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vasilyu1983
vasilyu1983COMMUNITY
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Website𝕏@vasilyuGitHub

quality-debugging-troubleshooting

Diagnose software bugs and production incidents with systematic debugging workflows.

73|16|Updated Nov 14, 2025
Search Tags:#debugging#troubleshooting#root cause analysis#logging#observability#performance profiling#incidents
One-click install
npx skills add https://github.com/vasilyu1983/AI-Agents-public --skill quality-debugging-troubleshooting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quality-debugging-troubleshooting
Source: https://github.com/vasilyu1983/AI-Agents-public/tree/main/frameworks/claude-code-kit/initial-setup/skills/quality-debugging-troubleshooting
Command: npx skills add https://github.com/vasilyu1983/AI-Agents-public --skill quality-debugging-troubleshooting

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Diagnosing and resolving software bugs, performance issues, and production incidents can be chaotic and time-consuming. This Skill provides systematic debugging methodologies and troubleshooting workflows to quickly identify root causes.

Core Features & Use Cases

  • Systematic Debugging: Apply scientific method, binary search, and observability-first approaches to efficiently diagnose application crashes, slow performance, and unexpected behavior.
  • Production Incident Response: Utilize structured logging, distributed tracing, and error tracking to debug production-only bugs and manage incidents with clear playbooks.
  • Performance & Memory Analysis: Conduct CPU/memory profiling, heap snapshot analysis, and database query optimization to pinpoint and resolve performance bottlenecks and memory leaks.

Quick Start

Use the quality-debugging-troubleshooting skill to analyze a stack trace from a Node.js application crash and suggest the next steps for root cause analysis.

Frequently Asked Questions about quality-debugging-troubleshooting

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

FAQPage Schema
How do I debug a production incident using logging and distributed tracing?▼

Production debugging uses structured logging and distributed tracing to reconstruct request flows across services. Collect logs with consistent fields, enable trace correlation IDs, and query traces to isolate where failures occur. This reveals root causes invisible in single-service logs.

What's the best way to find performance bottlenecks in my application?▼

Performance profiling measures CPU, memory, and I/O usage to pinpoint bottlenecks. Use CPU profilers to find hot code paths, heap snapshots to detect memory leaks, and database query analysis to optimize slow queries. Profile under realistic load to surface real constraints.

How do I systematically narrow down the root cause of a bug?▼

Systematic debugging applies binary search and observability-first methods to isolate failures. Divide the problem space in half with each test, examine detailed logs and traces, and reproduce issues in controlled environments. This replaces guessing with reproducible diagnosis.

Can I use observability tools to debug application crashes?▼

Error tracking and observability tools capture stack traces, context, and system state at crash time. Structured logging records the execution path leading to failure, and distributed tracing shows interactions across components. Together they provide the full picture needed to reproduce and fix crashes.

What should I do when bugs only appear in production?▼

Production-only bugs require capturing live telemetry without stopping the system. Implement detailed error tracking, structured logging with business context, and distributed tracing to replay request paths. Analyze patterns across occurrences to identify environmental or load-dependent root causes.

How do I optimize database queries that are slowing down my application?▼

Query optimization begins with profiling to measure execution time and resource usage. Analyze query plans, identify missing indexes, and trace slow queries to high-traffic code paths. Test optimizations under production-like load before deploying.

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