oimiragieo/debugging

Mode: Cognitive/Prompt-Driven — No standalone utility script; use via agent context.

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.

Iron Laws

1. NEVER propose or implement a fix before completing Phase 1 root cause investigation — a fix without root cause is a guess that will fail or create new bugs.
2. ALWAYS reproduce the bug reliably before debugging — if you can't reproduce it consistently, you're not debugging the real issue.
3. NEVER make more than one change at a time when testing a hypothesis — multiple simultaneous changes make it impossible to determine which change fixed the problem.
4. ALWAYS stop and question the architecture after 3 failed fix attempts — if each fix reveals a new problem, the issue is architectural, not symptomatic.
5. NEVER skip creating a failing test case before implementing the fix — without a test, you cannot verify the fix worked or that it won't regress.

When to Use

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)

When to Use: debugging vs smart-debug

| Scenario | Use debugging | Use smart-debug | | ------------------------------------ | --------------- | ----------------- | | Simple, locally reproducible bug | Yes | Overkill | | Root cause area already known | Yes | Optional | | Static analysis / code review bug | Yes | No | | Runtime / production issue | Start here | Preferred | | Intermittent / hard-to-reproduce | Escalate | Yes | | Needs hypothesis ranking gate | No | Yes (blocking) | | Needs instrumentation + log analysis | No | Yes | | Observability-driven (traces, APM) | No | Yes |

Rule of thumb: Start with debugging for straightforward bugs. Escalate to smart-debug when you need hypothesis ranking, structured instrumentation, or the bug is intermittent/production-only.

See also: .claude/skills/smart-debug/SKILL.md

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):

   # 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 OK, workflow - build FAIL)

   For distributed/microservice systems — prefer OpenTelemetry traces:

   # Query traces by component (preferred over manual echo/env logging)
   pnpm trace:query --component <service-name> --event <event-name> --since <ISO-8601> --limit 200
   
   # When trace ID is already known
   pnpm trace:query --trace-id <traceId> --compact --since <ISO-8601> --limit 200
   

   Fragmented traces (each service has its own root span, trace IDs don't match across boundaries) = broken context propagation. Fix traceparent/tracestate header forwarding before investigating business logic.

   Instrumentation Gate (before hypothesis generation): If runtime behavior remains unclear after static analysis, add targeted log statements at key decision nodes before generating hypotheses. Use session-scoped log files (.claude/context/tmp/debug-{sessionId}.log) to capture runtime state. Human-in-the-loop: ask the user to reproduce the bug after instrumentation is added, before analyzing results. Only proceed to Phase 2 once runtime evidence is collected.

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 the tdd 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. Cleanup

   • Remove all instrumentation added for this debug session (log statements, temporary diagnostics)
   • Verify cleanup: grep for the session debug ID or instrumentation markers to confirm no debug artifacts remain in production code
   • Example: rg "debug-{sessionId}" --type-add 'src:*.{js,ts,cjs,mjs}' -tsrc .

5. 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 6 below)
   • DON'T attempt Fix #4 without architectural discussion

6. 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 does not equal 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.

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
• find-polluter - For test pollution bisection (flaky tests due to shared state): run .claude/tools/analysis/find-polluter/find-polluter.sh (or find-polluter.ps1 on Windows) from the project root to isolate which test pollutes the suite.

Related skills:

• tdd - For creating failing test case (Phase 4, Step 1)
• 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

AI-Assisted Debugging & Modern Observability (2025+)

OpenTelemetry: The New Stack Trace

For distributed systems, OpenTelemetry traces replace manual echo/env evidence gathering. A trace shows the complete request journey across service boundaries via span IDs and trace IDs (W3C Trace Context standard: traceparent/tracestate headers).

Evidence hierarchy for distributed failures (prefer in order):

1. Distributed traces (OpenTelemetry spans, correlated trace IDs)
2. Structured logs with correlation IDs
3. Metrics with timestamps
4. Manual instrumentation (Phase 1 Step 4 bash examples)

Common symptom — fragmented traces: Each service shows its own root span, trace IDs don't match across boundaries. This means context propagation is broken — fix header forwarding before investigating business logic.

AI-Assisted Root Cause Analysis

LLM-based debugging agents (2025 pattern) augment Phase 1 by reading production traces and correlating with codebase context to suggest minimal reproduction cases.

Use AI assistance for:

• High-complexity distributed failures with multi-service blast radius
• On-call incidents requiring rapid root cause identification
• Converting production traces into deterministic test reproducers

Do NOT skip Phase 1 when using AI assistance. AI suggestions are hypotheses — apply Phase 3 (hypothesis testing) before implementing any AI-suggested fix. AI cannot replace systematic investigation; it accelerates evidence gathering.

Anti-Patterns

| Anti-Pattern | Why It Fails | Correct Approach | | -------------------------------------------- | --------------------------------------------------------------------------------- | -------------------------------------------------------------------- | | "Quick fix for now, investigate later" | The quick fix becomes permanent; the root cause resurfaces as a different symptom | Always complete Phase 1 before touching production code | | Making multiple changes at once | Can't determine which change fixed or broke the system; creates regressions | One change per hypothesis test; verify before the next change | | Proposing AI-suggested fixes without testing | AI suggestions are hypotheses, not facts; applying them blindly skips Phase 3 | Treat AI suggestions as hypotheses to test, not answers to implement | | Attempting a 4th fix after 3 failures | N+1 fix attempts on a broken approach compound the problem | After 3 failed fixes, escalate to architecture review | | Skipping the failing test before the fix | You can't verify the fix worked, and regressions are invisible | Create the failing test first; it proves root cause and verifies fix |

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

• New pattern -> .claude/context/memory/learnings.md
• Issue found -> .claude/context/memory/issues.md
• Decision made -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.