debugging

name	debugging
description	Systematic 4-phase debugging with root cause investigation. Use when fixing bugs to prevent random fixes.
version	1.1.1
model	composer-2.5-fast
invoked_by	both
user_invocable	true
tools	["Read","Write","Edit","Bash","Glob","Grep"]
best_practices	["Investigate root cause before any fix","Reproduce the bug reliably first","Compare working vs broken examples","Make one change at a time"]
error_handling	strict
streaming	supported
verified	true
lastVerifiedAt	"2026-02-22T00:00:00.000Z"

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.