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
| Category | Examples | Severity |
|---|---|---|
| SyntaxError | Invalid syntax, indentation | Low |
| ImportError | Missing module, wrong name | Low |
| RuntimeError | Division by zero, shape mismatch | Medium |
| TimeoutError | Infinite loop, too slow | Medium |
| OutputError | Missing files, wrong format | Medium |
| LogicError | Wrong results, 0% accuracy | High |
Step 2: Analyze Root Cause
- Read the error traceback (last 1500 chars if truncated)
- Identify the exact line and variable causing the error
- 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:
- Explain why the error occurred
- Identify which lines caused it
- Describe the fix line-by-line
- 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
