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Found 215 Skills
Improve code performance without changing behavior. Use when code fails latency/throughput requirements. Covers profiling, caching, and algorithmic optimization.
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.
Conducts comprehensive Magento 2 performance analysis and optimization. Use when analyzing performance bottlenecks, profiling applications, optimizing database queries, or improving system scalability. Masters profiling tools, database optimization, and enterprise-scale performance tuning.
Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (see golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill).
Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See golang-benchmark and golang-performance skills).
Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.
Optimize application performance for speed, efficiency, and scalability. Use when improving page load times, reducing bundle size, optimizing database queries, or fixing performance bottlenecks. Handles React optimization, lazy loading, caching, code splitting, and profiling.
Use when performance requirements exist, when you suspect performance regressions, or when Core Web Vitals or load times need improvement. Use when profiling reveals bottlenecks that need fixing.
Applies and explains code conventions across TypeScript, React, C#, and Markdown. Enforces naming rules, file naming patterns, TSDoc and XML doc standards, inline comment intent (the *why*, not the *what*), code structure, error handling, async patterns, and dead code policy. Also enforces ADR and contributor doc decisions, and flags decisions that appear stale or misaligned with current tooling. USE FOR: convention questions, code review against project standards, applying naming rules, auditing intent comments, checking TSDoc completeness, enforcing recorded ADR decisions, and flagging stale architectural decisions. DO NOT USE FOR: security vulnerability scanning, performance profiling, runtime debugging, or generating net-new code without a review target.
Comprehensive immune repertoire analysis for T-cell and B-cell receptor sequencing data. Analyze TCR/BCR repertoires to assess clonality, diversity, V(D)J gene usage, CDR3 characteristics, convergence, and predict epitope specificity. Integrate with single-cell data for clonotype-phenotype associations. Use for adaptive immune response profiling, cancer immunotherapy research, vaccine response assessment, autoimmune disease studies, or repertoire diversity analysis in immunology research.
Native profiling for CPU hotspots, UI hangs, and memory leaks. Currently iOS-only (xctrace-backed); Android support (Perfetto/simpleperf) is on the roadmap. Use when diagnosing native-level performance issues.
Analyze non-coding RNAs (miRNAs, lncRNAs, circRNAs) using miRBase, LNCipedia, RNAcentral, Rfam, and target prediction databases. Covers ncRNA identification, target prediction, disease associations, expression profiling, and functional annotation. Use when asked about microRNAs, long non-coding RNAs, RNA interference, miRNA targets, lncRNA function, or ncRNA-disease associations.