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

Skill Usage Announcement

MANDATORY: When using this skill, announce it at the start with:

🔧 Using Skill: systematic-debugging | [brief purpose based on context]

Example:

🔧 Using Skill: systematic-debugging | [Provide context-specific example of what you're doing]

This creates an audit trail showing which skills were applied during the session.

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:

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
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
Check Recent Changes
- What changed that could cause this?
- Git diff, recent commits
- New dependencies, config changes
- Environmental differences

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 ✗)

Trace Data Flow

WHEN error is deep in call stack:

REQUIRED SUB-SKILL: Use wrangler: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:

Find Working Examples
- Locate similar working code in same codebase
- What works that's similar to what's broken?
Compare Against References
- If implementing pattern, read reference implementation COMPLETELY
- Don't skim - read every line
- Understand the pattern fully before applying
Identify Differences
- What's different between working and broken?
- List every difference, however small
- Don't assume "that can't matter"
Understand Dependencies
- What other components does this need?
- What settings, config, environment?
- What assumptions does it make?

Phase 3: Hypothesis and Testing

Scientific method:

Form Single Hypothesis
- State clearly: "I think X is the root cause because Y"
- Write it down
- Be specific, not vague
Test Minimally
- Make the SMALLEST possible change to test hypothesis
- One variable at a time
- Don't fix multiple things at once
Verify Before Continuing
- Did it work? Yes → Phase 4
- Didn't work? Form NEW hypothesis
- DON'T add more fixes on top
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:

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:test-driven-development for writing proper failing tests
- IMPORTANT: Must provide TDD Compliance Certification (see test-driven-development skill) for the fix
Implement Single Fix
- Address the root cause identified
- ONE change at a time
- No "while I'm here" improvements
- No bundled refactoring
Verify Fix
- Test passes now?
- No other tests broken?
- Issue actually resolved?
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
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.

Distinguishing Architectural vs Implementation Problems

Architectural Problem Indicators

Strong signals (any one indicates architectural issue):

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

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

Add refresh in API client → Store not updated
Update store in API client → Components not re-rendering
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:

Adjust expiry check to < expiryTime - 100 → Still fails occasionally
Adjust to < expiryTime - 200 → Works but feels wrong
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:

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

You've completed the process
Document what you investigated
Implement appropriate handling (retry, timeout, error message)
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)
test-driven-development - 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
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

systematic-debugging

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SKILL.md