diagnosing-bugs

Diagnose software bugs and performance regressions through deterministic feedback loops.

9|Updated Dec 1, 2025

Search Tags:#debugging #troubleshooting #root cause analysis #regression testing #performance tuning #reproducibility

Ready to run

npx skills add https://github.com/JohannesKonings/tanstack-aws --skill diagnosing-bugs-johanneskonings

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: diagnosing-bugs
Source: https://github.com/JohannesKonings/tanstack-aws/tree/main/.agents/skills/diagnosing-bugs
Command: npx skills add https://github.com/JohannesKonings/tanstack-aws --skill diagnosing-bugs-johanneskonings

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill solves the problem of aimless debugging by enforcing a rigorous, evidence-based feedback loop that prevents premature hypothesis generation and ensures bugs are caught by deterministic, reproducible signals.

Core Features & Use Cases

Feedback Loop Construction: Provides a structured methodology to build tight, red-capable signals (tests, scripts, or replays) that isolate the failure.
Minimization Strategy: Guides the reduction of complex failure states into the smallest possible reproducible scenario.
Hypothesis Testing: Enforces a falsifiable, ranked hypothesis framework to prevent bias and ensure efficient root-cause analysis.
Use Case: Use this when facing a non-deterministic performance regression or a hard-to-reproduce production error to systematically isolate the cause and verify the fix.

Quick Start

Invoke the diagnosing-bugs skill to begin a structured debugging session for the reported issue by establishing a tight feedback loop.

Frequently Asked Questions about diagnosing-bugs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I systematically debug a non-deterministic performance regression?▼

Systematically debug a non-deterministic performance regression by constructing tight, deterministic feedback loops that isolate failure modes and validate falsifiable hypotheses against controlled test seams.

What is the best way to minimize reproduction steps for a hard-to-reproduce production error?▼

The best way to minimize reproduction steps for a hard-to-reproduce production error is to reduce complex failure states into the smallest possible reproducible scenario using strict instrumentation discipline.

How does root cause analysis prevent premature hypothesis generation during troubleshooting?▼

Root cause analysis prevents premature hypothesis generation during troubleshooting by enforcing a rigorous, evidence-based feedback loop that requires ranked, falsifiable hypotheses validated through deterministic, reproducible signals.

Can I use shell scripts to isolate failure modes and maintain reproducibility?▼

Yes, you can use shell scripts to isolate failure modes and maintain reproducibility, as the process requires executing shell-based reproduction scripts to establish verifiable fixes and tight red-capable signals.

Do I need controlled test seams to validate falsifiable hypotheses when diagnosing bugs?▼

Yes, you need controlled test seams to validate falsifiable hypotheses when diagnosing bugs, ensuring that fixes are caught by deterministic, reproducible signals rather than aimless debugging.