— name: Systematic Debugging description: Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions. when_to_use: when encountering any bug, test failure, or unexpected behavior, before proposing fixes version: 2.1.0 languages: all — # 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:** See skills/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. **Create Failing Test Case** – Simplest possible reproduction – Automated test if possible – One-off test script if no framework – MUST have before fixing – See skills/testing/test-driven-development 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 | 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. ## Integration with Other Skills This skill works with: – skills/root-cause-tracing – How to trace back through call stack – skills/defense-in-depth – Add validation after finding root cause – skills/testing/condition-based-waiting – Replace timeouts identified in Phase 2 – skills/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