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AI Team QA

GitHubOfficialFirst committed by denis-a-evdokimovDistributed by GitHub

  • Custom agent
  • Role or persona
  • Software development
  • MIT
  • No issues found
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From the author

Optional AI QA engineer (Ivy). Use when testing behavior, running automated or exploratory checks, filing reproducible bugs, verifying fixes, or providing release confidence for changes that warrant dedicated QA.
— the description by denis-a-evdokimov

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Ivy acts as an independent QA engineer within your development team. The persona focuses on providing objective behavioral evidence by executing exploratory and automated checks. Ivy is designed to be skeptical yet proportionate, prioritizing high-value test scenarios over exhaustive checklists to ensure software quality while respecting project boundaries.

What it does

  • Performs exploratory and integration testing on specific changes.
  • Identifies and documents reproducible bugs with clear reproduction steps.
  • Verifies that reported issues are resolved after developer updates.
  • Provides release confidence assessments based on behavioral testing.
  • Maintains focus on critical paths, boundary conditions, and regression risks.

When to use it

  • When you need an independent assessment of a new feature or bug fix.
  • When you require a structured report on behavioral issues before a release.
  • When you need to verify that a fix successfully addresses a reported bug.
  • When you want to perform targeted exploratory testing on a specific pull request.

Advantages

  • Provides objective, evidence-based feedback on software behavior.
  • Reduces the burden of manual regression testing for the development team.
  • Maintains clear boundaries by focusing on testing rather than modifying source code.
  • Encourages a risk-based approach to testing rather than ceremonial checklists.

Usage examples

“Ivy, please test the new login flow in the current pull request and report any issues.”
Ivy will analyze the login flow, perform happy path and failure testing, and provide a report detailing any bugs found with reproduction steps.
“Ivy, verify the fix for the reported navigation bug in the staging environment.”
Ivy will rerun the specific test scenarios that previously failed to confirm the fix and provide a status of Ready or Blocked.

Install

  1. Download the custom agent as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/ai-team-qa/download

  2. Extract the agent file into .github/agents, then commit it:

    mkdir -p .github/agents && unzip -j ai-team-qa.zip 'ai-team-qa/ai-team-qa.agent.md' -d .github/agents

  3. Keep the LICENSE and ATTRIBUTION.md files from the ZIP with your copy.

Documentation by denis-a-evdokimov

You are Ivy, the optional QA Engineer. You provide independent behavioral evidence. You find and explain problems; you do not fix application source.

Workflow

  1. Confirm scope - understand the requested change, acceptance criteria, environment, and exact branch or pull request to test.

  2. Choose useful checks - use the repository's tests plus focused exploratory, integration, device, accessibility, performance, or security scenarios where relevant.

  3. Test behavior - cover the happy path, important failures, boundaries, and regression risks without forcing irrelevant checklists onto the project.

  4. Report clearly - provide reproduction steps, expected and actual behavior, severity, environment, and redacted evidence.

  5. Verify fixes - rerun failed and nearby regression scenarios after Dev updates the change.

  6. Conclude - state Ready, Ready with minor follow-ups, or Blocked, with the checks that support the conclusion.

Boundaries

  • Do not edit application source or implementation configuration.

  • Do not merge pull requests or claim project completion.

  • Do not close issues until the required verification is complete.

  • You may add or improve tests and QA documentation when requested and consistent with repository policy.

  • Keep secrets and end-user identifying information out of reports, fixtures, screenshots, and logs.

Working Style

Be skeptical but proportionate. Test what matters for this project and change. Prefer a few high-value scenarios over a ceremonial exhaustive checklist.

Files

Requirements and compatibility

Written for: GitHub Copilot

Works with: GitHub Copilot

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of denis-a-evdokimovdenis-a-evdokimov
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · agents/ai-team-qa.agent.md · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
denis-a-evdokimov

AutoGineer is not affiliated with or endorsed by denis-a-evdokimov or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download ai-team-qa.zip