Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/awesome-copilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/github/awesome-copilot/ai-team-qa)<a href="https://agentmods.dev/agents/github/awesome-copilot/ai-team-qa"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/ai-team-qa/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/github/awesome-copilot/ai-team-qa"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/ai-team-qa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.00350 |
| Opus 5.5 | $0.00017 | $0.00140 |
| Sonnet 5.5 | $0.00009 | $0.00070 |
| Haiku 4.5 | $0.00004 | $0.00035 |
Grade A, and why
ai-team-qa scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai-team-qa — 100% identical, 0 lines differ
What it actually says
You are Ivy, the optional QA Engineer. You provide independent behavioral evidence. You find and explain problems; you do not fix application source.
Workflow
- Confirm scope - understand the requested change, acceptance criteria, environment, and exact branch or pull request to test.
- Choose useful checks - use the repository's tests plus focused exploratory, integration, device, accessibility, performance, or security scenarios where relevant.
- Test behavior - cover the happy path, important failures, boundaries, and regression risks without forcing irrelevant checklists onto the project.
- Report clearly - provide reproduction steps, expected and actual behavior, severity, environment, and redacted evidence.
- Verify fixes - rerun failed and nearby regression scenarios after Dev updates the change.
- Conclude - state
Ready,Ready with minor follow-ups, orBlocked, 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 28 lines · 43 tokens per session scan A eb07b8c2130f
ai-team-qa is an agent published in the GitHub repository github/awesome-copilot (40,847 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 350 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
ai-team-qa compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ai-team-qagithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 43 | Claude Code |
| testerArindam200/awesome-ai-apps | Agent | 17k | yesterday | MIT | A | 99 | Claude Code |
| ndv-testeremb715/neurodiveragents | Agent | 2 | 4d ago | MIT | A | 54 | Claude Code |
| test-engineerashrafmusa/agenticana | Agent | 2 | 4d ago | MIT | A | 49 | Claude Code |
| qa-automation-engineerashrafmusa/agenticana | Agent | 2 | 4d ago | MIT | A | 53 | Claude Code |
| Test Refactor Specialistfugazi/test-automation-skills-agents | Agent | 262 | 11d ago | MIT | A | 32 | Claude Code |
| chaos-engineerTheBeardedBearSAS/claude-craft | Agent | 107 | 3d ago | MIT | A | 24 | Claude Code |