code-debugging

The full skill.

Original instructions from the publisher’s SKILL.md

# 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
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](../experiment-code/)
- See also: [paper-to-code](../paper-to-code/), [data-analysis](../data-analysis/)