使い方
- インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
- 必要なツール、認証情報、対応環境を原文で確認します。
- 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/melvynx/ai-maxxing-skills --skill code-debug開始前の確認
- 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
- スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文
見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。
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.
