使用方法
- 复制安装命令,在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/melvynx/ai-maxxing-skills --skill code-debug开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Build the feedback loop first. Name one fast, deterministic, agent-runnable command that exercises the user's exact symptom, run it, and capture the red result. Tighten and minimise it until every remaining element is load-bearing.
No red-capable loop means no hypotheses. If a loop cannot be built, report what was tried and request the missing access or artifact.
Once red:
- Rank 3 to 5 falsifiable hypotheses, each with a prediction.
- Test one variable at a time; prefer a debugger, then uniquely tagged targeted logs. Measure performance regressions before changing them.
- Compare the viable fixes and choose the smallest one that addresses the evidenced cause. In
-aorautomode, use the recommended reversible option without waiting for confirmation. - Turn the minimal repro into a failing regression test at the real seam when one exists, then implement the fix and watch it pass. Document when the architecture provides no honest seam.
- Re-run the original unminimised loop and exercise the real runtime path. Passing tests alone do not prove the bug is fixed.
Before finishing, remove temporary instrumentation and throwaway harnesses, state the confirmed root cause, and pass the result through the verify skill. Finish only when the original symptom is proven gone or report BLOCKED: NOT PROVEN.



