/debug-like-expert

name: debug-like-expert
description: Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.

<objective> 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. </objective>

<context_scan> **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.** </context_scan>

<domain_expertise> **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.

<scan_domains>

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. </scan_domains>

<inference_rules> 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)

</inference_rules>

<no_inference> 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:

</no_inference>

<load_domain> 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. </load_domain>

<when_to_load> 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"

</when_to_load> </domain_expertise>

<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. </context>

<core_principle> **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. </core_principle>

<quick_start>

<evidence_gathering>

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 t