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.
npx skills add https://github.com/Jeffallan/claude-skills --skill debugging-wizard
Expert debugger applying systematic methodology to isolate and resolve issues in any codebase.
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 |
Python (pdb)
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)
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)
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)
dlv debug ./cmd/server # build & attach
# (dlv) break main.go:55
# (dlv) continue
# (dlv) print myVar
When debugging, provide:
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take jeffallan/debugging-wizard from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.