「benchmark」の検索結果

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).

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。

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).

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。

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).

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 model validation and benchmarking. Investigates any ML model (LLM, image gen, TTS, time series, etc.), runs it on GPU cloud, evaluates quality and performance, and generates HTML reports. Use when user asks to verify, benchmark, evaluate, or test a model. Triggers on "verify model", "benchmark", "evaluate model", "test model", "run benchmark", "model evaluation", "モデルを検証", "ベンチマーク", "モデルを試して".

Phase 2B of the Gaia evidence verification pipeline. Verifies benchmark-result rows and benchmark-source candidates against registry/benchmark-sources.json before adversarial audit, link validation, catalog promotion, or ingestion. Use when running /ev-pipeline, checking benchmark-result evidence, reviewing benchmark-source candidates, or preparing benchmark rows for /gaia-ingest-batch.

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。

Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling only.

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。

Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report - use performing-kubernetes-cis-benchmark-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。

Webページの表示や操作、APIの応答、ビルド・テストの所要時間を計測するスキル。変更前後の結果を比較し、Gitで共有する基準値をもとに性能の悪化を確認します。

• コード変更前後の性能比較
• ページが遅いという報告の調査
• 公開前に性能目標を確認したいとき

自社の強みや顧客像を基に、比較すべき競合と調査先を選びます。直接競合・周辺競合・目標となる企業に分類し、根拠付きの候補一覧を作って詳しい比較の準備を整えます。

• 競合比較の対象を決めたいとき
• 直接競合・周辺競合・目標企業の分類
• 市場調査レポートの候補を絞りたいとき

Operate or analyze LoopX-managed benchmark experiments through benchmark-toolkit, including trajectory-based effectiveness and efficiency diagnosis, matched comparisons, run selection and integrity. Solving an assigned task under an existing runner does not by itself select this skill. Excludes casual benchmark discussion and ordinary software microbenchmarks.

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。

dontbesilent 对标分析。用五重过滤法帮你找到值得模仿的对标，排除一切关于「我」的噪音。 触发方式：/dbs-benchmark、/对标、「帮我找对标」「我该模仿谁」 Benchmark analysis using dontbesilent's five-filter method. Trigger: /dbs-benchmark, "find me a benchmark", "who should I copy"

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。

dontbesilent 对标分析。用五重过滤法帮你找到值得模仿的对标，排除一切关于「我」的噪音。 触发方式：/dbs-benchmark、/对标、「帮我找对标」「我该模仿谁」 Benchmark analysis using dontbesilent's five-filter method. Trigger: /dbs-benchmark, "find me a benchmark", "who should I copy"

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。

MindIE-SD 核心算子（FA/BSA/GMM/MM）性能基准工具链。使用：模型优化（model-auto-optimization 的 S1/S4 选型）中用 mindie_bench 对单算子做实现级实测，按 dtype/量化档/稀疏度/形态对比 选出最优配置（产物：选型证据；稀疏度-性能曲线供 S4 稀疏度选型）；开发：benchmarks/ 工具链扩展与新算子接入测试（供 operator-dev / pattern-dev 调用）。 当用户需要对比算子实现选型、新增或修改 benchmark 代码、排查 benchmark 数据异常、 给基准加算子/指标时使用；即使用户只说"benchmark 数据不对""给基准加个算子" "对比下这几个 FA 实现哪个快"也应触发；特性级方案选档请走 dit-perf-opt （本 skill 只做实现级实测）。 由 model-auto-optimization 的 S1/S4 选型场景与 operator-dev 的算子接入验证场景调用， 亦由 dev-workflow 在基准开发时指引加载。

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。

Audit AWS, Azure, and GCP environments against the CIS Foundations Benchmarks by running automated scans with tools like Prowler and ScoutSuite, interpreting failed controls, and tracking remediation for continuous compliance. Use when conducting a cloud security audit, validating CIS benchmark compliance (CIS v5 AWS, v4 Azure, v4 GCP), or setting up continuous cloud compliance monitoring.

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。

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。

Competitor research and intelligence skill. Takes a user's company (with optional seed competitor URLs), auto-discovers additional competitors via Browserbase Search API, deeply researches each using a 4-lane pattern (marketing surface, external signal, public benchmarks, strategic diff vs the user's company), and compiles the results into an HTML report with four views: overview, per-competitor deep dive, side-by-side feature/pricing matrix, and a chronological mentions feed (news, reviews, social, comparison pages, and public benchmarks). Use when the user wants to: (1) analyze competitors, (2) build a competitive matrix, (3) extract competitor pricing / features, (4) find comparison pages and online mentions of competitors, (5) surface public benchmarks. Triggers: "competitor analysis", "analyze competitors", "competitive intel", "competitor research", "competitor pricing", "feature comparison", "price comparison", "find comparisons", "who's comparing us", "competitor mentions", "competitor 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。

Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while `samber/cc-skills-golang@golang-performance` provides the optimization patterns.

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 the user hands over a dsh plugin repository (or a real migration commit / version corridor) and wants its upgrade experience extracted into one auto-graded Harbor benchmark exam task — produce tasks/<id>/ (fixture + instruction.md + task.toml + judge.mjs + solution) that harbor run can grade 0–1 and that folds into the dsh-plugin-upgrade-skill benchmark suite; also applies to turning existing version cards (references/v*.md) into executable exam tasks. Not for writing upgrade cards; not for running the benchmark.

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。

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。

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。