「benchmark」の検索結果

Design AI benchmarks for comparing models and tracking improvement. TRIGGERS - Use when user needs help with ai-benchmark-creator related tasks.

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。

Create benchmark suites for AI model comparison. TRIGGERS - Use when user needs help with ai-benchmark-suite related tasks.

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。

Perform triangulated cognitive research combining SQLite memory, GitHub 10k+ stars patterns, and ToT DAG benchmarking before critical architectural decisions. Automatically triggers subagent research escalation when confidence is low (<0.70). Keywords: research, benchmark, github, memory, triangulation, grounding, best-practices, escalation

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, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.

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。

"Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'.".

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。

Experimental/manual harness canary procedure. It records benchmark manifests and scores but does not provide a fully automated runner.

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。

Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.

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 LLMs em 100+ benchmarks de 18+ harnesses (MMLU, HumanEval, GSM8K, segurança, VLM) com execução multi-backend. Use quando precisar de avaliação escalável em Docker local, HPC Slurm ou plataformas cloud. Plataforma enterprise-grade da NVIDIA com arquitetura container-first para benchmarking reproduzível.

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。

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.

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。

Generate self-contained HTML/CSS benchmark and comparison charts in an ego-inspired Browser-Native Editorial style. Use for blue-white product metrics, model benchmarks, A/B comparisons, efficiency reports, difficulty breakdowns, and compact technical data panels.

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。

Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

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。

Audit Kubernetes cluster security posture against CIS benchmarks using kube-bench with automated checks for control plane, worker nodes, and RBAC.

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 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible 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。

Rigorous methodology for evaluating ML models on established benchmarks. Covers proper train/val/test splits, baseline verification from original papers, exact metric formula discrepancies, data-leak detection checklist, multi-seed robustness, and honest reporting templates. Use when claiming to beat published baselines, writing methods papers, or auditing existing results.

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 code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

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。

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。

Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.

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。

Create benchmark suites for AI model comparison. TRIGGERS - Use when user needs help with ai-benchmark-suite related tasks.

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 AI benchmarks for comparing models and tracking improvement. TRIGGERS - Use when user needs help with ai-benchmark-creator related tasks.

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。

Runs BenchmarkDotNet microbenchmarks. Setup, memory diagnosers, baselines, result analysis.

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。