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Found 40 Skills
Use when automating Instruments profiling, running headless performance analysis, or integrating profiling into CI/CD - comprehensive xctrace CLI reference with record/export patterns
Expert at advanced debugging and root cause analysis. Use when troubleshooting complex issues, finding root causes of bugs, investigating performance problems, or analyzing system failures.
Expert in system optimization, profiling, and scalability. Specializes in eBPF, Flamegraphs, and kernel-level tuning.
Advanced sub-skill for PyTorch focused on deep research and production engineering. Covers custom Autograd functions, module hooks, advanced initialization, Distributed Data Parallel (DDP), and performance profiling.
Debugging and troubleshooting Tokio applications using tokio-console, detecting deadlocks, memory leaks, and performance issues. Use when diagnosing async runtime problems.
Automatically discover debugging and profiling skills when working with GDB, LLDB, breakpoints, profiling, stack traces, memory leaks, core dumps, or performance profiling. Activates for debugging development tasks.
Django-extensions management commands for project introspection, debugging, and development. Use when exploring URLs, models, settings, database schema, running scripts, or profiling performance. Triggers on questions about Django project structure, model fields, URL routes, or requests to run development servers.
Correlates performance targets with actual profiling results. Identifies bottlenecks and validates against non-functional requirements.
React DevTools CLI for AI agents. Use when the user asks you to debug a React or React Native app at runtime, inspect component props/state/hooks, diagnose render performance, profile re-renders, find slow components, or understand why something re-renders. Triggers include "why does this re-render", "inspect the component", "what props does X have", "profile the app", "find slow components", "debug the UI", "check component state", "the app feels slow", or any React runtime debugging task.
Comprehensive bash script debugging and troubleshooting techniques for 2025
When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.
Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).