Diagnose bugs and performance regressions with reproducible feedback loops.
npx skills add https://github.com/mpsuesser/workspace --skill diagnose-mpsuesser
Please help me install this Agent Skill. Skill: diagnose Source: https://github.com/mpsuesser/workspace/tree/main/dotconfig/pi/agent/skills/.deprecated/diagnose Command: npx skills add https://github.com/mpsuesser/workspace --skill diagnose-mpsuesser
This Skill helps you systematically diagnose hard bugs and performance regressions by turning vague symptoms into a reproducible, testable cause.
Ask the AI to diagnose a failing bug you’re seeing by first constructing a tight feedback loop and then running the loop until the symptom reproduces reliably.
High-intent search queries and answers about installing and using this skill.
To find the root cause of a hard bug, you must first build a deterministic feedback loop that reliably reproduces the exact failure mode. Then test ranked, falsifiable hypotheses using targeted instrumentation until you isolate the cause.
The best way to debug a failing test without guessing is to adopt a reproduce-first workflow. Construct a tight pass/fail signal using scripts or harnesses, confirm the symptom, and then probe ranked hypotheses systematically.
You diagnose a performance regression by building a fast feedback loop that captures the slowdown, formulating falsifiable predictions about the bottleneck, and adding targeted instrumentation to verify those specific hypotheses.
Yes, a reproduce-first workflow effectively fixes thrown errors in HTTP or CLI scripts by constructing deterministic test harnesses. This ensures you are chasing the exact reported failure before attempting a fix.
After fixing a bug, you should write a regression test to lock in the fix and clean up any debug instrumentation artifacts. This disciplined loop leaves the codebase safer and confirms the root cause is resolved.
Use hypothesis testing for broken behavior when you need root-cause certainty and face vague symptoms. It transforms guessing into falsifiable predictions, ensuring your instrumentation directly validates or eliminates suspected causes.
miptah21Diagnose bugs and performance regressions through an evidence-based debugging loop.
RobinNorbergRemove dead code and duplicate logic from target modules while preserving behavior.
janpeterdDiagnose bugs and performance regressions using deterministic pass/fail feedback loops.
timequityIdentify performance bottlenecks in web applications, backend services, and databases.
