/code-debugging
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.
$ npx -y skills add lingzhi227/agent-research-skills --skill code-debugging --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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- Slash command
/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
code-debugging.SKILL.mdname: code-debugging
description: 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.
argument-hint: [error-or-code]
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/)
Read more
name: code-debugging description: 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. argument-hint: [error-or-code]
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/)
31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
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Open skill - /backward-traceability
Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.
Open skill - /citation-management
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
Open skill - /data-analysis
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Open skill - /deep-research
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
Open skill

