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---
name: systematic-debugging
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 superpowers root-cause-tracing 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. **Decide on Testing Strategy**
**Auto-decide based on complexity:**
- **Write test for**: Complex algorithms, business logic, data transformations where bugs are likely
- **Skip test for**: UI components, React hooks, simple CRUD, straightforward mappings, anything you're 100% certain is correct
- **Test type**: Only deterministic unit tests - no integration tests, no complex mocking, no async complexity
**If writing test:**
- Simplest possible reproduction
- Automated test that fails before fix
- Verify logic, not implementation details
**If skipping test:**
- Verify fix with typecheck/lint
- Manual verification for UI changes
- Code review confidence that fix is correct
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**
**If test was written:**
- Test passes now?
- No other tests broken?
**If no test:**
- Typecheck passes?
- Lint clean?
- Manual verification confirms fix?
**Always check:**
- Issue actually resolved?
- No regressions in related functionality?
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 your human partner before attempting more fixes**
This is NOT a failed hypothesis - this is a wrong architecture.
## 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"
- "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**
- **Writing tests for UI components when you're certain the fix is correct**
**ALL of these mean: STOP. Return to Phase 1.**
**If 3+ fixes failed:** Question the architecture (see Phase 4.5)
## 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. |
| "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. |
| "UI fix doesn't need tests" | Correct! UI components verified via typecheck/manual testing, not unit tests. |
## 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:**
- **root-cause-tracing** - REQUIRED when error is deep in call stack (see Phase 1, Step 5)
**Testing skills (when needed):**
- **test-driven-development** (if available) - Use when fixing complex business logic that needs test coverage
- Skip for UI components, simple CRUD, or anything verifiable via typecheck/manual testing
## 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