debug-like-expert

glittercowboy/debug-like-expert

Coding

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About

SKILL.md

Deep analysis debugging mode for complex issues. This skill activates methodical investigation protocols with evidence gathering, hypothesis testing, and rigorous verification when standard troubleshooting has failed.

The skill emphasizes treating code you wrote with MORE skepticism than unfamiliar code, as cognitive biases about "how it should work" can blind you to actual implementation errors. Use scientific method to systematically identify root causes rather than applying quick fixes.

**Run on every invocation to detect domain-specific debugging expertise:**

# What files are we debugging?
echo "FILE_TYPES:"
find . -maxdepth 2 -type f 2>/dev/null | grep -E '\.(py|js|jsx|ts|tsx|rs|swift|c|cpp|go|java)$' | head -10

# Check for domain indicators
[ -f "package.json" ] && echo "DETECTED: JavaScript/Node project"
[ -f "Cargo.toml" ] && echo "DETECTED: Rust project"
[ -f "setup.py" ] || [ -f "pyproject.toml" ] && echo "DETECTED: Python project"
[ -f "*.xcodeproj" ] || [ -f "Package.swift" ] && echo "DETECTED: Swift/macOS project"
[ -f "go.mod" ] && echo "DETECTED: Go project"

# Scan for available domain expertise
echo "EXPERTISE_SKILLS:"
ls ~/.claude/skills/expertise/ 2>/dev/null | head -5

Present findings before starting investigation.

**Domain-specific expertise lives in `~/.claude/skills/expertise/`**

Domain skills contain comprehensive knowledge including debugging, testing, performance, and common pitfalls. Before investigation, determine if domain expertise should be loaded.

```bash ls ~/.claude/skills/expertise/ 2>/dev/null ```

This reveals available domain expertise (e.g., macos-apps, iphone-apps, python-games, unity-games).

If no expertise skills found: Proceed without domain expertise (graceful degradation). The skill works fine with general debugging methodology.

If user's description or codebase contains domain keywords, INFER the domain:

Keywords/Files	Domain Skill
"Python", "game", "pygame", ".py" + game loop	expertise/python-games
"React", "Next.js", ".jsx/.tsx"	expertise/nextjs-ecommerce
"Rust", "cargo", ".rs" files	expertise/rust-systems
"Swift", "macOS", ".swift" + AppKit/SwiftUI	expertise/macos-apps
"iOS", "iPhone", ".swift" + UIKit	expertise/iphone-apps
"Unity", ".cs" + Unity imports	expertise/unity-games
"SuperCollider", ".sc", ".scd"	expertise/supercollider
"Agent SDK", "claude-agent"	expertise/with-agent-sdk

If domain inferred, confirm:

Detected: [domain] issue → expertise/[skill-name]
Load this debugging expertise? (Y / see other options / none)

If no domain obvious, present options:

What type of project are you debugging?

Available domain expertise:
1. macos-apps - macOS Swift (SwiftUI, AppKit, debugging, testing)
2. iphone-apps - iOS Swift (UIKit, debugging, performance)
3. python-games - Python games (Pygame, physics, performance)
4. unity-games - Unity (C#, debugging, optimization)
[... any others found in build/]

N. None - proceed with general debugging methodology
C. Create domain expertise for this domain

Select:

When domain selected, READ all references from that skill:

cat ~/.claude/skills/expertise/[domain]/references/*.md 2>/dev/null

This loads comprehensive domain knowledge BEFORE investigation:

Common issues and error patterns
Domain-specific debugging tools and techniques
Testing and verification approaches
Performance profiling and optimization
Known pitfalls and anti-patterns
Platform-specific considerations

Announce: "Loaded [domain] expertise. Investigating with domain-specific context."

If domain skill not found: Inform user and offer to proceed with general methodology or create the expertise.

Domain expertise should be loaded BEFORE investigation when domain is known.

Domain expertise is NOT needed for:

Pure logic bugs (domain-agnostic)
Generic algorithm issues
When user explicitly says "skip domain context"

This skill activates when standard troubleshooting has failed. The issue requires methodical investigation, not quick fixes. You are entering the mindset of a senior engineer who debugs with scientific rigor.

Important: If you wrote or modified any of the code being debugged, you have cognitive biases about how it works. Your mental model of "how it should work" may be wrong. Treat code you wrote with MORE skepticism than unfamiliar code - you're blind to your own assumptions.

**VERIFY, DON'T ASSUME.** Every hypothesis must be tested. Every "fix" must be validated. No solutions without evidence.

ESPECIALLY: Code you designed or implemented is guilty until proven innocent. Your intent doesn't matter - only the code's actual behavior matters. Question your own design decisions as rigorously as you'd question anyone else's.

Before proposing any solution:

A. Document Current State

What is the EXACT error message or unexpected behavior?
What are the EXACT steps to reproduce?
What is the ACTUAL output vs EXPECTED output?
When did this start working incorrectly (if known)?

B. Map the System

Trace the execution path from entry point to failure point
Identify all components involved
Read relevant source files completely, not just scanning
Note dependencies, imports, configurations affecting this area

C. Gather External Knowledge (when needed)

Use MCP servers for API documentation, library details, or domain knowledge
Use web search for error messages, framework-specific behaviors, or recent changes
Check official docs for intended behavior vs what you observe
Look for known issues, breaking changes, or version-specific quirks

See references/when-to-research.md for detailed guidance on research strategy.

A. Form Hypotheses

Based on evidence, list possible causes:

[Hypothesis 1] - because [specific evidence]
[Hypothesis 2] - because [specific evidence]
[Hypothesis 3] - because [specific evidence]

B. Test Each Hypothesis

For each hypothesis:

What would prove this true?
What would prove this false?
Design a minimal test
Execute and document results

See references/hypothesis-testing.md for scientific method application.

C. Eliminate or Confirm

Don't move forward until you can answer:

Which hypothesis is supported by evidence?
What evidence contradicts other hypotheses?
What additional information is needed?

Only after confirming root cause:

A. Design Solution

What is the MINIMAL change that addresses the root cause?
What are potential side effects?
What could this break?

B. Implement with Verification

Make the change
Add logging/debugging output if needed to verify behavior
Document why this change addresses the root cause

C. Test Thoroughly

Does the original issue still occur?
Do the reproduction steps now work?
Run relevant tests if they exist
Check for regressions in related functionality

See references/verification-patterns.md for comprehensive verification approaches.

NO DRIVE-BY FIXES: If you can't explain WHY a change works, don't make it
VERIFY EVERYTHING: Test your assumptions. Read the actual code. Check the actual behavior
USE ALL TOOLS:
- MCP servers for external knowledge
- Web search for error messages, docs, known issues
- Extended thinking ("think deeply") for complex reasoning
- File reading for complete context
THINK OUT LOUD: Document your reasoning at each step
ONE VARIABLE: Change one thing at a time, verify, then proceed
COMPLETE READS: Don't skim code. Read entire relevant files
CHASE DEPENDENCIES: If the issue involves libraries, configs, or external systems, investigate those too
QUESTION PREVIOUS WORK: Maybe the earlier "fix" was wrong. Re-examine with fresh eyes

Before starting:

Context scan executed to detect domain
Domain expertise loaded if available and relevant

During investigation:

Do you understand WHY the issue occurred?
Have you verified the fix actually works?
Have you tested the original reproduction steps?
Have you checked for side effects?
Can you explain the solution to someone else?
Would this fix survive code review?

If you can't answer "yes" to all of these, keep investigating.

CRITICAL: Do NOT mark debugging tasks as complete until this checklist passes.

## Issue: [Problem Description]

### Evidence
[What you observed - exact errors, behaviors, outputs]

### Investigation
[What you checked, what you found, what you ruled out]

### Root Cause
[The actual underlying problem with evidence]

### Solution
[What you changed and WHY it addresses the root cause]

### Verification
[How you confirmed this works and doesn't break anything else]

For deeper topics, see reference files:

Debugging mindset: references/debugging-mindset.md

First principles thinking applied to debugging
Cognitive biases that lead to bad fixes
The discipline of systematic investigation
When to stop and restart with fresh assumptions

Investigation techniques: references/investigation-techniques.md

Binary search / divide and conquer
Rubber duck debugging
Minimal reproduction
Working backwards from desired state
Adding observability before changing code

Hypothesis testing: references/hypothesis-testing.md

Forming falsifiable hypotheses
Designing experiments that prove/disprove
What makes evidence strong vs weak
Recovering from wrong hypotheses gracefully

Verification patterns: references/verification-patterns.md

Definition of "verified" (not just "it ran")
Testing reproduction steps
Regression testing adjacent functionality
When to write tests before fixing

Research strategy: references/when-to-research.md

Signals that you need external knowledge
What to search for vs what to reason about
Balancing research time vs experimentation