language intent, and prowlqa run --json returns structured results with exit codes. You can also import Prowl QA as a Node library. No MCP server or tool discovery needed.

Why not rely on manual testing only?

Manual checks miss regressions in large apps. Prowl QA gives repeatable coverage of critical user journeys on every release.

What is visual regression testing?

Visual regression compares screenshots against saved baselines to detect unintended visual changes. Prowl QA's assertScreenshot step does pixel-level comparison with a configurable threshold. On first run it saves the baseline automatically. Run prowlqa update-baselines to accept new screenshots as baselines.

Can Prowl QA generate tests automatically?

Yes. Run prowlqa analyze <url> --json to extract page elements, then pipe the output to prowlqa generate --intent 'describe the test' to produce a valid hunt file. The generated YAML is validated against Prowl's schema before output.

Does Prowl support network mocking?

Yes. The mockRoute step intercepts requests matching a URL pattern and returns a custom response — inline JSON or from a file. Use unmockRoute to remove the mock. This lets you test error, loading, and empty states without backend changes.

Ready to harden your release flow?

Install Prowl QA and start testing entire user journeys today.

Keep your velocity, reduce blind spots, and make end-to-end coverage a default part of shipping.

Install the CLI How it Works