debugging-wizard

--- name: debugging-wizard description: Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting crashes, or performing log analysis, error investigation, or root cause analysis. license: MIT metadata: author: https://github.com/Jeffallan version: "1.1.0" domain: quality triggers: debug, error, bug, exception, traceback, stack trace, troubleshoot, not working, crash, fix issue role: specialist scope: analysis output-format: analysis related-skills: test-master, fullstack-guardian, monitoring-expert --- # Debugging Wizard Expert debugger applying systematic methodology to isolate and resolve issues in any codebase. ## Core Workflow 1. **Reproduce** - Establish consistent reproduction steps 2. **Isolate** - Narrow down to smallest failing case 3. **Hypothesize and test** - Form testable theories, verify/disprove each one 4. **Fix** - Implement and verify solution 5. **Prevent** - Add tests/safeguards against regression ## Reference Guide Load detailed guidance based on context: <!-- Systematic Debugging row adapted from obra/superpowers by Jesse Vincent (@obra), MIT License --> | Topic | Reference | Load When | |-------|-----------|-----------| | Debugging Tools | `references/debugging-tools.md` | Setting up debuggers by language | | Common Patterns | `references/common-patterns.md` | Recognizing bug patterns | | Strategies | `references/strategies.md` | Binary search, git bisect, time travel | | Quick Fixes | `references/quick-fixes.md` | Common error solutions | | Systematic Debugging | `references/systematic-debugging.md` | Complex bugs, multiple failed fixes, root cause analysis | ## Constraints ### MUST DO - Reproduce the issue first - Gather complete error messages and stack traces - Test one hypothesis at a time - Document findings for future reference - Add regression tests after fixing - Remove all debug code before committing ### MUST NOT DO - Guess without testing - Make multiple changes at once - Skip reproduction steps - Assume you know the cause - Debug in production without safeguards - Leave console.log/debugger statements in code ## Common Debugging Commands **Python (pdb)** ```bash python -m pdb script.py # launch debugger # inside pdb: # b 42 — set breakpoint at line 42 # n — step over # s — step into # p some_var — print variable # bt — print full traceback ``` **JavaScript (Node.js)** ```bash node --inspect-brk script.js # pause at first line, attach Chrome DevTools # In Chrome: open chrome://inspect → click "inspect" # Sources panel: add breakpoints, watch expressions, step through ``` **Git bisect (regression hunting)** ```bash git bisect start git bisect bad # current commit is broken git bisect good v1.2.0 # last known good tag/commit # Git checks out midpoint — test, then: git bisect good # or: git bisect bad # Repeat until git identifies the first bad commit git bisect reset ``` **Go (delve)** ```bash dlv debug ./cmd/server # build & attach # (dlv) break main.go:55 # (dlv) continue # (dlv) print myVar ``` ## Output Templates When debugging, provide: 1. **Root Cause**: What specifically caused the issue 2. **Evidence**: Stack trace, logs, or test that proves it 3. **Fix**: Code change that resolves it 4. **Prevention**: Test or safeguard to prevent recurrence [Documentation](https://jeffallan.github.io/claude-skills/skills/quality/debugging-wizard/)

1. **Gather Inputs**: Collect the error message, stack trace, and relevant log snippets. Tools like IDE debuggers (e.g., IntelliJ, VS Code), log aggregators (e.g., ELK Stack, Splunk), or error tracking tools (e.g., Sentry, Datadog) can help extract this data. 2. **Paste the Prompt**: Copy the prompt template and replace the placeholders with your specific error details. Ensure the stack trace and logs are complete to avoid missing critical context. 3. **Analyze the Output**: Review the AI’s summary, execution flow, and log correlation. Verify the hypotheses by checking the suggested next steps (e.g., database queries, code changes). 4. **Iterate**: If the root cause isn’t clear, provide additional logs or context (e.g., environment variables, recent code changes) and ask the AI to refine its analysis. 5. **Implement Fixes**: Use the proposed solutions to resolve the issue. Test the changes in a staging environment before deploying to production. Tip: For complex issues, break the problem into smaller chunks (e.g., isolate the failing component) and debug each part separately. Use the AI’s output as a starting point, but validate its findings with manual checks.