Systematic Debugging

---
name: systematic-debugging
description: Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
---

# Systematic Debugging

## Overview

**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: $([ -n "${IDENTITY:-}" ] && echo SET || echo UNSET)"

   # Layer 2: Build script
   echo "=== Env vars in build script: ==="
   printenv IDENTITY >/dev/null 2>&1 && echo "IDENTITY in environment" || 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:**

   See `root-cause-tracing.md` ([source](https://raw.githubusercontent.com/obra/superpowers/main/skills/systematic-debugging/root-cause-tracing.md)) in this directory for the complete 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
   - Use the `superpowers:test-driven-development` 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?
   - Use the `superpowers:verification-before-completion` skill before claiming success

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"
- "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)

## 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
- "Ultra-think 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.

## Supporting Techniques

These techniques are part of systematic debugging and available in this directory:

- **`root-cause-tracing.md`** - Trace bugs backward through call stack to find original trigger
- **`defense-in-depth.md`** - Add validation at multiple layers after finding root cause
- **`condition-based-waiting.md`** - Replace arbitrary timeouts with condition polling


---

## Companion Files

The following reference files are included for convenience:

### defense-in-depth.md

# Defense-in-Depth Validation

## Overview

When you fix a bug caused by invalid data, adding validation at one place feels sufficient. But that single check can be bypassed by different code paths, refactoring, or mocks.

**Core principle:** Validate at EVERY layer data passes through. Make the bug structurally impossible.

## Why Multiple Layers

Single validation: "We fixed the bug"
Multiple layers: "We made the bug impossible"

Different layers catch different cases:
- Entry validation catches most bugs
- Business logic catches edge cases
- Environment guards prevent context-specific dangers
- Debug logging helps when other layers fail

## The Four Layers

### Layer 1: Entry Point Validation
**Purpose:** Reject obviously invalid input at API boundary

```typescript
function createProject(name: string, workingDirectory: string) {
  if (!workingDirectory || workingDirectory.trim() === '') {
    throw new Error('workingDirectory cannot be empty');
  }
  if (!existsSync(workingDirectory)) {
    throw new Error(`workingDirectory does not exist: ${workingDirectory}`);
  }
  if (!statSync(workingDirectory).isDirectory()) {
    throw new Error(`workingDirectory is not a directory: ${workingDirectory}`);
  }
  // ... proceed
}
```

### Layer 2: Business Logic Validation
**Purpose:** Ensure data makes sense for this operation

```typescript
function initializeWorkspace(projectDir: string, sessionId: string) {
  if (!projectDir) {
    throw new Error('projectDir required for workspace initialization');
  }
  // ... proceed
}
```

### Layer 3: Environment Guards
**Purpose:** Prevent dangerous operations in specific contexts

```typescript
async function gitInit(directory: string) {
  // In tests, refuse git init outside temp directories
  if (process.env.NODE_ENV === 'test') {
    const normalized = normalize(resolve(directory));
    const tmpDir = normalize(resolve(tmpdir()));

    if (!normalized.startsWith(tmpDir)) {
      throw new Error(
        `Refusing git init outside temp dir during tests: ${directory}`
      );
    }
  }
  // ... proceed
}
```

### Layer 4: Debug Instrumentation
**Purpose:** Capture context for forensics

```typescript
async function gitInit(directory: string) {
  const stack = new Error().stack;
  logger.debug('About to git init', {
    directory,
    cwd: process.cwd(),
    stack,
  });
  // ... proceed
}
```

## Applying the Pattern

When you find a bug:

1. **Trace the data flow** - Where does bad value originate? Where used?
2. **Map all checkpoints** - List every point data passes through
3. **Add validation at each layer** - Entry, business, environment, debug
4. **Test each layer** - Try to bypass layer 1, verify layer 2 catches it

## Example from Session

Bug: Empty `projectDir` caused `git init` in source code

**Data flow:**
1. Test setup → empty string
2. `Project.create(name, '')`
3. `WorkspaceManager.createWorkspace('')`
4. `git init` runs in `process.cwd()`

**Four layers added:**
- Layer 1: `Project.create()` validates not empty/exists/writable
- Layer 2: `WorkspaceManager` validates projectDir not empty
- Layer 3: `WorktreeManager` refuses git init outside tmpdir in tests
- Layer 4: Stack trace logging before git init

**Result:** All 1847 tests passed, bug impossible to reproduce

## Key Insight

All four layers were necessary. During testing, each layer caught bugs the others missed:
- Different code paths bypassed entry validation
- Mocks bypassed business logic checks
- Edge cases on different platforms needed environment guards
- Debug logging identified structural misuse

**Don't stop at one validation point.** Add checks at every layer.

### condition-based-waiting.md

# Condition-Based Waiting

## Overview

Flaky tests often guess at timing with arbitrary delays. This creates race conditions where tests pass on fast machines but fail under load or in CI.

**Core principle:** Wait for the actual condition you care about, not a guess about how long it takes.

## When to Use

```dot
digraph when_to_use {
    "Test uses setTimeout/sleep?" [shape=diamond];
    "Testing timing behavior?" [shape=diamond];
    "Document WHY timeout needed" [shape=box];
    "Use condition-based waiting" [shape=box];

    "Test uses setTimeout/sleep?" -> "Testing timing behavior?" [label="yes"];
    "Testing timing behavior?" -> "Document WHY timeout needed" [label="yes"];
    "Testing timing behavior?" -> "Use condition-based waiting" [label="no"];
}
```

**Use when:**
- Tests have arbitrary delays (`setTimeout`, `sleep`, `time.sleep()`)
- Tests are flaky (pass sometimes, fail under load)
- Tests timeout when run in parallel
- Waiting for async operations to complete

**Don't use when:**
- Testing actual timing behavior (debounce, throttle intervals)
- Always document WHY if using arbitrary timeout

## Core Pattern

```typescript
// ❌ BEFORE: Guessing at timing
await new Promise(r => setTimeout(r, 50));
const result = getResult();
expect(result).toBeDefined();

// ✅ AFTER: Waiting for condition
await waitFor(() => getResult() !== undefined);
const result = getResult();
expect(result).toBeDefined();
```

## Quick Patterns

| Scenario | Pattern |
|----------|---------|
| Wait for event | `waitFor(() => events.find(e => e.type === 'DONE'))` |
| Wait for state | `waitFor(() => machine.state === 'ready')` |
| Wait for count | `waitFor(() => items.length >= 5)` |
| Wait for file | `waitFor(() => fs.existsSync(path))` |
| Complex condition | `waitFor(() => obj.ready && obj.value > 10)` |

## Implementation

Generic polling function:
```typescript
async function waitFor<T>(
  condition: () => T | undefined | null | false,
  description: string,
  timeoutMs = 5000
): Promise<T> {
  const startTime = Date.now();

  while (true) {
    const result = condition();
    if (result) return result;

    if (Date.now() - startTime > timeoutMs) {
      throw new Error(`Timeout waiting for ${description} after ${timeoutMs}ms`);
    }

    await new Promise(r => setTimeout(r, 10)); // Poll every 10ms
  }
}
```

See `condition-based-waiting-example.ts` in this directory for complete implementation with domain-specific helpers (`waitForEvent`, `waitForEventCount`, `waitForEventMatch`) from actual debugging session.

## Common Mistakes

**❌ Polling too fast:** `setTimeout(check, 1)` - wastes CPU
**✅ Fix:** Poll every 10ms

**❌ No timeout:** Loop forever if condition never met
**✅ Fix:** Always include timeout with clear error

**❌ Stale data:** Cache state before loop
**✅ Fix:** Call getter inside loop for fresh data

## When Arbitrary Timeout IS Correct

```typescript
// Tool ticks every 100ms - need 2 ticks to verify partial output
await waitForEvent(manager, 'TOOL_STARTED'); // First: wait for condition
await new Promise(r => setTimeout(r, 200));   // Then: wait for timed behavior
// 200ms = 2 ticks at 100ms intervals - documented and justified
```

**Requirements:**
1. First wait for triggering condition
2. Based on known timing (not guessing)
3. Comment explaining WHY

## Real-World Impact

From debugging session (2025-10-03):
- Fixed 15 flaky tests across 3 files
- Pass rate: 60% → 100%
- Execution time: 40% faster
- No more race conditions