Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.

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 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/)

code-debugging