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Found 117 Skills
Use this skill when writing or running performance benchmarks for Jazz packages. Covers cronometro setup, file conventions, gotchas with worker threads, and how to compare implementations.
Benchmarking and competitive analysis techniques. Compares performance, processes, and practices against industry standards, competitors, and best-in-class organizations.
Activate this skill when BenchmarkDotNet (BDN) is involved in the task — creating, running, configuring, or reviewing BDN benchmarks. Also activate when microbenchmarking .NET code would be useful and BenchmarkDotNet is the likely tool. Consider activating when answering a .NET performance question requires measurement and BenchmarkDotNet may be needed. Covers microbenchmark design, BDN configuration and project setup, how to run BDN microbenchmarks efficiently and effectively, and using BDN for side-by-side performance comparisons. Do NOT use for profiling/tracing .NET code (dotnet-trace, PerfView), production telemetry, or load/stress testing (Crank, k6).
Benchmark compensation against market data. Trigger with "what should we pay", "comp benchmark", "market rate for", "salary range for", "is this offer competitive", or when the user needs help evaluating or setting compensation levels.
Guides benchmarking and comparing explicit multi-statement transactions versus single-statement CTE transactions in CockroachDB, with fair test methodology, contention analysis, and performance interpretation. Use when comparing transaction formulations, benchmarking CockroachDB workloads under contention, investigating retry pressure, or deciding whether to rewrite multi-step application flows into single SQL statements.
Analyze compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay a [role]", "is this offer competitive", "model this equity grant", or when uploading comp data to find outliers and retention risks.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
JVM performance tuning - GC optimization, profiling, memory analysis, benchmarking
Profile application performance, identify bottlenecks, and optimize hot paths using CPU profiling, flame graphs, and benchmarking. Use when investigating performance issues or optimizing critical code paths.
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.
Advanced test optimization with cargo-nextest, property testing, and performance benchmarking. Use when optimizing test execution speed, implementing property-based tests, or analyzing test performance.
Run cross-framework agent comparisons using evaluatorq from orqkit — compares any combination of agents (orq.ai, LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK) head-to-head on the same dataset with LLM-as-a-judge scoring. Use when comparing agents, benchmarking, or wanting side-by-side evaluation. Do NOT use when comparing only orq.ai configurations with no external agents (use run-experiment instead).