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Found 909 Skills
Use when tasks require current, source-backed technical information from MCP tools. Apply for library/API questions, dependency version checks, third-party integration work, framework- or SDK-specific debugging, and any case where stale model knowledge could cause incorrect guidance.
Use when encountering bugs or test failures - systematic debugging using debuggers, internet research, and agents to find root cause before fixing
Use when preparing branches, commits, or PRs for Python changes — scoping work, running validation gates, and ensuring merge readiness. Also use when debugging CI gate failures, resolving lockfile conflicts, or uncertain what checks to run before opening a PR.
Intelligent Code Debugging Assistant that helps you visualize how your code runs. When you say things like "I want to see why this function is so slow", "The code throws an error halfway through execution and I don't know where the problem is", or "This business logic is too complex and I can't figure out the execution order", I will help you trace the code execution path, identify slow-performing sections, and locate the root cause of errors. When skill optimization points are identified, I will ask if you want to call the skill-evolution-driver for optimization
Master iOS testing - XCTest, UI testing, mocking, debugging, performance
Use when investigating why something happened and need to distinguish correlation from causation, identify root causes vs symptoms, test competing hypotheses, control for confounding variables, or design experiments to validate causal claims. Invoke when debugging systems, analyzing failures, researching health outcomes, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?"
Complete debugging and troubleshooting guide for Shopify including Liquid errors, theme preview debugging, API error handling, JavaScript console debugging, network request inspection, cart issues, checkout problems, and common error codes. Use when debugging Liquid syntax errors, troubleshooting theme rendering issues, fixing API errors, debugging JavaScript, investigating cart problems, or resolving webhook failures.
Comprehensive Google Tag Manager guide covering container setup, tags, triggers, variables, data layer, debugging, custom templates, and API automation. Use when working with GTM implementation, configuration, optimisation, troubleshooting, or any GTM-related tasks.
Debug Angular applications systematically with expert-level diagnostic techniques. This skill provides comprehensive guidance for troubleshooting dependency injection errors, change detection issues (NG0100), RxJS subscription leaks, lazy loading failures, zone.js problems, and common Angular runtime errors. Includes structured four-phase debugging methodology, Angular DevTools usage, console debugging utilities (ng.probe), and performance profiling strategies for modern Angular applications.
Debug Docker containers, images, and infrastructure with systematic diagnostic techniques. This skill provides comprehensive guidance for troubleshooting container exit codes, OOM kills, image build failures, networking issues, volume mount problems, and permission errors. Covers four-phase debugging methodology from quick assessment to deep analysis, essential Docker commands, debug container techniques for minimal images, and platform-specific troubleshooting for Windows, Mac, and Linux.
Debug Flask applications systematically with this comprehensive troubleshooting skill. Covers routing errors (404/405), Jinja2 template issues, application context problems, SQLAlchemy session management, blueprint registration failures, and circular import resolution. Provides structured four-phase debugging methodology with Flask-specific tools including Werkzeug debugger, Flask-DebugToolbar, and Flask shell for interactive investigation.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.