debugging-toolkit-smart-debug

Diagnose software failures and performance regressions from debugging evidence.

Updated May 5, 2026

Search Tags:#debugging #logs #observability #traces #root-cause-analysis #stack-trace #incident-response

Ready to run

npx skills add https://github.com/Movchanets/Microservices_Learning --skill debugging-toolkit-smart-debug-movchanets

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: debugging-toolkit-smart-debug
Source: https://github.com/Movchanets/Microservices_Learning/tree/main/.agents/skills/debugging-toolkit-smart-debug
Command: npx skills add https://github.com/Movchanets/Microservices_Learning --skill debugging-toolkit-smart-debug-movchanets

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps diagnose software failures, performance regressions, and intermittent incidents by turning raw debugging signals into a clear root-cause path.

Core Features & Use Cases

Initial Triage: Parse error messages, stack traces, reproduction steps, and affected components to build a first-pass diagnosis.
Observability-Driven Analysis: Use logs, traces, metrics, and session data to correlate failures with deployments, environments, and user impact.
Fix and Validation Guidance: Generate ranked hypotheses, testing approaches, and post-fix checks so teams can verify the repair before release.
Use Case: A production checkout timeout can be investigated by comparing traces, identifying the slow dependency, testing the suspected code path, and validating the fix with monitoring.

Quick Start

Ask the skill to analyze the issue using the available error output, reproduction steps, environment details, and any logs or traces you have.

Frequently Asked Questions about debugging-toolkit-smart-debug

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

FAQPage Schema

How do I perform root-cause analysis for a production incident using logs and traces?▼

Root-cause analysis for a production incident requires parsing stack traces and correlating observability data like logs and traces to identify slow dependencies, rank hypotheses, and plan validation checks for safe fix verification.

What is the best way to diagnose intermittent outages across multiple services?▼

Diagnosing intermittent outages across services involves correlating observability metrics, session data, and traces with recent deployments to build a first-pass diagnosis and generate ranked failure hypotheses for targeted testing.

How do I troubleshoot a reproducible bug when I only have a stack trace?▼

Troubleshooting a reproducible bug from a stack trace requires parsing the error output and reproduction steps to map affected components, establishing a structured issue context to guide the debugging path.

Can I use observability data to investigate performance regressions in my application?▼

Observability data is essential for investigating performance regressions, allowing you to compare traces, identify slow dependencies, and correlate user impact with specific environment changes or recent deployments.

How do I validate a fix before releasing it after a complex software failure?▼

Validating a fix after a software failure requires generating testing approaches and post-fix checks based on ranked hypotheses, ensuring the repair is verified against monitoring data before release.