Systematic debugging frameworks for finding and fixing bugs - includes root cause analysis, defense-in-depth validation, and verification protocols
npx skills add https://github.com/mrgoonie/claudekit-skills --skill debugging
A collection of systematic debugging methodologies that ensure thorough investigation before attempting fixes.
Location: systematic-debugging/SKILL.md
Four-phase debugging framework: Root Cause Investigation → Pattern Analysis → Hypothesis Testing → Implementation. The iron law: NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.
Location: root-cause-tracing/SKILL.md
Trace bugs backward through the call stack to find the original trigger. Don't fix symptoms - find where invalid data originated and fix at the source.
Location: defense-in-depth/SKILL.md
Validate at every layer data passes through to make bugs structurally impossible. Four layers: Entry Point → Business Logic → Environment Guards → Debug Instrumentation.
Location: verification-before-completion/SKILL.md
Run verification commands and confirm output before claiming success. The iron law: NO COMPLETION CLAIMS WITHOUT FRESH VERIFICATION EVIDENCE.
| Symptom | Sub-Skill |
|---------|-----------|
| Test failure, unexpected behavior | systematic-debugging |
| Error appears in wrong location | root-cause-tracing |
| Same bug keeps recurring | defense-in-depth |
| Need to confirm fix works | verification-before-completion |
> "Systematic debugging is FASTER than guess-and-check thrashing."
From real debugging sessions:
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 mrgoonie/debugging 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.