'benchmark' 검색 결과

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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Add a new benchmark to sgl-eval by vendoring its NeMo-Skills dataset module and registering it in `_TABLE`. Use when the user asks to "add <benchmark>" / "support <benchmark>" / "register <benchmark>" inside the sgl-eval repo.

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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。

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Measure web page loading times, rendering milestones, and resource sizes in a real browser. Save baseline measurements, compare results before and after code changes, and inspect historical trends to spot performance regressions.

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• Saving performance baselines before changes
• Checking page speed after a code change
• Finding slow resources and large transfers

Guide GAIA benchmark runs from environment checks and cost estimates to result comparison. Create a signed submission package compatible with HAL, including agent traces, metadata, and an audit report that checks for benchmark exploitation.

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• Check readiness for a GAIA benchmark run
• Estimate costs and run an evaluation
• Package existing results for submission

Compare saved benchmark runs over time to track win rate, response times, escalation rate, and LLM baseline costs. Review changes between the first and latest runs, with flags for declining win rates or rising average latency.

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• Check performance trends before a release
• Review benchmark history after expanding the test corpus
• Track latency changes after an agent-booster upgrade

Measure performance in the agentic-flow worker system, including trigger detection, agent selection, caching, and concurrent operations. Run a full suite or individual benchmarks to compare response times and resource usage against configured targets.

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• Checking overall worker system performance
• Measuring trigger detection and agent selection speed
• Examining cache behavior and memory usage

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

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• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.

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• Recording agent evaluation results in a shared format
• Comparing a candidate agent with a baseline
• Tracking actual cost per solved task

Build an Excel comparison of public companies using financial performance, valuation ratios, and statistical benchmarks. The spreadsheet includes formulas, source notes, medians, and quartiles to show how each company compares with its peers.

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• Comparing public company valuations for M&A
• Benchmarking performance against industry peers
• Identifying unusually high or low valuation ratios

Design tests that assess an AI agent's capabilities and reliability. Covers repeated runs to measure variation, checks for required behavior, and adversarial testing to expose failures that ordinary successful examples may miss.

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• Measuring variation across repeated runs of the same test
• Checking whether an agent follows required behavior
• Testing responses to unexpected or adversarial inputs

Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.

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• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.

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• Compare coding or reasoning models by benchmark scores
• Find model candidates for a laptop's available memory
• Choose models for OCR or speech recognition

Quickly look up SaaS financial metrics, including recurring revenue and customer acquisition costs. Get definitions, formulas, benchmarks, and warning signs, plus reminders of which metrics matter when reviewing features, pricing, acquisition channels, or business health.

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• Looking up NRR formulas and benchmarks
• Preparing a SaaS metrics sheet for a business review
• Checking metrics before expanding an acquisition channel

Evaluate local Codex skills and plugins, understand their scores, and identify fixes to prioritize. Get explanations of token estimates, set up benchmark scenarios, and prepare workflows for measuring actual token usage.

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• Evaluate a local skill or plugin
• Understand scores and prioritize fixes
• Explain token estimates and prepare actual measurements

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".

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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.

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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.

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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.

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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.

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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.

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

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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 benchmarksUse when "agent testing, agent evaluation, benchmark agents, agent reliability, test agent, testing, evaluation, benchmark, agents, reliability, quality" mentioned.

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