debug-investigator

Structured debugging methodology that replaces ad-hoc exploration with hypothesis-driven investigation. Captures symptoms, analyzes evidence (stacktraces, logs, state), generates ranked hypotheses, designs bisection strategies, identifies instrumentation points, and produces minimal reproductions — documenting every step so dead ends are never revisited.

When to use this skill vs native debugging: The base model handles straightforward debugging (clear stacktraces, obvious errors) natively. Use this skill for non-obvious bugs requiring systematic investigation: intermittent failures, bugs with no clear stacktrace, performance regressions, or issues requiring git bisection and hypothesis ranking.

| references/stacktrace-patterns.md | Exception taxonomy, traceback reading, common Python/JS error signatures | Stacktrace or exception present | | references/hypothesis-templates.md | Bug category catalog, probability ranking, confirmation/refutation tests | Always |

假設驅動的調試方法：透過確認/反駁測試對假設進行排名、git bisect 策略、日誌分析、偵測點規劃和最小再現設計。觸發條件：「系統性地偵錯此問題」、「根本原因分析」、「平分此錯誤」、「對此錯誤的假設進行排名」、「幫助我隔離此問題」、「建立最小重現」、「此錯誤的檢測計劃」、「為什麼這一直失敗」。差異在於結構化調查方法（假設排名、二分策略、檢測點）——將此技能用於需要係統調查的非明顯錯誤，而不是模型直接診斷的簡單錯誤。不適用於沒有特定錯誤的抽象推理或問題分解 - 該模型本身處理一般推理。 來源：mathews-tom/praxis-skills。