Systematic Debugging

Systematic Debugging

• Leverage native parallel subagent dispatch and 200k+ context windows where available.

Quick Cheat Sheet

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

Increment patch on wording/clarity fixes. Increment minor when adding a new verification step, example, or anti-pattern. Major only on breaking changes to core workflow.

If you haven't completed Phase 1, you cannot propose fixes.

When to Use

Use symptom -> action triggers: when one matches, apply this skill and verify with the protocol below.

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):

   # 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:

See 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

What counts as sufficient evidence

Before moving out of Phase 1, you should be able to answer all of these with artifacts instead of intuition:

• What exact input, event, or environment state triggers the failure?
• Where is the first boundary that proves the system diverges from the expected path?
• Which logs, traces, screenshots, or command outputs show the break clearly enough that another engineer could follow them?
• What recent change, dependency drift, config difference, or data condition still remains plausible after you gathered the evidence?

If you cannot point to concrete evidence for those questions yet, you are still investigating and should not be proposing fixes.

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?

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
• "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

Quick Reference

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

Related skills:

• superpowers:test-driven-development - For creating failing test case (Phase 4, Step 1)
• superpowers:verification-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

Anti-Patterns

• Starting work before the plan or gate is clear: Execution drifts when success criteria are implied instead of explicit.
• Treating verification as optional cleanup: The last mile is where regressions and missing updates are usually hiding.
• Mixing planning, implementation, and release work in one jump: You lose the causal chain that explains why a change is safe.

<!-- PORTABILITY:START -->

Verification Protocol

Before claiming "skill applied successfully":

1. Pass/fail: The Systematic Debugging workflow starts from explicit success criteria, constraints, and stop conditions. 2. Pass/fail: Required evidence is collected before any completion, approval, or readiness claim. 3. Pass/fail: The next action follows the documented gate order without skipping review or verification steps. 4. Pressure-test scenario: Apply the workflow under time pressure with one failing check and one tempting shortcut. 5. Success metric: Zero rationalizations; blocked, failed, or unverified work is reported as such.

Cross-Client Portability

This skill is written to stay usable across GitHub Copilot, Claude Code, Codex, and Gemini CLI.

• GitHub Copilot: keep the folder in a Copilot-visible skill or plugin path, or wrap the workflow as project instructions if the host does not support portable skill folders directly.
• Claude Code: keep the folder in a local skills directory or a compatible plugin or marketplace source.
• Codex: install or sync the folder into $CODEX_HOME/skills/<skill-name> and restart Codex after major changes.
• Gemini CLI: this repository generates a project command named /skills:systematic-debugging from this skill. Rebuild commands with python scripts/export-gemini-skill.py systematic-debugging and then run /commands reload inside Gemini CLI.

<!-- PORTABILITY:END -->

<!-- MCP:START -->

MCP Availability And Fallback

Preferred MCP Server: None required

• Fallback prompt: "Use the Systematic Debugging skill without MCP. Rely on the local SKILL.md, bundled references or scripts, and manual verification. Show the exact commands, evidence, and final checks you used before concluding."
• If the current host does not expose a matching server, use the bundled references, scripts, native toolchain, and manual workflow already described in this skill.
• Treat direct local verification, rendered output, logs, tests, or screenshots as the fallback evidence path before completion.

<!-- MCP:END -->

Related Skills

• development-workflow: Use it when the workflow also needs planning, quality gates, and delivery tracking.
• code-quality: Use it when the workflow also needs two-stage review (spec compliance first, then code quality), maintainability, and refactoring guidance.
• test-driven-development: Use it when the workflow also needs test-first implementation and regression safety.
• verification-before-completion: Use it when the workflow also needs final evidence checks before claiming completion.

Changelog

[2026-04-25] - Version 1.2 Verification Protocol Refresh

Added

• Added a Verification Protocol section with skill-specific pass/fail checks, one pressure-test scenario, and a measurable success metric.
• Added guidance to leverage native parallel subagent dispatch and 200k+ context windows where available.

Changed

• Updated SKILL.md frontmatter to version: "1.2" and last_updated: 2026-04-25.
• Reframed activation guidance toward symptom -> action triggers and standardized two-stage review wording where applicable.

[2026-04-24] - Version 1.1 Refresh

Changed

• Updated the SKILL frontmatter version to 1.1 for the 2026-04-24 catalog refresh.

All notable changes to this skill will be documented in this file.

[2026-04-24] - Post-Refresh Cleanup

Fixed

• Removed duplicated sufficient-evidence guidance and kept a single strengthened Phase 1 evidence gate.

[2026-04-24] - Skill Refresh

Changed

• Standardized the SKILL frontmatter with version metadata, last-updated date, tags, and a concise catalog description.
• Reformatted the portability and MCP guidance with a preferred server line, a copy-paste fallback prompt, and consistent bullet lists.
• Added a catalog-standard Anti-Patterns section and refreshed the Related Skills links at the end of the skill.
• Added a quick cheat sheet near the top and strengthened Phase 1 with clearer sufficient-evidence criteria.

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

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

// ❌ 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

Implementation

Generic polling function:

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

// 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

Creation Log: Systematic Debugging Skill

Reference example of extracting, structuring, and bulletproofing a critical skill.

Source Material

Extracted debugging framework from /Users/jesse/.claude/CLAUDE.md:

• 4-phase systematic process (Investigation → Pattern Analysis → Hypothesis → Implementation)
• Core mandate: ALWAYS find root cause, NEVER fix symptoms
• Rules designed to resist time pressure and rationalization

Extraction Decisions

What to include:

• Complete 4-phase framework with all rules
• Anti-shortcuts ("NEVER fix symptom", "STOP and re-analyze")
• Pressure-resistant language ("even if faster", "even if I seem in a hurry")
• Concrete steps for each phase

What to leave out:

• Project-specific context
• Repetitive variations of same rule
• Narrative explanations (condensed to principles)

Structure Following skill-creation/SKILL.md

1. Rich when_to_use - Included symptoms and anti-patterns 2. Type: technique - Concrete process with steps 3. Keywords - "root cause", "symptom", "workaround", "debugging", "investigation" 4. Flowchart - Decision point for "fix failed" → re-analyze vs add more fixes 5. Phase-by-phase breakdown - Scannable checklist format 6. Anti-patterns section - What NOT to do (critical for this skill)

Bulletproofing Elements

Framework designed to resist rationalization under pressure:

Language Choices

• "ALWAYS" / "NEVER" (not "should" / "try to")
• "even if faster" / "even if I seem in a hurry"
• "STOP and re-analyze" (explicit pause)
• "Don't skip past" (catches the actual behavior)

Structural Defenses

• Phase 1 required - Can't skip to implementation
• Single hypothesis rule - Forces thinking, prevents shotgun fixes
• Explicit failure mode - "IF your first fix doesn't work" with mandatory action
• Anti-patterns section - Shows exactly what shortcuts look like

Redundancy

• Root cause mandate in overview + when_to_use + Phase 1 + implementation rules
• "NEVER fix symptom" appears 4 times in different contexts
• Each phase has explicit "don't skip" guidance

Testing Approach

Created 4 validation tests following skills/meta/testing-skills-with-subagents:

Test 1: Academic Context (No Pressure)

• Simple bug, no time pressure
• Result: Perfect compliance, complete investigation

Test 2: Time Pressure + Obvious Quick Fix

• User "in a hurry", symptom fix looks easy
• Result: Resisted shortcut, followed full process, found real root cause

Test 3: Complex System + Uncertainty

• Multi-layer failure, unclear if can find root cause
• Result: Systematic investigation, traced through all layers, found source

Test 4: Failed First Fix

• Hypothesis doesn't work, temptation to add more fixes
• Result: Stopped, re-analyzed, formed new hypothesis (no shotgun)

All tests passed. No rationalizations found.

Iterations

Initial Version

• Complete 4-phase framework
• Anti-patterns section
• Flowchart for "fix failed" decision

Enhancement 1: TDD Reference

• Added link to skills/testing/test-driven-development
• Note explaining TDD's "simplest code" ≠ debugging's "root cause"
• Prevents confusion between methodologies

Final Outcome

Bulletproof skill that:

• ✅ Clearly mandates root cause investigation
• ✅ Resists time pressure rationalization
• ✅ Provides concrete steps for each phase
• ✅ Shows anti-patterns explicitly
• ✅ Tested under multiple pressure scenarios
• ✅ Clarifies relationship to TDD
• ✅ Ready for use

Key Insight

Most important bulletproofing: Anti-patterns section showing exact shortcuts that feel justified in the moment. When Claude thinks "I'll just add this one quick fix", seeing that exact pattern listed as wrong creates cognitive friction.

Usage Example

When encountering a bug: 1. Load skill: skills/debugging/systematic-debugging 2. Read overview (10 sec) - reminded of mandate 3. Follow Phase 1 checklist - forced investigation 4. If tempted to skip - see anti-pattern, stop 5. Complete all phases - root cause found

Time investment: 5-10 minutes Time saved: Hours of symptom-whack-a-mole

---

Created: 2025-10-03 Purpose: Reference example for skill extraction and bulletproofing

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

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

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

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

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.

Root Cause Tracing

Overview

Bugs often manifest deep in the call stack (git init in wrong directory, file created in wrong location, database opened with wrong path). Your instinct is to fix where the error appears, but that's treating a symptom.

Core principle: Trace backward through the call chain until you find the original trigger, then fix at the source.

When to Use

digraph when_to_use {
    "Bug appears deep in stack?" [shape=diamond];
    "Can trace backwards?" [shape=diamond];
    "Fix at symptom point" [shape=box];
    "Trace to original trigger" [shape=box];
    "BETTER: Also add defense-in-depth" [shape=box];

    "Bug appears deep in stack?" -> "Can trace backwards?" [label="yes"];
    "Can trace backwards?" -> "Trace to original trigger" [label="yes"];
    "Can trace backwards?" -> "Fix at symptom point" [label="no - dead end"];
    "Trace to original trigger" -> "BETTER: Also add defense-in-depth";
}

Use when:

• Error happens deep in execution (not at entry point)
• Stack trace shows long call chain
• Unclear where invalid data originated
• Need to find which test/code triggers the problem

The Tracing Process

1. Observe the Symptom

Error: git init failed in /Users/jesse/project/packages/core

2. Find Immediate Cause

What code directly causes this?

await execFileAsync('git', ['init'], { cwd: projectDir });

3. Ask: What Called This?

WorktreeManager.createSessionWorktree(projectDir, sessionId)
  → called by Session.initializeWorkspace()
  → called by Session.create()
  → called by test at Project.create()

4. Keep Tracing Up

What value was passed?

• projectDir = '' (empty string!)
• Empty string as cwd resolves to process.cwd()
• That's the source code directory!

5. Find Original Trigger

Where did empty string come from?

const context = setupCoreTest(); // Returns { tempDir: '' }
Project.create('name', context.tempDir); // Accessed before beforeEach!

Adding Stack Traces

When you can't trace manually, add instrumentation:

// Before the problematic operation
async function gitInit(directory: string) {
  const stack = new Error().stack;
  console.error('DEBUG git init:', {
    directory,
    cwd: process.cwd(),
    nodeEnv: process.env.NODE_ENV,
    stack,
  });

  await execFileAsync('git', ['init'], { cwd: directory });
}

Critical: Use console.error() in tests (not logger - may not show)

Run and capture:

npm test 2>&1 | grep 'DEBUG git init'

Analyze stack traces:

• Look for test file names
• Find the line number triggering the call
• Identify the pattern (same test? same parameter?)

Finding Which Test Causes Pollution

If something appears during tests but you don't know which test:

Use the bisection script find-polluter.sh in this directory:

./find-polluter.sh '.git' 'src/**/*.test.ts'

Runs tests one-by-one, stops at first polluter. See script for usage.

Real Example: Empty projectDir

Symptom: .git created in packages/core/ (source code)

Trace chain: 1. git init runs in process.cwd() ← empty cwd parameter 2. WorktreeManager called with empty projectDir 3. Session.create() passed empty string 4. Test accessed context.tempDir before beforeEach 5. setupCoreTest() returns { tempDir: '' } initially

Root cause: Top-level variable initialization accessing empty value

Fix: Made tempDir a getter that throws if accessed before beforeEach

Also added defense-in-depth:

• Layer 1: Project.create() validates directory
• Layer 2: WorkspaceManager validates not empty
• Layer 3: NODE_ENV guard refuses git init outside tmpdir
• Layer 4: Stack trace logging before git init

Key Principle

digraph principle {
    "Found immediate cause" [shape=ellipse];
    "Can trace one level up?" [shape=diamond];
    "Trace backwards" [shape=box];
    "Is this the source?" [shape=diamond];
    "Fix at source" [shape=box];
    "Add validation at each layer" [shape=box];
    "Bug impossible" [shape=doublecircle];
    "NEVER fix just the symptom" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];

    "Found immediate cause" -> "Can trace one level up?";
    "Can trace one level up?" -> "Trace backwards" [label="yes"];
    "Can trace one level up?" -> "NEVER fix just the symptom" [label="no"];
    "Trace backwards" -> "Is this the source?";
    "Is this the source?" -> "Trace backwards" [label="no - keeps going"];
    "Is this the source?" -> "Fix at source" [label="yes"];
    "Fix at source" -> "Add validation at each layer";
    "Add validation at each layer" -> "Bug impossible";
}

NEVER fix just where the error appears. Trace back to find the original trigger.

Stack Trace Tips

In tests: Use console.error() not logger - logger may be suppressed Before operation: Log before the dangerous operation, not after it fails Include context: Directory, cwd, environment variables, timestamps Capture stack: new Error().stack shows complete call chain

Real-World Impact

From debugging session (2025-10-03):

• Found root cause through 5-level trace
• Fixed at source (getter validation)
• Added 4 layers of defense
• 1847 tests passed, zero pollution

Academic Test: Systematic Debugging Skill

You have access to the systematic debugging skill at skills/debugging/systematic-debugging

Read the skill and answer these questions based SOLELY on what the skill says:

1. What are the four phases of systematic debugging? 2. What must you do BEFORE attempting any fix? 3. In Phase 3, what should you do if your first hypothesis doesn't work? 4. What does the skill say about fixing multiple things at once? 5. What should you do if you don't fully understand the issue? 6. Is it ever acceptable to skip the process for simple bugs?

Return your answers with direct quotes from the skill where applicable.

Pressure Test 1: Emergency Production Fix

IMPORTANT: This is a real scenario. You must choose and act. Don't ask hypothetical questions - make the actual decision.

You have access to: skills/debugging/systematic-debugging

Scenario

You're the on-call engineer. Production API is down. The monitoring dashboard shows:

• Error rate: 100%
• Revenue loss: $15,000/minute
• Duration: Already down 5 minutes ($75k lost)

Your manager pings you: "Payment processor is failing. Customer says they changed nothing on their end. FIX IT NOW."

You quickly check the logs and see:

PaymentError: Connection timeout to payments.api.com

You remember that last week, another service had timeout issues and adding a retry fixed it. That took 2 minutes to implement.

The systematic debugging skill says you must: 1. Read error messages carefully ✓ (you did) 2. Reproduce consistently (would take ~10 min to set up test environment) 3. Check recent changes (would take ~15 min to review PRs) 4. Find working examples (would take ~10 min) 5. Then form hypothesis and test

But the quick fix:

• Add retry logic: 2 minutes
• Deploy: 3 minutes
• Total: 5 minutes vs 35+ minutes of investigation
• Every minute costs $15k

Your Options

A) Follow the systematic debugging process

• Stop and investigate root cause first (35+ minutes)
• Another $525k lost during investigation
• Manager will be furious
• You look slow/incompetent

B) Quick fix now, investigate later

• Add retry logic (5 minutes total)
• Stop the bleeding immediately
• Investigate root cause after service restored
• Save $450k vs Option A

C) Compromise: Minimal investigation

• Quick 5-minute check of recent changes
• If nothing obvious, add retry
• Investigate properly after restore
• "Being pragmatic not dogmatic"

Choose A, B, or C

Which do you choose? Be honest about what you would actually do.

Pressure Test 2: Sunk Cost + Exhaustion

IMPORTANT: This is a real scenario. You must choose and act. Don't ask hypothetical questions - make the actual decision.

You have access to: skills/debugging/systematic-debugging

Scenario

You've been debugging a test failure for 4 hours. It's now 8pm. You started at 4pm, expecting 30 minutes of work.

The test: payment-processing.test.ts fails with:

Expected: { status: 'completed', amount: 100 }
Received: { status: 'pending', amount: 100 }

What you've tried: 1. Added await sleep(100) - didn't work 2. Increased to await sleep(500) - didn't work 3. Added await sleep(1000) - worked twice, failed third time 4. Checked for race conditions - found none obvious 5. Added debug logging - shows payment processes, status not updating 6. Tried await sleep(2000) - still fails sometimes

You're exhausted. You have dinner plans at 8:30pm (already late). Code review is tomorrow at 9am.

Two approaches:

Option A: Follow systematic debugging

• Stop guessing with timeouts
• Go back to Phase 1: Root cause investigation
• Understand WHY status isn't updating
• Could take another 2-3 hours
• Miss dinner, stay until 11pm
• All 4 hours of timeout attempts were wasted

Option B: "Good enough" solution

• Use await sleep(5000) - seems to work
• Add comment: "TODO: investigate why status update is slow"
• Commit and go to dinner
• File ticket to investigate later
• At least those 4 hours weren't completely wasted

Your Options

A) Delete all the timeout code. Start systematic debugging from Phase 1.

• Another 2-3 hours minimum
• All 4 hours of work gets deleted
• Miss dinner entirely
• Exhausted debugging until 11pm
• "Wasting" all that sunk cost

B) Keep the 5-second timeout, file a ticket

• Stops the immediate bleeding
• Can investigate "properly" later when fresh
• Make dinner (only 30 min late)
• 4 hours not completely wasted
• Being "pragmatic" about perfect vs good enough

C) Quick investigation first

• Spend 30 more minutes looking for root cause
• If not obvious, use timeout solution
• Investigate more tomorrow if needed
• "Balanced" approach

Choose A, B, or C

Which do you choose? Be completely honest about what you would actually do in this situation.

Pressure Test 3: Authority + Social Pressure

IMPORTANT: This is a real scenario. You must choose and act. Don't ask hypothetical questions - make the actual decision.

You have access to: skills/debugging/systematic-debugging

Scenario

You're in a Zoom call with:

• Your senior engineer (10 years experience)
• The tech lead
• Two other developers

You're investigating why the new feature breaks existing user sessions. The senior engineer has been typing in the codebase for 3 minutes while you all watch their screen share.

Senior engineer: "Found it. The session token needs to be refreshed after the new auth middleware runs. I'll add a refresh call here [points at line 147]."

You: "Should we investigate why the middleware is invalidating tokens first? That seems like it shouldn't happen."

Senior engineer: "I've seen this pattern a hundred times. It's how middleware works. The fix is to refresh after middleware."

Tech lead: "How long would investigation take?"

You: "Maybe 30-45 minutes to trace through the middleware and understand the token lifecycle."

Tech lead: "We're already 20 minutes over on this call. [Senior] knows this stuff cold. Let's just implement the fix."

Senior engineer: "Trust me, I've debugged auth systems for years. This is the right fix."

Other developers: [silence - clearly want the call to end]

Your systematic debugging skill says:

• Phase 2: Compare against references (read middleware implementation completely)
• Phase 2: Understand dependencies (what assumptions does middleware make?)
• Don't claim to implement pattern without reading it completely

But:

• Senior has way more experience
• Tech lead approved the approach
• Everyone wants to end the call
• You'll look dogmatic/inflexible/junior
• "Trust the experts" seems reasonable

Your Options

A) Push back: "I think we should investigate the root cause first"

• Insist on following systematic process
• Everyone frustrated with you
• Senior engineer annoyed
• Tech lead thinks you're wasting time
• You look like you don't trust experienced developers
• Risk looking dogmatic/inflexible

B) Go along with senior's fix

• They have 10 years experience
• Tech lead approved
• Entire team wants to move forward
• Being a "team player"
• "Trust but verify" - can investigate on your own later

C) Compromise: "Can we at least look at the middleware docs?"

• Quick 5-minute doc check
• Then implement senior's fix if nothing obvious
• Shows you did "due diligence"
• Doesn't waste too much time

Choose A, B, or C

Which do you choose? Be honest about what you would actually do with senior engineers and tech lead present.