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Design, configure, run, and review BenchmarkDotNet measurements of small .NET operations. Compare implementations, runtime versions, or input sizes, while controlling setup work, measurement errors, and case counts so results support a concrete performance decision.

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• Compare the performance of alternative implementations
• Reproduce a reported performance problem
• Measure performance before and after a code change

Carry out Benchmark Email tasks through Rube MCP, which links an AI agent with external tools. The skill discovers supported actions and their input formats, confirms an active connection, and executes the requested operations.

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• Finding supported Benchmark Email actions
• Checking the connection before running a task
• Batching multiple Benchmark Email operations

Carry out Benchmark Email tasks through Rube MCP, which links an AI agent with external tools. The skill discovers supported actions and their input formats, confirms an active connection, and executes the requested operations.

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• Finding supported Benchmark Email actions
• Checking the connection before running a task
• Batching multiple Benchmark Email operations

Guides LoopX benchmark experiments through benchmark-toolkit, covering run management, comparisons under matched conditions, and analysis of agent action histories. It helps explain outcomes and efficiency while tracking source versions, score validity, and evidence boundaries.

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• Planning and managing benchmark runs
• Comparing baselines and treatments under matched conditions
• Diagnosing outcomes and efficiency from agent action histories

Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.

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• Setting up kube-bench for the first time
• Selecting benchmark versions and scan targets
• Exporting JSON or JUnit results for CI

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).

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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).

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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.

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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.

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用文字条形图展示Ponytail 5公开基准测试中的代码量、输出词元、费用、时间及检查结果，对比未使用技能的情况，不计算当前项目的节省量。

• 查看公开的代码量与费用对比
• 查看正确性和测试相关测量结果
• 同时查看测量条件与结果

Audit AWS, Azure, and GCP configurations against CIS Benchmarks, security configuration guidelines. Use automated scan results to identify failed checks, prioritize fixes, report compliance by area, and establish ongoing monitoring for configuration changes.

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• Running an initial cloud security configuration audit
• Preparing CIS-related evidence for SOC 2 or ISO 27001 audits
• Prioritizing fixes for failed security checks

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

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• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.

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• Add SQL benchmarks for DataFusion operators
• Select representative input sizes and filter conditions
• Reduce unrelated processing overhead in measurements

测量网页加载与交互、API响应以及构建和测试的耗时，对比修改前后的结果，并以通过Git共享的基准值检查性能是否下降。

• 比较代码修改前后的性能
• 调查页面缓慢的反馈
• 发布前检查性能目标

根据公司的服务、目标客户和差异化依据，选择比较对象与调研来源，将候选企业分为直接竞争者、相邻竞争者和标杆企业，形成附分类及来源链接的名单。

• 确定需要深入比较的竞争者
• 划分直接、相邻竞争者和标杆企业
• 缩小市场调研报告的候选范围

以文字条形图展示ponytail公开基准测试中的代码量、输出词元、费用和耗时降幅，同时列出质量指标，并与当前任务的实测结果区分。

• 查看代码量和费用的降幅
• 比较运行时间与输出词元
• 同时查看降幅和质量指标

Benchmark distributed consensus protocols, methods computers use to agree on shared results. Compare processing rates, response times, scaling behavior, and resource usage across Byzantine, Raft, and Gossip, then identify bottlenecks and potential tuning changes.

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• Comparing Byzantine, Raft, and Gossip performance
• Measuring response times under increasing load
• Finding CPU, memory, or network bottlenecks

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

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• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

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• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

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• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

Run MNN checks and regression tests on a host computer or Android device, with stage summaries and failure logs. A separate workflow benchmarks LLM prompt processing and token generation on real iPhones and iPads, including branch comparisons.

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• Run static checks and host regression tests
• Verify selected backends on an Android device
• Benchmark LLM speed and compare branches on iPhone or iPad

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

中文简介正在准备中。当前显示英语简介。

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

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• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

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"

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