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Found 1,083 Skills
Use when you see memory warnings, 'retain cycle', app crashes from memory pressure, or when asking 'why is my app using so much memory', 'how do I find memory leaks', 'my deinit is never called', 'Instruments shows memory growth', 'app crashes after 10 minutes' - systematic memory leak detection and fixes for iOS/macOS
Systematic debugging process for Laravel applications - ensures root cause investigation before attempting fixes. Use for any Laravel issue (test failures, bugs, unexpected behavior, performance problems).
Use when encountering "Unable to simultaneously satisfy constraints" errors, constraint conflicts, ambiguous layout warnings, or views positioned incorrectly - systematic debugging workflow for Auto Layout issues in iOS
Use when encountering dependency conflicts, CocoaPods/SPM resolution failures, "Multiple commands produce" errors, or framework version mismatches - systematic dependency and build configuration debugging for iOS projects. Includes pressure scenario guidance for resisting quick fixes under time constraints
Debug Streamlit frontend and backend changes using make debug with hot-reload. Use when testing code changes, investigating bugs, checking UI behavior, or needing screenshots of the running app.
Systematic debugging frameworks for finding and fixing bugs - includes root cause analysis, defense-in-depth validation, and verification protocols
How to debug tursodb using Bytecode comparison, logging, ThreadSanitizer, deterministic simulation, and corruption analysis tools
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.
Debug network issues using browser tools and network analysis. Diagnose connection problems, latency, and data transmission issues.
Debug Docker containers and containerized applications. Diagnose deployment issues, container lifecycle problems, and resource constraints.
Identify and debug performance regressions from code changes. Use comparison and profiling to locate what degraded performance and restore baseline metrics.
Debug issues that occur sporadically and are hard to reproduce. Use monitoring and systematic investigation to identify root causes of flaky behavior.