Debugging Systematically

---
name: debugging-systematically
description: Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions
---

# 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

**Don't skip when:**
- Issue seems simple (simple bugs have root causes too)
- You're in a hurry (rushing guarantees rework)
- Manager wants it fixed NOW (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**
   - 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
   ```

   **Example (multi-layer system):**
   ```bash
   # Layer 1: Workflow
   echo "=== Secrets available in workflow: ==="
   echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"

   # Layer 2: Build script
   echo "=== Env vars in build script: ==="
   env | grep IDENTITY || echo "IDENTITY not in environment"

   # Layer 3: Signing script
   echo "=== Keychain state: ==="
   security list-keychains
   security find-identity -v

   # Layer 4: Actual signing
   codesign --sign "$IDENTITY" --verbose=4 "$APP"
   ```

   **This reveals:** Which layer fails (secrets → workflow ✓, workflow → build ✗)

5. **Trace Data Flow**

   **WHEN error is deep in call stack:**

   **REQUIRED SUB-SKILL:** Use wrangler:tracing-root-causes for backward tracing technique

   **Quick version:**
   - 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
   - **REQUIRED SUB-SKILL:** Use wrangler:practicing-tdd for writing proper failing tests
   - **IMPORTANT:** Must provide TDD Compliance Certification (see practicing-tdd skill) for the fix

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 implementing-issue
   - 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 your human partner before attempting more fixes**

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

## Distinguishing Architectural vs Implementation Problems

### Architectural Problem Indicators

**Strong signals** (any one indicates architectural issue):

1. **Shared state in multiple places**
   - Same data stored in 3+ locations (localStorage, context, store, DB cache)
   - Updates to one location don't propagate to others
   - Synchronization bugs appear repeatedly

2. **Tight coupling across modules**
   - Fix in module A breaks module B unexpectedly
   - Can't change one component without changing 5 others
   - Dependencies are circular or tangled

3. **Missing abstraction layer**
   - Similar code repeated in 10+ places
   - Fix requires updating all instances
   - No central place to change behavior

4. **Wrong separation of concerns**
   - UI logic mixed with business logic
   - Database queries in presentation layer
   - Can't test one piece without testing everything

### Implementation Problem Indicators

**Strong signals** (indicates implementation bug, not architecture):

1. **Single root cause, multiple symptoms**
   - All failures trace back to one incorrect assumption
   - Fix in one place resolves all symptoms
   - Not coupled to other modules

2. **Edge case handling**
   - Works in 99% of cases, fails on corner cases
   - Fix is adding bounds checking or validation
   - Isolated to one function/module

3. **Timing or concurrency issue**
   - Race condition with clear sequence
   - Fix is synchronization primitive
   - Not a design problem, execution order problem

### When to Question Architecture

After 3 failed fix attempts:

**IF any Strong architectural signal present:**
  Stop fixing symptoms
  Discuss architectural refactor with your human partner

**IF only Implementation signals:**
  Return to Phase 1 (re-investigate with new information)
  May still be solvable without architectural change

## Examples

### Example: Architectural Problem

**Scenario:** Auth token refresh bug

Fix attempts:
1. Add refresh in API client → Store not updated
2. Update store in API client → Components not re-rendering
3. Add re-render trigger → Race condition with logout

**Analysis:**
- Pattern: Each fix reveals new shared state issue
- Root cause: Auth state stored in 4 places (localStorage, React context, API client, URL params)
- Architectural issue: No single source of truth for auth state

**Correct action:** Refactor to single auth state manager. Fixes are band-aids.

### Example: Implementation Problem

**Scenario:** Token refresh happening 100ms early

Fix attempts:
1. Adjust expiry check to `< expiryTime - 100` → Still fails occasionally
2. Adjust to `< expiryTime - 200` → Works but feels wrong
3. Check token validity in addition to expiry → Reveals expiry time parsing bug

**Analysis:**
- Pattern: Getting closer to root cause with each attempt
- Root cause: Expiry time parsing doesn't account for timezone
- Implementation issue: Single bug with misleading symptoms

**Correct action:** Fix parsing logic. Architecture is fine.

## Having the Architectural Discussion

When you've determined it's an architectural problem:

**Prepare your case:**

1. **Summarize the issue:**
   "I've attempted 3 fixes for [problem]. Each revealed new issues in [places]. This indicates an architectural problem: [specific issue]."

2. **Present the evidence:**
   - Fix 1: [what you tried] → [what failed]
   - Fix 2: [what you tried] → [what failed]
   - Fix 3: [what you tried] → [what failed]
   - Pattern: [which architectural indicator matches]

3. **Propose options:**
   "Possible approaches:
    A) Refactor [component] to [new architecture] (high effort, solves root cause)
    B) Continue fixing symptoms (low effort, technical debt)
    C) Defer to later, document workaround (lowest effort, future pain)

   I recommend A because [reasoning]."

4. **Ask for decision:**
   "Should we refactor the architecture now, or work around it?"

**Don't:**
- Just say "I'm stuck"
- Propose fixes without explaining why architecture is wrong
- Make the decision unilaterally (this is strategic, not tactical)

## 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+)**
  → Stop. Count your attempts. If ≥3, question architecture (see "Distinguishing Architectural vs Implementation Problems")
- **Each fix reveals new problem in different place**
  → Stop. This is architectural indicator. See criteria in "Distinguishing Architectural vs Implementation Problems" to confirm.

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

**If 3+ fixes failed:** Question the architecture (see "Distinguishing Architectural vs Implementation Problems")

## your human partner's Signals You're Doing It Wrong

**Watch for these redirections:**
- "Is that not happening?" - You assumed without verifying
- "Will it show us...?" - You should have added evidence gathering
- "Stop guessing" - You're proposing fixes without understanding
- "Ultrathink this" - Question fundamentals, not just symptoms
- "We're stuck?" (frustrated) - Your approach isn't working

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

## 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. |
| "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 ≠ understanding root cause. |
| "One more fix attempt" (after 2+ failures) | 3+ failures = architectural problem. Question pattern, don't 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 |

## When Process Reveals "No Root Cause"

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

1. You've 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.

## Integration with Other Skills

**This skill requires using:**
- **tracing-root-causes** - REQUIRED when error is deep in call stack (see Phase 1, Step 5)
- **practicing-tdd** - REQUIRED for creating failing test case (see Phase 4, Step 1)

**Complementary skills:**
- **defense-in-depth** - Add validation at multiple layers after finding root cause
- **condition-based-waiting** - Replace arbitrary timeouts identified in Phase 2
- **verifying-before-completion** - Verify fix worked before claiming success

## 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


## Workflow Checklist

Copy this checklist to track your progress:

See `assets/workflow-checklist.md` for the complete checklist.