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diagnose

Plan and execute a disciplined debugging workflow to diagnose hard bugs.

1|Updated Dec 20, 2025
Search Tags:#testing#debugging#instrumentation#diagnosis#hypothesis#reproduction#hitl-loop
One-click install
npx skills add https://github.com/ari-ayvazyan/AISkills --skill diagnose-ari-ayvazyan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diagnose
Source: https://github.com/ari-ayvazyan/AISkills/tree/main/skills/diagnose
Command: npx skills add https://github.com/ari-ayvazyan/AISkills --skill diagnose-ari-ayvazyan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill provides a repeatable, disciplined debugging workflow to diagnose hard bugs quickly by building a feedback loop, reproducing issues, forming hypotheses, instrumenting code paths, and validating fixes.

Core Features & Use Cases

  • Structured debugging loop for reproducible bug diagnosis across unit, integration, and performance issues.
  • Guided hypothesis generation and instrumentation to minimize investigation time and increase fix confidence.
  • Launchpad for experiments using a hitl-loop scaffold to validate changes before committing.

Quick Start

Run the hitl-loop template and follow the prompts to begin the interactive debugging cycle.

Frequently Asked Questions about diagnose

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

FAQPage Schema
What is a structured debugging loop for diagnosing flaky tests and performance regressions?▼

A structured debugging loop is a disciplined workflow that reproduces issues, forms hypotheses, instruments code paths, and validates fixes to diagnose hard bugs quickly and repeatably.

How do I debug elusive failures across unit, integration, and end-to-end scopes?▼

You can debug elusive failures by running a hitl-loop scaffold that interactively guides you through reproducing issues, generating hypotheses, and instrumenting code paths to map them to verifiable outcomes.

What's the best way to form hypotheses and instrument code paths during bug diagnosis?▼

The best way to form hypotheses is using guided generation alongside structured instrumentation, which minimizes investigation time and maps hypotheses to verifiable outcomes before validating fixes.

Do I need a predefined scaffold to run a disciplined debugging workflow?▼

Yes, you need a predefined hitl-loop scaffold, specifically a template script, to launch experiments and begin the interactive debugging cycle that validates changes before committing.

When should I use a guided hypothesis loop instead of standard debugging?▼

You should use a guided hypothesis loop when facing hard bugs like flaky tests, performance regressions, or elusive failures where standard debugging lacks the structure needed for verifiable outcomes.

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