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 skills add jackli12/codex-game-studio --skill run-qa-passgit clone --depth 1 https://github.com/jackli12/codex-game-studioWrote 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/skills/jackli12/codex-game-studio/run-qa-pass)<a href="https://agentmods.dev/skills/jackli12/codex-game-studio/run-qa-pass"><img src="https://agentmods.dev/badge/skills/jackli12/codex-game-studio/run-qa-pass/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/skills/jackli12/codex-game-studio/run-qa-pass"><img src="https://agentmods.dev/badge/skills/jackli12/codex-game-studio/run-qa-pass.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 | $0.00061 | $0.00230 |
| Opus 5.5 | $0.00024 | $0.00092 |
| Sonnet 5.5 | $0.00012 | $0.00046 |
| Haiku 4.5 | $0.00006 | $0.00023 |
Grade A, and why
run-qa-pass 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 1mo 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.
What it actually says
Run QA Pass
目标
为某个功能或构建定义并执行验证流程。
何时使用
- 某个功能已经准备好测试
- 某个构建需要验收或回归检查
工作流
- 定义测试范围
- 区分冒烟覆盖和回归覆盖
- 识别阻塞项
- 如果通过,把发布准备交给
release-manager
输出要求
- 测试范围
- 冒烟覆盖
- 回归覆盖
- 阻塞项
- 下一步交接对象
参考资料
../shared/references/qa-checklist-template.md../shared/references/risk-severity-guide.md
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.
- 1mo ago First seen · 36 lines · 61 tokens per session scan A dec40255c119
run-qa-pass is a skill published in the GitHub repository jackli12/codex-game-studio (20 stars, last pushed 4d ago), licensed MIT. It adds 61 tokens to every session and 230 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.
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test-playable-web-games
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godot-e2e
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Verify and release Three.js browser games: playtest QA, automated bot playtests, mobile and responsive checks, production builds, static-hosting base paths, debug gating, bundle review, screenshots, visual regression baselines, canvas-pixel inspection with measured metrics, and release risk reports.
pie-testing
Start, stop, and query Play-In-Editor (PIE) sessions for runtime testing of Blueprints, gameplay logic, widgets, AI, and any in-game behavior. Use when the user asks you to "play", "test", "run", "PIE", "start/stop the game", or otherwise needs a live game world to validate changes.
visual-qa
Inspect Godot screenshots or short frame sequences for acceptance issues. Use after capturing a scene, when checking camera framing, missing assets, UI overlap, collision visualization, animation, or a black/empty viewport.