devops
DevOps practices for web development including Docker, CI/CD, deployment, monitoring, and infrastructure as code. Use when setting up deployment pipelines, containerizing applications, configuring servers, or implementing DevOps workflows.
resource-monitor
Monitor system resources (CPU, memory, disk, network) during development and production.
container-debugging
Debug Docker containers and containerized applications. Diagnose deployment issues, container lifecycle problems, and resource constraints.
cpu-profiling
Profile CPU usage to identify hot spots and bottlenecks. Optimize code paths consuming most CPU time for better performance and resource efficiency.
infrastructure-cost-optimization
Optimize cloud infrastructure costs through resource rightsizing, reserved instances, spot instances, and waste reduction strategies.
infrastructure-monitoring
Set up comprehensive infrastructure monitoring with Prometheus, Grafana, and alerting systems for metrics, health checks, and performance tracking.
stress-testing
Test system behavior under extreme load conditions to identify breaking points, capacity limits, and failure modes. Use for stress test, capacity testing, breaking point analysis, spike test, and system limits validation.
performance-profiling
Instruments integration and performance analysis workflows for iOS apps. Use when profiling CPU usage, memory allocation, network activity, or energy consumption. Covers Time Profiler, Allocations, Leaks, Network instruments, and performance optimization strategies.
get-available-resources
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
