「benchmarking」の検索結果

Benchmark franchise locations by comparing top and bottom quartile performance across revenue, profitability, and operational scores.

日本語の概要は準備中です。原文の説明を表示しています。

Build data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Explore open-source GitHub repository source trees via web browsing to analyze and compare feature implementations at the code level. Supports two modes: cross-project comparison and single-project deep dive. Use when evaluating how OSS projects implement a specific feature, choosing architecture patterns, or benchmarking implementation strategies.

日本語の概要は準備中です。原文の説明を表示しています。

Avalia modelos de geração de código em HumanEval, MBPP, MultiPL-E e 15+ benchmarks com métricas pass@k. Use ao comparar modelos de código, avaliar capacidades de codificação, testar suporte multilíngue ou medir qualidade de geração de código. Padrão da indústria do BigCode Project usado pelos leaderboards do HuggingFace.

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Testes e benchmarking de agentes LLM incluindo testes comportamentais, avaliação de capacidades, métricas de confiabilidade e monitoramento em produção—onde até os melhores agentes alcançam menos de 50% em benchmarks do mundo real. Use quando: testes de agentes, avaliação de agentes, benchmark de agentes, confiabilidade de agentes, teste de agentes.

日本語の概要は準備中です。原文の説明を表示しています。

Run CIS Kubernetes Benchmark checks and remediate findings with kube-bench.

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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.

日本語の概要は準備中です。原文の説明を表示しています。

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...

日本語の概要は準備中です。原文の説明を表示しています。

Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Investigate model specifications, requirements, and evaluation strategy. Use when researching a model before benchmarking: reading HuggingFace model cards, estimating VRAM requirements, selecting GPU providers, and determining evaluation approach. Triggers on "model research", "investigate model", "model info", "VRAM estimate", "which provider", "model card".

日本語の概要は準備中です。原文の説明を表示しています。

Design and run reproducible benchmarks. Use when measuring performance scaling, comparing branches, or analyzing speedup metrics.

日本語の概要は準備中です。原文の説明を表示しています。

Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.

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Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to add or configure Maven plugins in your pom.xml — including quality tools (enforcer, surefire, failsafe, jacoco, pitest, spotbugs, pmd), security scanning (OWASP), code formatting (Spotless), version management, container image build (Jib), build information tracking, and benchmarking (JMH) — through a consultative, modular step-by-step approach that only adds what you actually need. This should trigger for requests such as Add Maven plugins in pom.xml; Improve Maven plugins in pom.xml. Part of cursor-rules-java project

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Guides Rust performance optimization. Use when profiling, benchmarking, reducing allocations, improving cache locality, choosing between rayon/async/threads, or applying SIMD/parallelism.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when the user asks about trucking rate negotiation — cost-plus pricing, market rate benchmarking via DAT/Truckstop, fuel surcharge math, how to negotiate with brokers/shippers, when to walk away, contract pricing vs spot rates. Reference DAT Rate Analytics + freight market reports.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

日本語の概要は準備中です。原文の説明を表示しています。

Apply systematic performance optimization techniques when writing or reviewing code. Use when optimizing hot paths, reducing latency, improving throughput, fixing performance regressions, or when the user mentions performance, optimization, speed, latency, throughput, profiling, or benchmarking.

日本語の概要は準備中です。原文の説明を表示しています。

Gates CI on perf regressions. Automated threshold alerts, baseline tracking, trend reports.

日本語の概要は準備中です。原文の説明を表示しています。