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debugging-strategies

Diagnoses bugs and performance issues using a reproduce-hypothesize-verify workflow across codebases.

6|1|Updated Apr 5, 2026
Search Tags:#performance#debugging#root cause analysis#stack trace#profiling#ast-grep#production incidents
One-click install
npx skills add https://github.com/snewhouse/aa-ma-forge --skill debugging-strategies-snewhouse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debugging-strategies
Source: https://github.com/snewhouse/aa-ma-forge/tree/main/claude-code/skills/debugging-strategies
Command: npx skills add https://github.com/snewhouse/aa-ma-forge --skill debugging-strategies-snewhouse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you stop guessing and instead diagnose bugs, performance regressions, and unexpected behavior by following a repeatable, evidence-driven debugging workflow.

Core Features & Use Cases

  • Structured debugging process: Reproduce issues, gather targeted evidence, form hypotheses, and verify fixes through methodical testing.
  • Tooling and techniques across stacks: Practical guidance for JavaScript/TypeScript (debugger, breakpoints, profiling), Python (pdb/breakpoint/cProfile), and Go (Delve, stack traces, profiling).
  • Advanced approaches for stubborn problems: AST-based tracing with ast-grep, git bisect for regressions, differential debugging (working vs broken comparisons), and trace-debug patterns for call flow.
  • Common failure patterns and safeguards: Best practices, quick checklists, and guidance for production-safe debugging and avoiding noisy change sets.

Quick Start

Use the debugging-strategies skill to investigate a failing test by first creating a minimal reproduction and then collecting the full stack trace and relevant environment details.

Frequently Asked Questions about debugging-strategies

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

FAQPage Schema
What is the best way to find the root cause of intermittent failures and memory leaks in production?▼

Systematic debugging for production incidents requires creating a minimal reproduction, gathering targeted evidence like stack traces, forming hypotheses, and verifying fixes through methodical testing rather than guessing.

How do I debug performance issues across JavaScript, Python, and Go codebases?▼

Debugging performance issues across JavaScript, Python, and Go codebases involves stack-specific tooling like debugger and profiling for JS, cProfile for Python, and Delve for Go, combined with a structured workflow to isolate root causes.

Does this debugging approach work for complex distributed or async behaviors?▼

Yes, evidence-based debugging applies to complex distributed or async behaviors by systematically gathering information and testing hypotheses, supported by advanced techniques like differential debugging and trace-debug patterns for call flow analysis.

How do I use git bisect and AST tracing to find code regressions?▼

Finding code regressions uses git bisect for binary search through commits and AST-based tracing with ast-grep to isolate changes, forming part of an advanced differential debugging workflow that compares working versus broken states.

What are the limitations of systematic debugging for production environments?▼

Systematic debugging in production environments requires safeguards to avoid noisy change sets and ensure production-safe debugging, necessitating careful evidence gathering and minimal reproductions to prevent further system disruption.

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