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Found 140 Skills
React render performance patterns including React Compiler integration, memoization strategies, TanStack Virtual, and DevTools profiling. Use when debugging slow renders, optimizing large lists, or reducing unnecessary re-renders.
Python performance optimization patterns using profiling, algorithmic improvements, and acceleration techniques. Use when optimizing slow Python code, reducing memory usage, or improving application throughput and latency.
This skill should be used when profiling code, optimizing bottlenecks, benchmarking, or when "performance", "profiling", "optimization", or "--perf" are mentioned.
Automatically discover software engineering practice skills when working with code review, documentation, pair programming, production debugging, performance profiling, deployment strategies, or software engineering practices. Activates for engineering development tasks.
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.
1600+ lines of performance optimization mastery - profiling, rendering, memory, network, battery, APK size with production-ready code examples.
Apply systematic performance optimization techniques when writing or reviewing code. Use when optimizing hot paths, reducing latency, improving throughput, fixing performance regressions, or when the user mentions performance, optimization, speed, latency, throughput, profiling, or benchmarking.
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.
Rust performance optimization covering memory allocation, ownership efficiency, data structure selection, iterator patterns, async concurrency, algorithm complexity, compile-time optimization, and micro-optimizations. Use when optimizing Rust code performance, profiling hot paths, reducing allocations, or choosing optimal data structures. Complements the rust-refactor skill (idiomatic patterns and architecture). Does NOT cover code style, naming conventions, or project organization (see rust-refactor skill).
Systematic debugging playbook for application errors and incidents: crashes, regressions, intermittent failures, production-only bugs, performance issues, stack traces, log/trace analysis, profiling, and distributed systems root cause analysis.
Profile datasets to understand schema, quality, and characteristics. Use when analyzing data files (CSV, JSON, Parquet), discovering dataset properties, assessing data quality, or when user mentions data profiling, schema detection, data analysis, or quality metrics. Provides basic and intermediate profiling including distributions, uniqueness, and pattern detection.