debug-mastery（调试掌握）

<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

1. 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

1. Check Recent Changes

- What changed that could cause this? - Git diff, recent commits - New dependencies, config changes - Environmental differences

1. 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

1. 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?

1. Compare Against References

- If implementing pattern, read reference implementation COMPLETELY - Don't skim - read every line - Understand the pattern fully before applying

1. Identify Differences

- What's different between working and broken? - List every difference, however small - Don't assume "that can't matter"

1. 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

1. Test Minimally

- Make the SMALLEST possible change to test hypothesis - One variable at a time - Don't fix multiple things at once

1. Verify Before Continuing

- Did it work? Yes → Phase 4 - Didn't work? Form NEW hypothesis - DON'T add more fixes on top

1. 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

1. Implement Single Fix

- Address the root cause identified - ONE change at a time - No "while I'm here" improvements - No bundled refactoring

1. Verify Fix

- Test passes now? - No other tests broken? - Issue actually resolved?

1. 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

1. 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

</red_flags_and_rationalizations> <observability_and_references>

📊 QUICK REFERENCE

🛠️ 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>