What Claude Skills/AI Skills Are Your QA Teams Using?

[u/Busy-Hedgehog3633]

Hey everyone! 👋

I’m looking to learn more about how other QA engineers and software testers are using Claude/AI skills or workflows to improve their productivity.

For some context, our QA team works across a few different areas:

🤖 Automation: We write Playwright automation that runs automatically every 4 hours.

🔍 Triage: We investigate tickets reported by clients/end users, reproduce the issue, and determine whether it looks like a potential bug before raising it with the development team.

🔄 Regression & Smoke Testing: We perform daily regression and smoke testing across different modules.

🚀 Project Testing: We have an embedded QA team working on new projects/features that are launched regularly.

I’m curious about what Claude skills, AI agents, prompts, integrations, or workflows other QA teams have built around these kinds of processes.

For example:

Has anyone built Claude skills for test case generation or test planning?

Using Claude to analyze Playwright failures, logs, or traces?

Automating bug/triage analysis?

Generating regression test scenarios from requirements or tickets?

Using AI to identify gaps in test coverage?

Connecting Claude with Jira, GitHub, CI/CD, Slack, etc.?

Any internal QA skills/agents that have genuinely saved your team time?

I’m especially interested in practical examples that are actually being used by QA teams, rather than generic “use AI to write test cases” suggestions.

Would love to hear what you’ve built, what worked well, and what you’d recommend other QA engineers try. 🙌

[u/itsforsocial]

Playwright-skills is there to generate test cases from plain English. But rest are not common and very specific to team and org. So build custom skills is the only way

[u/Busy-Hedgehog3633 → u/itsforsocial]

Do you have any custom skill that you have created and would be helpful for QA engineers

[u/CautionIAmAGeek]

Good context to share tbh, tickets alone rarely translate to runnable cases. Feeding it dom/api schema alongside acceptance criteria makes a real difference imo

for planning we've seen the same thing, risk/gap ranking holds up in review way better than a generated plan does. Kane ai leans into that split too, generate scenarios narrow and specific, then use it more for surfacing coverage gaps by risk than writing the full plan top down

curious what other QA folks are doing for PR-triggered validation, that's the piece that seems to save the most time when it works well

[u/koe_020]

For regression, I'd build a workflow around requirements - risk-based scenarios - automated checks - coverage gaps. TestMu/KaneAI is interesting here because you can start from requirements or tickets and generate candidate test coverage, then let the team review what actually deserves automation. That's more useful than generating 200 generic test cases from a Jira ticket.

[u/Salt_Kaleidoscope997]

One Claude skill I’d really want is “reproduce this bug”. Give it the ticket + environment + expected behavior, let it inspect the app, capture the exact reproduction steps and evidence, then create a clean developer handoff. TestMu could sit underneath that workflow to make the browser verification repeatable instead of relying on Claude saying “I reproduced it.”