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debug-helper

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Debug Helper

A systematic approach to debugging software issues.

Investigation Steps

  1. Reproduce the issue consistently
  2. Isolate the problem area
  3. Form a hypothesis
  4. Test the hypothesis
  5. Apply the fix
  6. Verify the solution

Common Debugging Techniques

  • Add logging at critical points
  • Use breakpoints and step-through debugging
  • Check input/output at boundaries
  • Review recent changes in version control
  • Compare working vs broken states

Error Analysis

  • Read error messages carefully
  • Check stack traces for root cause
  • Verify assumptions about data types and values
  • Look for off-by-one errors and null references

Source

git clone https://github.com/athola/skrills/blob/master/assets/demo-skills/debug-helper/SKILL.mdView on GitHub

Overview

Debug Helper provides a repeatable framework for identifying and fixing software bugs. It guides you through reproducing issues, isolating the problem, forming and testing hypotheses, applying fixes, and verifying results.

How This Skill Works

The approach follows a structured investigation: reproduce the issue, isolate the affected area, and formulate a testable hypothesis. You then test the hypothesis, apply the fix, and verify the solution, using logging, breakpoints, boundary checks, and comparison of current versus working states to pinpoint root causes.

When to Use It

  • Reproducing a new bug report consistently to establish a baseline
  • Diagnosing a regression after a code change
  • Debugging intermittent issues with a clear hypothesis testing loop
  • Verifying that a fix resolves the root cause and doesn’t introduce new issues
  • Onboarding teammates to a systematic debugging workflow

Quick Start

  1. Step 1: Reproduce the issue consistently to establish a baseline.
  2. Step 2: Isolate the problem area, add targeted logging or breakpoints, and collect evidence.
  3. Step 3: Form a hypothesis, test it, implement the fix, and verify the solution.

Best Practices

  • Reproduce issues deterministically before making changes
  • Isolate the smallest problematic area to reduce scope
  • Form testable hypotheses and design experiments to falsify them
  • Instrument with targeted logging, breakpoints, and boundary checks
  • Review recent changes and compare failing vs passing states to identify root cause

Example Use Cases

  • Debugging a null reference that surfaced after a recent merge by tracing input flow and adding boundary checks
  • Tracing a stack trace to locate the root cause of a failing API call through selective logging
  • Identifying an off-by-one error by reviewing loop boundaries and validating edge cases
  • Investigating a regression after a commit by comparing production vs staging state
  • Reproducing a flaky UI behavior with step-through debugging and instrumentation

Frequently Asked Questions

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