Code debugging
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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.
SKILL.md
2.6 KB, 550 tokens by cl100k_base, as published. Nobody here has run it
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
What ships with it: 1 file
4.2 KB alongside SKILL.md
references/
- debug-patterns.md4.2 KB
Gives 0 of the 12 instructions most debug triage skills give in 550 tokens
Counted across 839 of the 1,149 authors here whose files we hold, read 2026-08-07
- Investigate root cause before proposing any fixin 102 of 839, across 67 files
- Read error messages completelyin 89 of 839, across 49 files
- Create a failing test case before fixingin 84 of 839, across 46 files
- Reproduce the issue consistentlyin 82 of 839, across 41 files
- Change one variable at a timein 82 of 839, across 42 files
- Check recent changesin 74 of 839, across 36 files
- Write the regression test before fixingin 74 of 839, across 40 files
- Fix the root cause not the symptomin 60 of 839, across 45 files
- Implement a single fix at a timein 59 of 839, across 20 files
- Trace data flow backward to the sourcein 50 of 839, across 20 files
- Remove all debug instrumentationin 49 of 839, across 13 files
- Form a single hypothesisin 48 of 839, across 18 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.