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Found 147 Skills
Rust profiling skill for performance analysis. Use when generating flamegraphs from Rust binaries, measuring monomorphization bloat with cargo-llvm-lines, analysing binary size with cargo-bloat, microbenchmarking with Criterion, or interpreting inlined frames in profiles. Activates on queries about cargo flamegraph, cargo-bloat, cargo-llvm-lines, Criterion benchmarks, Rust performance profiling, or binary size analysis.
This is a skill for benchmarking the efficiency of automatic prefix caching in vLLM using fixed prompts, real-world datasets, or synthetic prefix/suffix patterns. Use when the user asks to benchmark prefix caching hit rate, caching efficiency, or repeated-prompt performance in vLLM.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
Use this skill when profiling application performance, debugging memory leaks, optimizing latency, benchmarking code, or reducing resource consumption. Triggers on CPU profiling, memory profiling, flame graphs, garbage collection tuning, load testing, P99 latency, throughput optimization, bundle size reduction, and any task requiring performance analysis or optimization.
Use this skill when load testing services, benchmarking API performance, planning capacity, or identifying bottlenecks under stress. Triggers on k6, Artillery, JMeter, load testing, stress testing, soak testing, spike testing, performance benchmarks, throughput testing, and any task requiring load or performance testing.
This skill should be used when profiling code, optimizing bottlenecks, benchmarking, or when "performance", "profiling", "optimization", or "--perf" are mentioned.
High-performance Rust optimization. Profiling, benchmarking, SIMD, memory optimization, and zero-copy techniques. Focuses on measurable improvements with evidence-based optimization.
Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
Set up performance benchmarks and CodSpeed harness for a project. Use this skill whenever the user wants to create benchmarks, add performance tests, set up CodSpeed, configure codspeed.yml, integrate a benchmarking framework (criterion, divan, pytest-benchmark, vitest bench, go test -bench, google benchmark), or when the user says 'add benchmarks', 'set up perf tests', 'create a benchmark', 'benchmark this', or wants to measure performance of their code for the first time. Also trigger when the optimize skill needs benchmarks that don't exist yet.
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).