The Five Phases

You MUST complete each phase before proceeding to the next.

Phase 0: Context Recall (MANDATORY FIRST STEP)

BEFORE doing ANYTHING else:

1. Extract Keywords from Error

   • What's the error type? (OOM, timeout, connection, type error...)
   • What component? (server, browser, API, database...)
   • What area of the codebase?

2. Search for Prior Knowledge

   • Check project docs, MEMORY files, or past conversations
   • Search codebase for similar error patterns: grep -r "ErrorType" .
   • Check git log for related recent changes: git log --oneline -20

3. Review Results

   • Found relevant experience? -> Apply directly, skip to Phase 4
   • Found partial match? -> Use as starting point for Phase 1
   • Nothing found? -> Proceed to Phase 1, remember to record solution later

4. Output Format

   Context Recall: - Query: "xxx" - Found: [description of related knowledge] - Action: [apply experience / continue investigation / no match]

VIOLATION: Proceeding to Phase 1 without Context Recall output = process failure.

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

WHEN system has multiple components (CI - > build -> signing, API -> service -> database):

BEFORE proposing fixes, add diagnostic instrumentation:

    For EACH component boundary:
  - Log what data enters component
  - Log what data exits component
  - Verify environment/config propagation
  - Check state at each layer

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
   • What works that's similar to what's broken?

2. Compare Against References

   • If implementing pattern, read reference implementation COMPLETELY
   • Don't skim - read every line
   • Understand the pattern fully before applying

3. Identify Differences

   • What's different between working and broken?
   • List every difference, however small
   • Don't assume "that can't 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 Single Hypothesis

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

2. Test Minimally

   • Make the SMALLEST possible change to test hypothesis
   • One variable at a time
   • Don't fix multiple things at once

3. Verify Before Continuing

   • Did it work? Yes -> Phase 4
   • Didn't work? Form NEW hypothesis
   • DON'T add more fixes on top

4. When You Don't Know

   • Say "I don't understand X"
   • Don't pretend to know
   • Ask for help
   • Research more

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create Failing Test Case

   • Simplest possible reproduction
   • Automated test if possible
   • One-off test script if no framework
   • MUST have before fixing

2. Implement Single Fix

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

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 (step 5 below)
   • DON'T attempt Fix #4 without architectural discussion

5. If 3+ Fixes Failed: Question Architecture

Pattern indicating architectural problem:

 * Each fix reveals new shared state/coupling/problem in 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 vs. continue fixing symptoms?

Discuss with the user 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"
• "Pattern says X but I'll adapt it differently"
• "Here are the main problems: [lists fixes without investigation]"
• 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.

If 3+ fixes failed: Question the architecture (see Phase 4.5)

Common Rationalizations

Quick Reference

Real-World Impact

From debugging sessions:

• Systematic approach: 15-30 minutes to fix
• Random fixes approach: 2-3 hours of thrashing
• First-time fix rate: 95% vs 40%
• New bugs introduced: Near zero vs common

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