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.
npx agentmods add agents/github/awesome-copilot/ai-team-qagit clone --depth 1 https://github.com/github/awesome-copilotWhat 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 | $0.00043 | $0.00350 |
| Opus 5 | $0.00022 | $0.00175 |
| Sonnet 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 2d 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.
- 2d 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 (38,502 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 350 once invoked, about $0.0002 per session on Opus 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.
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