420 skills · sorted by relevance
Profile app launches and individual user flows in iOS Simulator with ETTrace. Collect traces with readable function names, identify CPU-heavy call stacks, and compare performance changes when recording conditions are consistent.
Investigate observed slowdowns or increased CPU, memory allocation, and IO costs in a process you control. Use controlled timing and runtime-specific profiling tools to test a cause, then check performance and behavior after a fix.
Choose diagnostic tools and collect performance traces for production .NET problems such as high CPU usage, memory growth, or slow requests. Get environment-specific collection commands and guidance on which tools to use for later analysis.
Debug Node.js from the terminal by pausing execution, inspecting variables, and tracing function calls. Use interactive commands or scripts to investigate failing tests, collect runtime state, and capture CPU profiles or memory snapshots.
Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.
Measure scripts, running processes, or whole systems to find performance bottlenecks. Select suitable tools, add measurement code when needed, and report CPU, GPU, memory, and communication findings with prioritized recommendations and a record of file changes.
Improve MNN's Vulkan backend by separating CPU scheduling bottlenecks from GPU computation costs. It guides operator and shader changes, new feature integration with compatibility fallbacks, and device testing to verify both correctness and end-to-end performance.
Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.
Diagnose slow systems, crashes, and resource problems on servers, PCs, virtual machines, and containers. Quick checks identify affected components, while deeper analysis investigates causes and provides optimization advice or repair plans.
Investigate Finelog logs and runtime measurements for Iris tasks, training, and vLLM. Discover available fields, write time-bounded SQL, summarize resource usage, compare central and regional records, and interpret counters correctly.
Monitor agent and system performance through live metrics, resource tracking, bottleneck analysis, and anomaly detection. Track response times and availability against service targets, and prepare dashboard data and performance reports.
Investigate slow TPU training on GKE by comparing diagnostic events with one-minute monitoring metrics. Separate memory or CPU bottlenecks from hardware and network issues, and identify the appropriate next steps.
Find relevant Google Cloud monitoring metrics for a service or resource. Retrieves current metric definitions through the API, filters them by the requested concepts, and presents selected metrics with their names, data types, units, and monitored resources.
Check CPU cores, GPUs, available memory, and disk space before demanding computing tasks. Save a JSON report with recommendations for parallel workers, GPU use, and processing data in chunks when memory is limited.
Use to lock/cap Jetson CPU/GPU/EMC clocks, toggle EMC/CPU DVFS, or change cpufreq governors by editing BPMP DTB and nvpower.sh pre-flash. Do NOT use for live tuning or nvpmodel edits.
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Use when diagnosing NPU + PyTorch or LLM Serving Host CPU affinity, NUMA locality, cgroup/cpuset constraints, CPU range conflicts, PyTorch/runtime threading, DataLoader, tokenizer, scheduler, vLLM-Ascend, SGLang, TTFT, TPOT, tokens/s, QPS, or multi-rank/multi-worker CPU binding issues.
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Benchmark distributed consensus protocols, methods computers use to agree on shared results. Compare processing rates, response times, scaling behavior, and resource usage across Byzantine, Raft, and Gossip, then identify bottlenecks and potential tuning changes.
Create container-based hosted agents in Azure AI Foundry using Python and the Azure AI Projects SDK. Follow examples for resource settings, authentication, version management, and checks needed before treating a deployment as healthy.
Design and improve Unity games that process large numbers of entities. Learn ECS, an approach that separates data from behavior, and apply Jobs and Burst patterns to organize parallel processing and address CPU performance bottlenecks.
Write and register SGLang unit and end-to-end tests, from choosing models and file locations to reusing server fixtures. Follow project rules for meaningful assertions, CI registration, dependency mocking, and safe server cleanup after tests.
Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.
Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
Investigate slow Node.js, Python, and Go applications by measuring CPU use, memory, database queries, and data transfer. Identify bottlenecks, inspect frontend bundle sizes, run load tests, and compare measurements before and after changes to verify improvements.
Install the free OpenWork desktop app on macOS, Windows, or Linux using the appropriate download for the computer. Guide initial setup, folder authorization, model selection, and a first task such as summarizing local files.