systematic-debugging

name	systematic-debugging
description	Four-phase debugging methodology: root cause investigation, pattern analysis, hypothesis testing, and TDD-based fix implementation. Enforces evidence-first discipline and stops random fix attempts. Use when encountering any bug, test failure, or unexpected behavior — especially when under time pressure or after a fix attempt has already failed.

Systematic Debugging

Find root cause before attempting fixes. Random patches waste time and create new bugs.

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you have not completed Phase 1, you cannot propose fixes. Period.

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 have already tried a fix and it did not work
• you do not fully understand the issue

Do not skip when:

• issue seems simple (simple bugs have root causes too)
• you are in a hurry (systematic is faster than thrashing)

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

   • Do not skip past errors or warnings
   • Read stack traces completely
   • Note line numbers, file paths, error codes

2. Reproduce consistently

   • Can you trigger it reliably?
   • What are the exact steps?
   • If not reproducible, gather more data — do not guess

3. Check recent changes

   • git diff, recent commits
   • New dependencies, config changes
   • Environmental differences

4. Gather evidence in multi-component systems

   When the system has multiple components:

   For EACH component boundary:
     - Log what data enters the component
     - Log what data exits the component
     - Verify environment and config propagation
     - Check state at each layer
   
   Run once to gather evidence showing WHERE it breaks.
   THEN analyze evidence to identify the failing component.
   THEN investigate that specific component.
   

5. Trace data flow

   • Where does the bad value originate?
   • What called this with the 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 the same codebase
   • What works that is similar to what is broken?

2. Compare against references

   • If implementing a pattern, read the reference implementation completely
   • Do not skim — read every line

3. Identify differences

   • What is different between working and broken?
   • List every difference, however small
   • Do not assume "that cannot matter"

4. Understand dependencies

   • What other components does this need?
   • What settings, config, environment?
   • What assumptions does it make?

Phase 3: Hypothesis and Testing

Scientific method:

1. Form a single hypothesis

   • State clearly: "I think X is the root cause because Y"
   • Be specific, not vague

2. Test minimally

   • Make the SMALLEST possible change to test the hypothesis
   • One variable at a time
   • Do not fix multiple things at once

3. Verify before continuing

   • Did it work? → Phase 4
   • Did not work? → Form NEW hypothesis
   • Do NOT add more fixes on top

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create a failing test case

   • Simplest possible reproduction
   • Automated test if possible
   • MUST have before fixing
   • Follow the TDD Iron Law from $execute-plan-tdd

2. Implement a single fix

   • Address the root cause identified
   • ONE change at a time
   • No "while I am here" improvements
   • No bundled refactoring

3. Verify the fix

   • Test passes now?
   • No other tests broken?
   • Issue actually resolved?

4. If the fix does not work — count your attempts

   • Fewer than 3 attempts: return to Phase 1 with new information
   • 3 or more attempts: STOP — question the architecture (see below)

When 3+ Fixes Fail: Question Architecture

Pattern indicating an architectural problem:

• Each fix reveals new shared state, coupling, or problems in a different place
• Fixes require massive refactoring to implement
• Each fix creates new symptoms elsewhere

STOP and question fundamentals:

• Is this pattern fundamentally sound?
• Are we sticking with it through sheer inertia?
• Should we refactor architecture instead of continuing to fix symptoms?

Discuss with the user before attempting more fixes.

This is NOT a failed hypothesis — this is a wrong architecture.

Red Flags — STOP and Return to Phase 1

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 will manually verify"
• "It is probably X, let me fix that"
• "I do not fully understand but this might work"
• "One more fix attempt" (when already tried 2+)
• Proposing solutions before tracing data flow

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

Common Rationalizations (all wrong)

Excuse	Reality
"Issue is simple, do not 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 will write test after confirming fix works"	Untested fixes do not stick. Test first proves it.
"Multiple fixes at once saves time"	Cannot isolate what worked. Causes new bugs.
"I see the problem, let me fix it"	Seeing symptoms is not understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, do not fix again.

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

Relationship To Other Skills

• $break-loop: Use when the AI is stuck in a repetitive loop. systematic-debugging is for when YOU encounter a bug.
• $execute-plan-tdd: Phase 4 follows the same TDD Iron Law. Write failing test before fix.
• $harvest-learnings: After fixing a subtle bug, run harvest-learnings to promote the lesson into spec.

Checkpoint — Context Continuity

After identifying root cause (Phase 1-2 complete), save diagnostic findings to .fusion/:

python3 .trellis/scripts/fusion/checkpoint.py \
  --status "root cause identified" \
  --source "systematic-debugging" \
  --decision "root cause: <description>::<evidence>" \
  --next "implement fix with TDD"

This preserves diagnostic conclusions across session boundaries, preventing repeated investigation.

Completion Message

Use a closing message shaped like this:

Debugging complete for <issue description>.

Root cause: <what was actually wrong>
Fix: <what was changed>
Test: <test that proves it>
Phases traversed: <1-4, how many hypothesis cycles>

Next steps:
1. Run /fusion:checkpoint to persist debugging conclusions
2. Run $harvest-learnings (if the bug revealed a reusable lesson)
3. Run $check
4. Run $finish-work