「benchmark」の検索結果

Scrape job listings from Indeed.com by keyword, location, and country. Returns job title, company, salary, rating, description, benefits, and apply links. Use when user mentions Indeed, Indeed scraper, Indeed jobs, scrape Indeed, job search Indeed, Indeed job listings, extract Indeed data, Indeed job data, get jobs from Indeed, Indeed employment data, job market research Indeed, Indeed salary data, bulk job extraction Indeed, Indeed job scraper, monitor Indeed listings, Indeed hiring data, job postings Indeed, Indeed career search. Also applies to: job market analysis, salary benchmarking from Indeed, competitor hiring monitoring, recruitment data collection, building job databases from Indeed search 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。

Etsy category page scraper: given an Etsy category URL (e.g. https://www.etsy.com/c/jewelry) and optional page number, returns paginated product listings with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge from category and subcategory pages. Use when user mentions Etsy category, Etsy c/ URL, etsy.com category, browse Etsy categories, scrape Etsy category, Etsy category listing, extract Etsy category products, Etsy subcategory, Etsy jewelry, Etsy clothing, Etsy home decor, Etsy wedding, Etsy accessories, Etsy craft supplies, Etsy vintage category, Etsy category page scraper, Etsy taxonomy, get all products from an Etsy category, bulk category export from Etsy, Etsy niche scraping. Also applies to trend research within a specific Etsy category, competitor benchmarking within a category, sourcing suppliers by category, seasonal product monitoring, and paginated bulk collection driven by a category URL.

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。

Etsy shop catalog scraper: given an Etsy shop URL (e.g. https://www.etsy.com/shop/{shop-name}) and optional page number, returns paginated product listings from that shop's own storefront with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge. Use when user mentions Etsy shop, Etsy seller, Etsy store, etsy.com/shop, scrape Etsy shop, extract all products from Etsy shop, Etsy shop catalog, Etsy seller catalog, list all Etsy shop items, Etsy storefront scraper, Etsy vendor products, Etsy competitor shop, Etsy shop monitoring, get inventory of an Etsy shop, dump Etsy shop products, Etsy seller product export, seller product benchmarking, Etsy shop bulk export. Also applies to competitor shop tracking, supplier catalog collection, price monitoring for a specific shop, new-item detection on a shop, and any paginated bulk collection driven by a single Etsy shop URL.

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。

Walmart product reviews scraper: given a walmart.com product item ID, navigate to the reviews page and extract paginated customer reviews including reviewId, rating, title, review text, author nickname, submission date, verified purchase status, helpful votes, variant selected (color/size), badges, fulfilled by, seller name, and photo count. Use when user mentions walmart reviews, walmart product reviews, walmart customer reviews, scrape walmart reviews, extract walmart reviews, walmart review scraper, walmart ratings and reviews, walmart review data, walmart review text, walmart review pagination, get reviews from walmart, walmart review collector, walmart verified purchase reviews, walmart review analysis, walmart sentiment analysis, walmart review export, walmart buyer feedback. Also applies to bulk collection of walmart product reviews across multiple items, sentiment analysis on walmart reviews for market research, competitor product review benchmarking on walmart, and monitoring new walmart reviews over time.

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, revise, evaluate, publish, and improve Open-Science Skills through the native JavaScript host.skills composer. Use when the user wants a reusable workflow, an existing Skill changed, test cases or benchmarks for a Skill, or better Skill triggering.

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。

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

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。

Stand up and run a LinkedIn employee advocacy program for a marketing or sales team. Covers 14-day launch playbook, brand-guideline governance, per-post time budget, cadence benchmarks, and team ROI (reach, engagement, pipeline). Triggers on "employee advocacy", "get the team posting", "scale LinkedIn across team", "advocacy ROI". Not for planning one person's own calendar (use linkedin-content-planner).

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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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。

Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.

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 performance optimization: research, PoC, benchmark, implement, review, PR

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 an explicitly requested Zephyr control/treatment benchmark on the same pre-normalized sample and compare Finelog stage 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。

Audit the developer experience of a product, SDK, docs site, or SKILL.md by dropping multiple Claude subagents at it with only a tiny task prompt and real tools (WebFetch, Bash, Write). Agents must discover the docs themselves, install deps, ask for credentials if needed, and attempt real execution. The skill captures each agent's trace — tool calls, retries, wall time, errors — and scores on Setup Friction, Speed, Efficiency, Error Recovery, and Doc Quality, then emits an HTML report with an A–F grade and concrete fixes. Use when the user asks to audit agent experience, test a skill, audit docs for agents, check if a SDK is agent-friendly, validate a SKILL.md, measure agent DX, or benchmark how painful onboarding is for an AI agent. Triggers: 'audit agent experience', 'test this skill', 'audit docs for agents', 'is my SDK agent-friendly', 'run a DX audit', 'agent experience test', 'test my docs', 'how do agents do with my product'.

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。

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

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。

Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.

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。

Compare current benchmark results against historical baselines to spot performance regressions. Use as an explicit/manual helper for build-to-build degradation review, not for broad optimization strategy or low-level profiling ownership.

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 or update GoClaw agent skills with eval-driven iteration. Use for new skills, skill scripts, references, benchmark optimization, description optimization, eval testing, extending agent capabilities.

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。

Data-backed email marketing skill for AI agents. Use when building or running email automation, driving an ESP from an agent (MCP/connectors), diagnosing deliverability, writing or de-slopping email copy, directing AI email design, choosing a platform, or pulling benchmarks. Covers flows, segmentation, compliance, cold email, WhatsApp, SMS and RCS, and 19 industry playbooks.

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。

Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize, evaluate, or compose these into a pipeline. Do NOT use for frontend/dashboard/visualization work, generic ML advice, billing/access, or non-Nemotron coding 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。

Diagnose failed or unhealthy Dynamo deployments. Use when pods, model-cache jobs, PVCs, workers, frontend/router health, endpoints, or benchmark jobs fail; use recipe-runner/router-starter before this for normal bring-up.

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 for non-failing HoloHub app work with ./holohub: scaffold, build, run, test, visual evidence, lint, and flow 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。

Use this skill when the operator is authoring, building, loading, or debugging a custom doca-bench plug-in — a versioned shared library with DOCA_EXPERIMENTAL-marked C entry points that doca-bench loads to measure a workload class its built-in modes do not cover, with doca_bench_cuda as the shipped reference exemplar. Trigger even when the user does not say "doca-bench-extension" or "doca_bench_cuda" — typical implicit phrasings include "no built-in doca-bench mode fits my workload", "how do I benchmark a CUDA GPUNetIO RX/TX kernel", "doca-bench cannot find or load my custom .so", "extension exported symbols do not match what the parent expects", "soversion mismatch after a DOCA upgrade", or "my GPU kernel hangs because stop_flag was never set". Refuse and route elsewhere for questions about which built-in doca-bench mode to pick, DOCA GPUNetIO programming semantics, CUDA toolkit installation, or contributor work on in-tree extensions — those belong to other skills.

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 is measuring the host or DPU-CPU control-plane rate of a DOCA Flow pipeline with doca_flow_perf — picking a JSON policy from configs/, choosing the DPDK or DOCA backend, running the single-iteration smoke then the iterative eval loop, interpreting per-iteration CPU cycles and num_pushed / num_failed, or capturing the four-tuple (DOCA version, BlueField/firmware, JSON policy, worker/queue/burst config) that makes a Kops/sec number defensible. Trigger even when the user does not explicitly mention "doca-flow-perf" — typical implicit phrasings include "how many rules per second can my BlueField insert", "5-tuple hairpin rule rate", "Kops/sec for steering", "flow-perf number does not match release notes", "DPDK vs DOCA benchmark", or "rule-install variance too high". Refuse and route elsewhere for optimizing a live Flow app (doca-flow-tune), the DPA-offloaded path (doca-flow-dpa-perf), dataplane throughput or latency, or library-internal pipe semantics — those belong to other skills.

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 is building, running, or interpreting the doca/tools/gpunetio_ib_write_bw client+server benchmark — a CUDA kernel on the client posts RDMA WRITE work requests through the doca-gpunetio device-side surface to measure sustained GPU-driven WRITE bandwidth on a GPU+IB-device pair. Trigger even when the user does not explicitly mention "doca-gpunetio-ib-write-bw" or "GPUNetIO" — typical implicit phrasings include "measure WRITE BW when the GPU posts the WRs", "BW swings between runs on the same flags", "is the NIC saturated or am I CPU-bound on the CUDA kernel", "meson compile fails for the GPUNetIO bw tool", "nvidia_peermem isn't picking up my GPU buffer", or "GPU-initiated WRITE throughput vs CPU-initiated perftest". Refuse and route elsewhere for general doca-gpunetio library work, DOCA install, the GPU-initiated WRITE latency analog, the CPU-initiated upstream perftest, or application-level end-to-end throughput — those belong to other skills.

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。