Code Debugging

Systematically debug experiment code with structured error categorization and fix strategies.

Input

• $0 — Error message, stderr output, or code file with issues
• $1 — Optional: the code that produced the error

References

• Debug patterns and state machine: ~/.claude/skills/code-debugging/references/debug-patterns.md

Workflow

Step 1: Categorize the Error

Step 2: Analyze Root Cause

1. Read the error traceback (last 1500 chars if truncated)
2. Identify the exact line and variable causing the error
3. Check for common patterns:
   • Device mismatch (CPU vs GPU tensors)
   • Shape mismatch in matrix operations
   • Missing data normalization
   • Off-by-one errors in indexing
   • Incorrect loss function for task type

Step 3: Apply Fix Strategy

For syntax/import errors: Direct fix, single attempt For runtime errors: Fix and rerun, up to 4 retries For logic errors: Reflect on approach, consider alternative methods For timeout: Reduce dataset size, optimize bottleneck, add early stopping

Step 4: Reflect and Prevent

After fixing:

1. Explain why the error occurred
2. Identify which lines caused it
3. Describe the fix line-by-line
4. Note patterns to avoid in future code

Fix Strategy State Machine

Stage 0 (first attempt) → repost code as fresh
Stage 1 (second attempt) → repost or leave depending on severity
Stage 2 (third attempt) → regenerate from scratch if still failing

Rules

• Prefer minimal targeted edits over full rewrites
• Maximum 4-5 fix attempts before changing approach
• Always truncate long error outputs to last 1500 characters
• After fixing, verify the fix doesn’t introduce new errors
• Keep error history to avoid repeating the same mistakes
• If 0% accuracy: check accuracy calculation first, then check data pipeline

Related Skills

• Upstream: experiment-code
• See also: paper-to-code, data-analysis