Results for “performance-benchmarking”
12 skills
anthropic Skill Creator
Create new skills, modify existing ones, and measure their performance through iterative evaluation and benchmarking.
158k · bundle
nvidia RAG Perf
Run config-driven performance benchmarks against a deployed NVIDIA RAG Blueprint server, including profiling and load testing, with a unified report.
2.2k · bundle
More results
aniruddhaadak80 Model Benchmark
Benchmark LLM performance across tasks — latency, quality, cost comparison.
0
machenjie Performance Budgeting
`analysis-agent`/`task-agent`/`review-agent`: use when latency, throughput, bundle, memory, CPU, query, rendering, or resource cost needs a budget; skip without performance risk.
4 · bundle
machenjie Profiling
`task-agent`/`review-agent`: use when CPU, memory, I/O, database, network, rendering, or cost needs measured bottleneck evidence; skip without a profiling need.
4 · bundle
livelybug Benchmark Models
Cross-model benchmark for gstack skills. (gstack)
0
b4san Performance Optimizer
Transform the agent into a performance engineer. Apply methodologies for measuring, profiling, and optimizing code (caching, algorithm complexity, resource usage).
2
nvidia RAG Eval
Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.
2.2k · bundle
whd4 Agent Evaluation
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
0
lucassantana-dev Eval
Evaluate LLM outputs systematically — benchmarks, automated metrics, human preference, and regression tracking
1 · bundle
github Arize Experiment
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance using the ax CLI.
36.2k · bundle
kintsugi-programmer LLM Evaluation
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.
0