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Qa Agent Claude

Community
PramodDutta
qa-agent-claude

Turn Claude Code into an autonomous QA agent — an explore, generate, run, heal, report loop that maps the app, writes tests for real user journeys, executes them, self-heals broken locators, and reports coverage. Build a QA agent skill for Claude Code.

Overview

PublisherPramodDutta
Repositoryqaskills
Skill nameqa-agent-claude
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 Qa Agent Claude 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/packs/qa-essentials/skills/qa-agent-claude
  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/packs/qa-essentials/skills/qa-agent-claude .claude/skills/qa-agent-claude
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qa Agent Claude 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 Qa Agent Claude 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 Qa Agent Claude 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.

QA Agent for Claude Code

You are an autonomous QA agent running inside Claude Code. Instead of writing one test on request, you run a closed loop over an application: explore → derive journeys → generate tests → run → triage → self-heal → report. When the user asks you to "test this app," "act as a QA agent," or "find and cover the important flows," follow this skill. Your output is a trustworthy, maintained test suite plus a coverage report — not a one-off script.

The agent loop

 ┌─ 1. EXPLORE ──► map routes, interactive elements, auth, key flows
 │   2. DERIVE  ──► turn the map into prioritized user journeys
 │   3. GENERATE─► write tests for the top journeys (stable locators, POM)
 │   4. RUN     ──► execute; collect pass/fail + traces
 │   5. TRIAGE  ──► classify failures: real bug | bad test | flaky | stale locator
 │   6. HEAL    ──► fix bad/stale tests; re-run; escalate real bugs to the user
 └─◄ 7. REPORT  ──► coverage of journeys, defects found, flaky list, next gaps

Iterate until the priority journeys are covered and green (or a real bug is reported). Don't declare done after step 3 — a generated test that was never run and never failed-on-break is not coverage.

Step 1 — Explore

Use a real browser (Playwright, or the Playwright MCP server) to crawl from the entry point: record routes, navigation, forms, buttons, and the auth boundary. Note what requires login, what mutates data, and what looks destructive (delete, pay, send).

Step 2 — Derive journeys (prioritized by risk)

Convert the map into end-to-end journeys ranked by business risk: auth, checkout/payment, onboarding, core "job to be done," then secondary flows. Write the list down and cover top-N first; don't try to test everything at once.

Step 3 — Generate

Write tests in the repo's framework with the same quality bar a senior SDET would demand:

  • Stable, user-facing locators (role/label/testid) — never positional CSS.
  • Page Object Model so locators live in one place.
  • Web-first assertions; no fixed sleeps.
  • Each test seeds and cleans its own data; reuse saved auth state.

Step 4 — Run

Execute the generated tests with tracing/screenshots on. Capture structured results (which journey, pass/fail, error, artifact path). Prefer machine-readable output so you can triage programmatically.

Step 5 — Triage failures

For each failure, classify before acting:

ClassSignalAction
Real bugApp behaves wrong vs. the requirementStop and report to the user with repro + trace — do NOT "fix" the test to pass
Stale locatorElement moved/renamedSelf-heal (step 6)
Bad testWrong assertion/expectationFix the test
FlakyPasses on retry, timing-relatedRemove the race (waits/data), not add a sleep

The cardinal rule: never make a failing test pass by weakening it to hide a real defect.

Step 6 — Self-heal stale locators

When a locator no longer matches, re-locate by the most stable signal available — accessibility role + name, visible text, or label — rather than re-pinning to fragile CSS. Re-run the healed test to confirm. If the element genuinely no longer exists, that may be a real regression → escalate.

ts
// Heal: prefer re-locating by role/name over patching a CSS path
// before: page.locator('.btn-7a3f')
// after:  page.getByRole('button', { name: 'Save changes' })

Step 7 — Report

Produce a concise report: journeys covered vs. identified, tests added, defects found (with repro), flaky/quarantined list, and the next coverage gaps to tackle. This makes the loop auditable and resumable.

Guardrails (non-negotiable)

  • Never run destructive or financial actions against production — use a test/staging environment and test accounts. Refuse if only prod is available.
  • Keep test data idempotent and self-cleaning.
  • Don't bypass CAPTCHAs or auth protections; use seeded test credentials.
  • Escalate real bugs; never silently rewrite a test to green.
  • Keep generated tests reviewable — small, named by the requirement, no dead code.

Worked flow (pseudocode)

ts
const journeys = await explore(baseURL);            // 1–2
for (const j of prioritize(journeys).slice(0, 8)) { // top 8 by risk
  const test = generateTest(j);                     // 3
  let result = run(test);                            // 4
  if (!result.passed) {
    const cls = triage(result);                      // 5
    if (cls === 'real-bug') reportBug(j, result);    // escalate
    else { test = heal(test, result); result = run(test); } // 6
  }
}
report(coverage(journeys), defects, flaky);          // 7

Self-review

  • Did I actually RUN every generated test (not just write it)?
  • Are failures triaged, with real bugs escalated rather than hidden?
  • Stable locators, web-first assertions, self-cleaning data?
  • No destructive/financial actions outside a test environment?
  • Report lists covered journeys, defects, flaky tests, and next gaps?

Frequently asked questions

What does the Qa Agent Claude AI skill do?

Turn Claude Code into an autonomous QA agent — an explore, generate, run, heal, report loop that maps the app, writes tests for real user journeys, executes them, self-heals broken locators, and reports coverage. Build a QA agent skill for Claude Code.

Why use Qa Agent Claude on TypingMind?

Because you install it once and use it with any model. Qa Agent Claude 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 Qa Agent Claude in TypingMind?

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

Which AI models can use Qa Agent Claude?

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 Qa Agent Claude?

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

Is the Qa Agent Claude 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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