systematic-debugging

name	systematic-debugging
description	Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Enforces 4-phase root cause investigation: no fixes without understanding the root cause first.
argument-hint	Describe the bug, error, or unexpected behavior to debug.

Systematic Debugging

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.

Violating the letter of this process is violating the spirit of debugging.

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.

When to Use

Use for ANY technical issue:

• Test failures
• Bugs in production
• Unexpected behavior
• Performance problems
• Build failures
• Integration issues

Use this ESPECIALLY when:

• Under time pressure (emergencies make guessing tempting)
• "Just one quick fix" seems obvious
• You've already tried multiple fixes
• Previous fix didn't work
• You don't fully understand the issue

The Four Phases

You MUST complete each phase before proceeding to the next.

Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

1. Read Error Messages Carefully

   • Don't skip past errors or warnings
   • They often contain the exact solution
   • Read stack traces completely
   • Note line numbers, file paths, error codes

2. Reproduce Consistently

   • Can you trigger it reliably?
   • What are the exact steps?
   • Does it happen every time?
   • If not reproducible → gather more data, don't guess

3. Check Recent Changes

   • What changed that could cause this?
   • Git diff, recent commits
   • New dependencies, config changes
   • Environmental differences

4. Gather Evidence in Multi-Component Systems

   • For EACH component boundary: log what enters and exits
   • Run once to gather evidence showing WHERE it breaks
   • THEN analyze evidence to identify failing component
   • THEN investigate that specific component

5. Trace Data Flow

   • Where does bad value originate?
   • What called this with bad value?
   • Keep tracing up until you find the source
   • Fix at source, not at symptom

Phase 2: Pattern Analysis

Find the pattern before fixing:

1. Find Working Examples — locate similar working code in same codebase
2. Compare Against References — if implementing pattern, read reference implementation COMPLETELY
3. Identify Differences — list every difference between working and broken, however small
4. Understand Dependencies — what other components, settings, config, environment?

Phase 3: Hypothesis and Testing

Scientific method:

1. Form Single Hypothesis — "I think X is the root cause because Y"
2. Test Minimally — make the SMALLEST possible change to test hypothesis. One variable at a time.
3. Verify Before Continuing — did it work? Yes → Phase 4. Didn't work? Form NEW hypothesis.
4. When You Don't Know — say "I don't understand X". Don't pretend to know.

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create Failing Test Case — simplest possible reproduction. MUST have before fixing.

2. Implement Single Fix — address the root cause identified. ONE change at a time. No "while I'm here" improvements.

3. Verify Fix — test passes now? No other tests broken? Issue actually resolved?

4. If Fix Doesn't Work — STOP. Count: How many fixes have you tried?

   • If < 3: Return to Phase 1, re-analyze with new information
   • If ≥ 3: STOP and question the architecture (see step 5)

5. If 3+ Fixes Failed: Question Architecture

   • Is this pattern fundamentally sound?
   • Are we "sticking with it through sheer inertia"?
   • Should we refactor architecture vs. continue fixing symptoms?
   • Discuss with your human partner before attempting more fixes

Red Flags - STOP and Follow Process

If you catch yourself thinking:

• "Quick fix for now, investigate later"
• "Just try changing X and see if it works"
• "Add multiple changes, run tests"
• "Skip the test, I'll manually verify"
• "It's probably X, let me fix that"
• "I don't fully understand but this might work"
• Proposing solutions before tracing data flow
• "One more fix attempt" (when already tried 2+)
• Each fix reveals new problem in different place

ALL of these mean: STOP. Return to Phase 1.

Quick Reference

Phase	Key Activities	Success Criteria
1. Root Cause	Read errors, reproduce, check changes, gather evidence	Understand WHAT and WHY
2. Pattern	Find working examples, compare	Identify differences
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis
4. Implementation	Create test, fix, verify	Bug resolved, tests pass

Common Rationalizations

Excuse	Reality
"Issue is simple, don't need process"	Simple issues have root causes too. Process is fast for simple bugs.
"Emergency, no time for process"	Systematic debugging is FASTER than guess-and-check thrashing.
"Just try this first, then investigate"	First fix sets the pattern. Do it right from the start.
"I'll write test after confirming fix works"	Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"	Can't isolate what worked. Causes new bugs.
"I see the problem, let me fix it"	Seeing symptoms ≠ understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, don't fix again.

Related Skills

• superpowers:test-driven-development — For creating failing test case (Phase 4, Step 1)
• superpowers:verification-before-completion — Verify fix worked before claiming success