debug

Systematic Debugging

PURPOSE: Find and fix the root cause of bugs through disciplined investigation. 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.

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The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

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

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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 multiple fixes
• Previous fix 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 (rushing guarantees rework)
• Stakeholders want it fixed NOW (systematic is faster than thrashing)

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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
   • 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 -- do not 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 the system has multiple components (CI -> build -> signing, API -> service -> database, frontend -> API -> cache -> database):

   BEFORE proposing fixes, add diagnostic instrumentation:

   For EACH component boundary:
     - Log what data enters the component
     - Log what data exits the component
     - Verify environment/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
   

   Example (multi-layer system):

   # Layer 1: Entry point
   log("=== Request received ===")
   log("Input:", sanitize(input))
   
   # Layer 2: Processing
   log("=== Processing layer ===")
   log("Config loaded:", config.isValid)
   log("Dependencies available:", checkDeps())
   
   # Layer 3: External call
   log("=== External call ===")
   log("Request:", sanitize(request))
   log("Response status:", response.status)
   log("Response body:", sanitize(response.body))
   
   # Layer 4: Output
   log("=== Final output ===")
   log("Result:", sanitize(result))
   

   This reveals: Which layer fails (e.g., entry -> processing OK, processing -> external FAILS).

5. Trace Data Flow

   WHEN the error is deep in a call stack:

   • 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

   Backward tracing technique:

   1. Start at the error site
   2. Identify the variable/value that is wrong
   3. Find where that variable was last assigned
   4. Was it wrong at that point? If yes, go to step 2 with the new assignment
   5. If no, the bug is between this assignment and the error site
   6. Narrow down until you find the exact line where correct becomes incorrect

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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?
   • If implementing a known pattern, find the canonical example

2. Compare Against References

   • If implementing a pattern, read the reference implementation COMPLETELY
   • Do not skim -- read every line
   • Understand the pattern fully before applying

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?

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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 the hypothesis
   • One variable at a time
   • Do not fix multiple things at once

3. Verify Before Continuing

   • Did it work? Yes -> Phase 4
   • Did not work? Form NEW hypothesis
   • DO NOT add more fixes on top

4. When You Do Not Know

   • Say "I do not understand X"
   • Do not pretend to know
   • Ask for help
   • Research more

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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
   • REQUIRED: Use tdd skill for writing proper failing tests

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?
   • REQUIRED: Use verify skill before claiming the fix works

4. If Fix Does Not 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)
   • DO NOT 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.

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

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Red Flags Table

If you catch yourself thinking any of these, STOP and return to Phase 1:

Red Flag	What It Means
"Quick fix for now, investigate later"	You are skipping root cause
"Just try changing X and see if it works"	You are guessing, not investigating
"Add multiple changes, run tests"	You cannot isolate what worked
"Skip the test, I'll manually verify"	Manual verification is unreliable
"It's probably X, let me fix that"	"Probably" means you do not know
"I don't fully understand but this might work"	Partial understanding = wrong fix
"Pattern says X but I'll adapt it differently"	Deviation without understanding = bugs
"Here are the main problems: [lists fixes]"	Proposing solutions before investigating
Proposing solutions before tracing data flow	You skipped the investigation
"One more fix attempt" (when already tried 2+)	3+ failures = architectural problem
Each fix reveals new problem in different place	Architecture is wrong, not the code

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User Signals You Are Doing It Wrong

Watch for these redirections from the user:

• "Is that not happening?" -- you assumed without verifying
• "Will it show us...?" -- you should have added evidence gathering
• "Stop guessing" -- you are proposing fixes without understanding
• "Think harder about this" -- question fundamentals, not just symptoms
• "We're stuck?" (frustrated) -- your approach is not working

When you see these: STOP. Return to Phase 1.

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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 do not stick. Test first proves it.
"Multiple fixes at once saves time"	Cannot isolate what worked. Causes new bugs.
"Reference too long, I'll adapt the pattern"	Partial understanding guarantees bugs. Read it completely.
"I see the problem, let me fix it"	Seeing symptoms does not equal understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, do not fix again.

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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	Differences listed and understood
3. Hypothesis	Form theory, test minimally, one variable at a time	Confirmed hypothesis or new one formed
4. Implementation	Create failing test, single fix, verify	Bug resolved, tests pass, no regressions

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When Process Reveals "No Root Cause"

If systematic investigation reveals the issue is truly environmental, timing-dependent, or external:

1. You have completed the process
2. Document what you investigated
3. Implement appropriate handling (retry, timeout, error message)
4. Add monitoring/logging for future investigation

But: 95% of "no root cause" cases are incomplete investigation.

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Integration

Called by:

• Any skill or workflow when a bug is encountered
• kickoff routes bugs through light brainstorm then here

Related skills:

• tdd -- for creating the failing test case (Phase 4, Step 1)
• verify -- for verifying the fix worked before claiming success

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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