use-debug
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
When & Why to Use This Skill
This Claude skill implements a rigorous, four-phase debugging methodology designed to eliminate guesswork and identify root causes. It enforces the 'Iron Law' of investigation before implementation, guiding developers through systematic error analysis, data flow tracing, and hypothesis testing. By prioritizing Test-Driven Development (TDD) and architectural evaluation, it prevents the introduction of new bugs and ensures long-term code stability.
Use Cases
- Root Cause Investigation: Systematically analyzing stack traces and error messages to identify the source of a bug rather than just fixing symptoms.
- Multi-Component Debugging: Tracing data flow across different system boundaries to locate where data corruption or unexpected values originate.
- Preventing Regression: Using the integrated TDD approach to create failing test cases that verify a bug is truly fixed and won't return.
- Architectural Review: Identifying when repeated fix failures indicate a fundamental architectural flaw rather than a simple coding error.
| name | use-debug |
|---|---|
| description | Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes |
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.
The Iron Law
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
If you haven't completed Phase 1, you cannot propose fixes.
The Four Phases
Phase 1: Root Cause Investigation
BEFORE attempting ANY fix:
Read Error Messages Carefully
- Read stack traces completely
- Note line numbers, file paths, error codes
- They often contain the exact solution
Reproduce Consistently
- Can you trigger it reliably?
- Exact steps? Every time?
- If not reproducible → gather more data, don't guess
Check Recent Changes
- Git diff, recent commits
- New dependencies, config changes
- Environmental differences
Gather Evidence in Multi-Component Systems
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
Trace Data Flow
- 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 Working Examples
- Locate similar working code in same codebase
- What works that's similar to what's broken?
Compare Against References
- If implementing pattern, read reference implementation COMPLETELY
- Don't skim - understand the pattern fully
Identify Differences
- What's different between working and broken?
- Don't assume "that can't matter"
Phase 3: Hypothesis and Testing
Form Single Hypothesis
- State clearly: "I think X is the root cause because Y"
- Be specific, not vague
Test Minimally
- SMALLEST possible change to test hypothesis
- One variable at a time
- Don't fix multiple things at once
Verify Before Continuing
- Did it work? Yes → Phase 4
- Didn't work? Form NEW hypothesis
- DON'T add more fixes on top
Phase 4: Implementation
Create Failing Test Case
- Use the autonome:use-tdd skill
- Simplest possible reproduction
- MUST have before fixing
Implement Single Fix
- Address the root cause identified
- ONE change at a time
- No "while I'm here" improvements
Verify Fix
- Test passes now?
- No other tests broken?
- Issue actually resolved?
If Fix Doesn't Work
- STOP
- Count: How many fixes have you tried?
- If < 3: Return to Phase 1, re-analyze
- If ≥ 3: STOP and question the architecture
- DON'T attempt Fix #4 without discussion
If 3+ Fixes Failed: Question Architecture
Pattern indicating architectural problem:
- Each fix reveals new shared state/coupling in different place
- Fixes require "massive refactoring"
- Each fix creates new symptoms elsewhere
STOP and question fundamentals:
- Is this pattern fundamentally sound?
- Should we refactor architecture vs. continue fixing symptoms?
Discuss before attempting more fixes.
Red Flags - STOP
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"
- "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.
Common Rationalizations
| Excuse | Reality |
|---|---|
| "Issue is simple, don't need process" | Simple issues have root causes too. |
| "Emergency, no time for process" | Systematic debugging is FASTER than guess-and-check. |
| "Just try this first, then investigate" | First fix sets the pattern. Do it right from start. |
| "I'll write test after confirming fix works" | Untested fixes don't stick. Test first proves it. |
| "I see the problem, let me fix it" | Seeing symptoms ≠ understanding root cause. |
| "One more fix attempt" (after 2+ failures) | 3+ failures = architectural problem. |
Supporting Techniques
Available in this directory:
- root-cause-tracing.md - Trace bugs backward through call stack
- defense-in-depth.md - Add validation at multiple layers
- condition-based-waiting.md - Replace arbitrary timeouts
Related skills:
- autonome:use-tdd - For creating failing test case
- autonome:use-verify - Verify fix worked