debug-mastery skill

---
name: debug-mastery
description: Systematic debugging methodology with 4-phase process, root cause tracing, and elite observability standards. No fixes without investigation.
allowed-tools: Read, Write, Edit, Glob, Grep, Bash
---
<domain_overview>
# 🐛 DEBUG MASTERY: SYSTEMATIC DEBUGGING
> **Philosophy:** Random fixes waste time and create new bugs. Quick patches mask underlying issues. ALWAYS find root cause before attempting fixes.
**FORENSIC ANALYSIS MANDATE (CRITICAL):** Never apply a fix without a confirmed root cause. AI-generated fixes often address symptoms rather than underlying architectural logic. You MUST perform a 'Forensic Investigation' that identifies the specific assumption or boundary condition that failed. For every fix, you must provide a brief analysis note explaining WHY the original architecture allowed the bug to exist, transforming every error into a systemic engineering lesson.
---
## 🚨 THE IRON LAW
```
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
```
If you haven't completed Phase 1, you cannot propose fixes.
**Violating the letter of this process is violating the spirit of debugging.**
---
## 📋 WHEN TO USE
Use for ANY technical issue:
- Test failures
- Bugs in production
- Unexpected behavior
- Performance problems
- Build failures
- Integration issues
**Use 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 (systematic is faster than thrashing)
- Manager wants it fixed NOW (systematic is faster than guess-and-check)
</domain_overview>
<debugging_phases>
## 🔄 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
   ```
5. **Trace Data Flow**
   **WHEN error is deep in call stack:**
   See `@root-cause-tracing.md` 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-mastery` 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. **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 5 below)**
   - DON'T attempt Fix #4 without architectural discussion
5. **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 user before attempting more fixes**
   This is NOT a failed hypothesis - this is a wrong architecture.
</debugging_phases>
<red_flags_and_rationalizations>
## 🚨 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)
---
## 🚫 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 ≠ understanding root cause. |
| "One more fix attempt" (after 2+ failures) | 3+ failures = architectural problem. Question pattern, don't fix again. |
</red_flags_and_rationalizations>
<observability_and_references>
## 📊 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 |
---
## 🛠️ SUPPORTING TECHNIQUES
These techniques are part of systematic debugging:
- **`@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
---
## 🛰️ OBSERVABILITY TOOLING
### Precision Logging (JSON-First)
**Mandatory Fields:** `timestamp`, `level`, `traceId`, `component`, `message`, `context`
**Log Levels:**
- **ERROR:** System failure, data loss, crash. Immediate audit required.
- **WARN:** Recoverable anomaly (retry, fallback triggered).
- **INFO:** Significant state change (phase transition, tool started).
- **DEBUG:** Detailed execution path, raw payloads, environment.
### Distributed Tracing
1. **Propagation:** Every request/action carries `TraceID`
2. **Span Definition:** Wrap tool calls and complex logic to measure latency
### Domain-Specific Troubleshooting
**Frontend:**
- Time-Travel: Redux DevTools, state snapshots
- Visual Regression: `ux-audit.js` for layout shifts
**Backend:**
- eBPF Observability: Kernel-level IO/Network tracing
- Transaction Audits: ACID compliance verification
**Extensions (MV3):**
- Service Worker: Verify `chrome.alarms` pulses
- Context Bridge: Check "Disconnected Port" errors
---
## 📈 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
---
## 🔗 RELATED SKILLS
- **@tdd-mastery** - For creating failing test case (Phase 4, Step 1)
- **@verification-mastery** - Verify fix worked before claiming success
- **@clean-code** - Prevent bugs through good practices
</observability_and_references>