---

name: systematic-debugging description: "Four-phase debugging framework for any bug, test failure, or unexpected behavior. Enforces root cause investigation before proposing fixes through pattern analysis, hypothesis testing, and validated implementation. Use when standard debugging fails, issues span multiple components, or you've already tried obvious fixes without success. Do NOT use for simple typos, obvious syntax errors, or issues where the cause is immediately clear - those are faster to fix directly." inputs:

• from: any field: error_message required: false
• from: any field: reproduction_steps required: false outputs:
• field: root_cause type: string
• field: fix_applied type: boolean
• field: github_issue type: issue_number next_skills:
• pop-test-driven-development
• pop-root-cause-tracing workflow: id: systematic-debugging name: Systematic Debugging Workflow version: 1 description: Four-phase debugging with enforced root cause investigation steps:
  • id: initial_triage description: Gather error information and context type: agent agent: bug-whisperer next: issue_type_decision
  • id: issue_type_decision description: Classify the type of issue type: user_decision question: "What type of issue are we debugging?" header: "Issue Type" options:
    • id: test_failure label: "Test failure" description: "Test is failing - check for flakiness first" next: flakiness_check
    • id: runtime_bug label: "Runtime bug" description: "Bug in production or development" next: phase1_investigate
    • id: build_failure label: "Build failure" description: "Build or compilation error" next: phase1_investigate
    • id: performance label: "Performance" description: "Slow or degraded performance" next: phase1_investigate next_map: test_failure: flakiness_check runtime_bug: phase1_investigate build_failure: phase1_investigate performance: phase1_investigate
  • id: flakiness_check description: Run test 5x to check for flakiness type: agent agent: test-writer-fixer next: flakiness_result
  • id: flakiness_result description: Determine if test is flaky type: user_decision question: "What were the flakiness check results?" header: "Flaky?" options:
    • id: consistent_fail label: "Fails 5/5" description: "Consistent failure - investigate as bug" next: phase1_investigate
    • id: consistent_pass label: "Passes 5/5" description: "Not flaky - investigate normally" next: phase1_investigate
    • id: flaky label: "Mixed results" description: "Flaky test - fix test first" next: fix_flaky_test next_map: consistent_fail: phase1_investigate consistent_pass: phase1_investigate flaky: fix_flaky_test
  • id: fix_flaky_test description: Fix the flaky test before debugging code type: skill skill: pop-test-driven-development next: phase1_investigate
  • id: phase1_investigate description: "Phase 1: Root cause investigation" type: spawn_agents agents:
    • type: bug-whisperer task: "Read error messages, check recent changes, trace data flow"
    • type: code-explorer task: "Explore related code and dependencies" wait_for: all next: phase2_patterns
  • id: phase2_patterns description: "Phase 2: Pattern analysis - find working examples" type: agent agent: code-explorer next: root_cause_decision
  • id: root_cause_decision description: Confirm root cause understanding type: user_decision question: "Do we have a clear root cause hypothesis?" header: "Root Cause" options:
    • id: yes label: "Yes" description: "Root cause identified - proceed to fix" next: phase3_hypothesis
    • id: need_more label: "Need more info" description: "Gather more evidence" next: gather_more_evidence
    • id: architectural label: "Architectural" description: "Looks like an architectural issue" next: architectural_decision next_map: yes: phase3_hypothesis need_more: gather_more_evidence architectural: architectural_decision
  • id: gather_more_evidence description: Add diagnostic instrumentation and gather evidence type: skill skill: pop-root-cause-tracing next: root_cause_decision
  • id: architectural_decision description: Decide how to handle architectural issue type: user_decision question: "How should we handle this architectural problem?" header: "Approach" options:
    • id: refactor label: "Refactor" description: "Address the architectural issue" next: plan_refactor
    • id: workaround label: "Workaround" description: "Apply workaround for now" next: phase3_hypothesis
    • id: escalate label: "Escalate" description: "Need team discussion" next: complete next_map: refactor: plan_refactor workaround: phase3_hypothesis escalate: complete
  • id: plan_refactor description: Create refactoring plan type: skill skill: pop-writing-plans next: complete
  • id: phase3_hypothesis description: "Phase 3: Form and test hypothesis" type: agent agent: bug-whisperer next: phase4_implement
  • id: phase4_implement description: "Phase 4: Create failing test and implement fix" type: skill skill: pop-test-driven-development next: verify_fix
  • id: verify_fix description: Verify the fix worked type: user_decision question: "Did the fix resolve the issue?" header: "Verified?" options:
    • id: fixed label: "Fixed" description: "Issue resolved, tests pass" next: complete
    • id: not_fixed label: "Not fixed" description: "Issue persists" next: retry_decision next_map: fixed: complete not_fixed: retry_decision
  • id: retry_decision description: Decide whether to retry or escalate type: user_decision question: "How many fix attempts so far? (3+ suggests architectural issue)" header: "Attempts" options:
    • id: under_three label: "Less than 3" description: "Re-analyze with new information" next: phase1_investigate
    • id: three_plus label: "3 or more" description: "Stop and question architecture" next: architectural_decision next_map: under_three: phase1_investigate three_plus: architectural_decision
  • id: complete description: Debugging workflow complete type: terminal

---

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:

Special Case: Test Failures - Check for Flakiness First

For ANY test failure, check if the test itself is the problem:

Test fails → Is it flaky? → Run 5x locally
  ├─ Passes 5/5: Not your code, not flaky - investigate as normal bug
  ├─ Fails 5/5: Consistent failure - investigate as normal bug
  └─ Mixed results (e.g., 3/5): FLAKY TEST - fix the test first

Flaky test investigation checklist:

Check	How	Fix
Isolated or connected?	Run single test vs full suite	If fails alone but passes in suite (or vice versa): state pollution
Timing-dependent?	Look for setTimeout, sleep, hardcoded delays	Use condition-based waiting (see pop-test-driven-development skill)
Environment-specific?	Run in CI vs local, different machines	Mock environment variables, isolate external dependencies
Order-dependent?	Run tests in different order	Add proper setup/teardown, don't share state between tests
Race condition?	Look for async operations without proper waits	Add proper async/await, use polling for state changes

If test is flaky: Fix the test BEFORE debugging the code. A flaky test proves nothing.

Then continue with normal investigation:

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
   

5. Trace Data Flow

   WHEN error is deep in call stack:

   Use root-cause-tracing skill 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
   • Use 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 user before attempting more fixes

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)

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

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

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Cross-References

• Flaky test fixes: See pop-test-driven-development skill (Condition-Based Waiting section)
• Root cause tracing: See pop-root-cause-tracing skill (backward tracing through call stack)
• Defense in depth: See pop-defense-in-depth skill (multi-layer validation to prevent bugs)