systematic-debugging

name	systematic-debugging
description	Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
license	MIT
metadata	{"version":"0.1.0"}

Systematic Debugging

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.

This skill bundles its own steer runtime at scripts/steer.py; the commands below invoke it with python3 and need nothing installed. Paths are relative to this skill's directory: when your working directory is elsewhere (it usually is), use the skill's full path (python3 <path-to-this-skill>/scripts/steer.py ...).

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

In this skill the law is machine-enforced: the flow below keeps the fix step locked until the investigation artifacts actually exist. You cannot propose fixes in Phase 1 because the flow will not let you get there.

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)

Process

The four phases are an enforced flow; steps verify themselves against artifacts in out/debug/, and you cannot skip ahead.

1. Announce: "Working through the systematic-debugging flow."
2. Run python3 scripts/steer.py flow status (in the workspace) to see the current phase.
3. Do what the directive says, guided by the phase notes below.
4. Run python3 scripts/steer.py flow next and repeat until all steps report complete.

Do NOT claim the bug is fixed while python3 scripts/steer.py flow status shows incomplete steps. The flow is defined in flow.toml.

Phase 1: Root Cause Investigation (step: investigate)

BEFORE attempting ANY fix:

1. Read error messages carefully. 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? If not reproducible, gather more data; don't guess.
3. Check recent changes. Git diff, recent commits, new dependencies, config changes (git diff, git log).
4. Gather evidence in multi-component systems. For each component boundary, log what enters and what exits, and verify config propagation. Run once to see WHERE it breaks, then investigate that component. When the error is deep in a call stack, first read references/root-cause-tracing.md.

Everything you find goes in out/debug/evidence.md; the flow checks it exists before Phase 2 unlocks.

Phase 2: Pattern Analysis (step: analyze)

1. Find working examples. Locate similar working code in the same codebase.
2. Compare against references. If implementing a pattern, read the reference implementation COMPLETELY. Don't skim; read every line.
3. Identify differences. List every difference between working and broken, however small. Don't assume "that can't matter." The list goes in out/debug/comparison.md.
4. Understand dependencies. What components, settings, and assumptions does the working version rely on?

Phase 3: Hypothesis and Testing (step: hypothesize)

1. Form a single hypothesis. Write out/debug/hypothesis.md: "I think X is the root cause because Y." Be specific.
2. Test minimally. The SMALLEST possible change that tests the hypothesis. One variable at a time.
3. Verify before continuing. Confirmed? Move on. Wrong? Form a NEW hypothesis; do NOT stack more fixes on top.
4. When you don't know, say so. "I don't understand X" beats pretending. Research or ask for help.

Phase 4: Implementation (steps: failing-test, fix)

1. Create a failing test case first. Simplest possible reproduction, automated if a test framework exists. Record the command and its failing output in out/debug/failing-test.md; the fix step stays locked until it exists. The test-driven-development skill, if installed, covers writing proper failing tests.
2. Implement a single fix at the root cause. ONE change at a time. No "while I'm here" improvements, no bundled refactoring.
3. Verify: the failing test passes, no other tests broke, the issue is actually resolved. Then, and only then: python3 scripts/steer.py flow done fix.
4. If the fix didn't work: STOP. Count your attempts. Under three: return to Phase 1 and re-analyze with the new information (delete the stale artifacts in out/debug/ so the flow re-gates). Three or more: stop fixing.
5. If 3+ fixes failed, question the architecture. Each fix revealing a new problem elsewhere, fixes needing "massive refactoring", new symptoms after every change: that is not a failed hypothesis, that is a wrong architecture. Discuss with your human partner 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. python3 scripts/steer.py flow status will tell you exactly which phase you are really in; capture the rationalization you caught yourself in (see Learning).

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
• "Ultra-think 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 is not understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, don't fix again.

When Process Reveals "No Root Cause"

If systematic investigation shows the issue is truly environmental, timing-dependent, or external: you've completed the process. Document what you investigated, implement appropriate handling (retry, timeout, error message), and add monitoring for future investigation.

But: 95% of "no root cause" cases are incomplete investigation.

Learning

This skill improves with use. As you work:

• A disproven hypothesis, a rationalization you caught yourself in, or a technique that cracked the case is a lesson; capture it the moment it happens: python3 scripts/steer.py learn note "<one imperative rule>" --kind correction
• At the start of a debugging session, run python3 scripts/steer.py learn show; those lessons came from real previous bugs. Confirm the ones that helped (python3 scripts/steer.py learn confirm <id>), dispute the ones that misled.
• Before finishing, record the outcome: python3 scripts/steer.py learn run ok (or failed with --note).

If a learnings.md exists in this skill, read it too; those are promoted lessons that shipped with the skill.

References

Supporting techniques, loaded only when that branch is hit:

• Bug deep in a call stack: first read references/root-cause-tracing.md (backward tracing to the original trigger; includes the bisection script scripts/find-polluter.sh).
• Adding validation after the root cause is found: read references/defense-in-depth.md.
• Flaky waits and arbitrary timeouts: read references/condition-based-waiting.md (worked example in references/condition-based-waiting-example.ts).

Related skills, if installed: test-driven-development (Phase 4, step 1), verification-before-completion (verify the fix 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