사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/melvynx/ai-maxxing-skills --skill code-debug시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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.
