Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Systematic Debugging Overview Random fixes waste time and create new bugs. Quick patches mask underlying issues. Core principle: Investigate before guessing and separate a verified repair from a temporary mitigation. During an incident, an authorized rollback or containment action may restore service while root cause work continues. Violating the letter of this process is violating the spirit of debugging. The Iron Law Start with the observable failure and the smallest useful evidence check. Label any emergency mitigation explicitly and preserve evidence for the later root cause investigation. 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: Example (multi layer system): Interpretation: Compare presence checks at the actual workflow/build boundaries. Artifact verification does not itself prove that the correct signing identity propagated. 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 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 for a focused behavioral regression when appropriate 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 Repeated failures are evidence to reassess assumptions and coupling; they do not prove that the architecture is wrong. 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 State the remaining uncertainty and the evidence that would distinguish an environmental failure from an implementation defect. 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: test driven development Focused behavioral regression Use the current repository’s verification commands before claiming success Worked example and expected result Input: a build succeeds locally but fails in CI because a required identity is absent in the build subprocess. Record only whether it is set at each boundary, inspect how environment variables are forwarded, and change that propagation once. Re run the failing build and validate the artifact separately. Expected: the subprocess receives the required configuration and the original failure disappears; logs contain no credential value. Inputs and prerequisites A reproducible command or observed failure, exact revision/runtime, recent changes and access to an authorized test environment. The bundled historical case notes illustrate the technique; their reported counts are not fresh measurements or guarantees for this project. Limitations Temporary mitigation and root cause repair are different outcomes; record both when an incident requires immediate containment. Logging can expose secrets or personal paths. Use allowlisted summaries and inspect captured artifacts before sharing. The polluter helper runs the project’s test command and can execute project code; use an isolated fixture/checkout and verify the runner accepts a file argument. The waiting examples require domain adapters and cannot make every race impossible. Reproduce the actual timeout/error path.

systematic-debugging