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):** ```bash # 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:** ```bash # 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 |