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 |

假设驱动的调试方法：通过确认/反驳测试对假设进行排名、git bisect 策略、日志分析、检测点规划和最小再现设计。触发条件：“系统地调试此问题”、“根本原因分析”、“平分此错误”、“对此错误的假设进行排名”、“帮助我隔离此问题”、“创建最小重现”、“此错误的检测计划”、“为什么这一直失败”。区别在于结构化调查方法（假设排名、二分策略、检测点）——将此技能用于需要系统调查的非明显错误，而不是模型直接诊断的简单错误。不适用于没有特定错误的抽象推理或问题分解 - 该模型本身处理一般推理。 来源：mathews-tom/praxis-skills。