systematic-debugging

name	systematic-debugging
description	Use when encountering any bug, test failure, failing check, or unexpected behavior, BEFORE proposing fixes. A four-phase root-cause discipline (investigate → pattern → hypothesis → implement) that replaces guess-and-retry. Bundles root-cause-tracing, defense-in-depth, condition-based-waiting, and find-polluter.sh.

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, failing checks, bugs, 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)
• A check is red and re-running the same change "might" fix it (it won't)

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 errors/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? Exact steps? 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 the system has multiple components (CI → build → sign, API → service → DB), add diagnostic instrumentation at EACH component boundary (log what data enters/exits, verify env/config propagation, check state at each layer). Run once to gather evidence showing WHERE it breaks, THEN investigate that component.
5. Trace data flow — when the error is deep in the call stack, trace backward: where does the bad value originate? What called this with the bad value? Keep tracing up to the source. Fix at the source, not the symptom. See root-cause-tracing.md.

Phase 2: Pattern Analysis

1. Find working examples — locate similar working code in the same codebase.
2. Compare against references — if implementing a pattern, read the reference COMPLETELY, every line, before applying.
3. Identify differences — list every difference between working and broken, however small. Don't assume "that can't matter."
4. Understand dependencies — what other components, settings, config, environment does this need? What assumptions does it make?

Phase 3: Hypothesis and Testing

1. Form a single hypothesis — state clearly: "I think X is the root cause because Y." Be specific, not vague.
2. Test minimally — make the SMALLEST possible change to test the hypothesis. One variable at a time. Don't fix multiple things at once.
3. Verify before continuing — worked? → Phase 4. Didn't? → form a NEW hypothesis; DON'T pile more fixes on top.
4. When you don't know — say "I don't understand X." Don't pretend. Research more.

Phase 4: Implementation

1. Create a failing test case — simplest possible reproduction, automated if possible. MUST have it before fixing. Use the tdd skill for writing the proper failing test.
2. Implement a single fix — address the root cause, ONE change at a time, no "while I'm here" improvements, no bundled refactoring.
3. Verify the fix — test passes now? No other tests broken? Issue actually resolved?
4. If the fix doesn't work — STOP. Count fixes tried. If < 3: return to Phase 1 with the new information. If ≥ 3: STOP and question the architecture (next step).
5. If 3+ fixes failed: question the architecture. Pattern indicating an architectural problem: each fix reveals new shared state/coupling elsewhere; fixes need "massive refactoring"; each fix creates new symptoms. STOP and question fundamentals — is the pattern sound, or are we sticking with it through inertia? Surface the architectural question to the caller before attempting more fixes (in an autonomous pipeline, return a BLOCKED status with the concern — do NOT loop). This is NOT a failed hypothesis; it's 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"
• Proposing solutions before tracing data flow
• "One more fix attempt" (when already tried 2+)
• Each fix reveals a new problem in a different place

ALL of these mean: STOP. Return to Phase 1. If 3+ fixes failed: question the architecture.

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 the test after confirming the 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.
"Re-run the build, it'll probably pass"	A failing check is a signal, not noise. Re-running the same change is not a fix.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question the pattern, don't fix again.

Quick Reference

Phase	Key Activities	Success Criteria
1. Root Cause	Read errors, reproduce, check changes, gather evidence, trace data flow	Understand WHAT and WHY
2. Pattern	Find working examples, compare	Identify differences
3. Hypothesis	Form one theory, test minimally	Confirmed or new hypothesis
4. Implementation	Create test, single fix, verify	Bug resolved, tests pass, nothing else broken

When the process reveals "no root cause"

If investigation reveals the issue is truly environmental, timing-dependent, or external: document what you investigated, implement appropriate handling (retry, timeout with a clear error, condition-based wait), and add logging for future investigation. But: 95% of "no root cause" cases are incomplete investigation.

Supporting Techniques (in this directory)

• root-cause-tracing.md — trace a bug backward through the call stack to its original trigger; includes find-polluter.sh for bisecting which test pollutes shared state (useful for order-dependent failures across independently-built changes).
• defense-in-depth.md — after finding the root cause, add validation at every layer the bad data passes through to make the bug structurally impossible.
• condition-based-waiting.md — replace arbitrary timeouts with condition polling to kill flaky timing-dependent tests (acute when tests run under parallel load).

Related: the tdd skill (Phase 4 failing test); re-run the configured checks to verify the fix before claiming success.

Real-World Impact

• Systematic approach: 15–30 minutes to fix. Random fixes: 2–3 hours of thrashing.
• First-time fix rate: ~95% vs ~40%. New bugs introduced: near zero vs common.

---

Vendored into yaah from obra/superpowers (MIT), adapted for yaah (the tdd skill name; autonomous-pipeline escalation instead of a mid-run human prompt). Kept self-contained so the forge pipeline works the moment you clone it.