「benchmark」の検索結果

Use when the user wants help with academic papers or citations but it's unclear which specific workflow fits — reviewing a paper, checking a BibTeX file for fake references, or benchmarking multiple LLMs on reference-recommendation accuracy. Also use when the user mentions paper review, peer review, BibTeX verification, citation checking, reference hallucination, or academic literature accuracy and hasn't specified which of those three tasks they mean. This skill is the entry router for the academic-eval suite: it asks one diagnostic question then routes to the right sub-skill.

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Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/ language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.

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Expert CIS Controls v8 (CIS Top 18) advisor — implementation group scoping (IG1/IG2/IG3), control gap assessments, safeguard-level guidance, asset inventory, software inventory, data protection, secure configuration, account management, access control, continuous vulnerability management, audit log management, email and web browser protections, malware defenses, network infrastructure management, network monitoring and defense, application software security, incident response, penetration testing, and CIS Controls mapping to NIST CSF, ISO 27001, SOC 2, and CMMC. Use for any question about CIS Controls, CIS Benchmarks, Implementation Groups, or prioritized cyber hygiene for any organization size.

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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.

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granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.

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。

Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.

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Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline injection.

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Amazon competitor monitoring and competitive intelligence for sellers. Track pricing changes, inventory levels, new product launches, review velocity, and advertising patterns for competitor ASINs. Set up monitoring alerts for price drops, stock-outs, and market changes. Works on 12 Amazon marketplaces. No API key required. Use when: (1) monitoring competitor pricing strategies, (2) tracking competitor inventory and stock patterns, (3) detecting new product launches in your category, (4) analyzing competitor review velocity and ratings, (5) identifying market opportunities from competitor gaps, (6) setting up competitive alerts and benchmarks.

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Analyze how competitors present their brand — identity, messaging, positioning, voice, and visual style — to find gaps and opportunities. Use when the user says "competitor brand analysis", "analyze competitor brands", "how do competitors position themselves", "competitor messaging", "competitor identity", "what are competitors saying", "benchmark competitors", "competitive brand landscape", "how do we compare to competitors", "competitive audit", or wants to understand the brand landscape before defining their own position.

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Fetch website traffic estimates (monthly visits, traffic sources, top countries, keywords, engagement, ranks) for any domain from SimilarWeb. Use when the user asks about a domain's traffic, monthly visits, traffic sources, audience countries, or wants to compare/benchmark sites against competitors.

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Build an interactive competitive-traffic report for any company and its rivals — monthly visits (SimilarWeb), organic search traffic and Domain Rating (Ahrefs) — as one self-contained HTML page with ranked charts, a three-month trend, a multi-year overlay and a data table. Use when asked to benchmark a product against competitors, size up a market, answer "how much traffic does X get compared to Y", build a competitive landscape, or produce a traffic/authority comparison deck or page.

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。

Scan social platforms for top-performing content by engagement before you create anything. Use this skill when the user wants to see what content is winning in a niche, find viral content patterns, research what's working on YouTube/TikTok/X/Reddit, benchmark engagement, discover content gaps, or says "what content is working for [topic]", "show me top performing content about [keyword]", "what's trending in [niche]", "find viral content about [product]", "content research for [keyword]", "what gets views in [niche]", "engagement analysis for [topic]", "scout the competition", "what videos are getting the most views about [keyword]", "social listening for [topic]", "trending content in [niche]", "top content analysis", "what hooks work for [keyword]", "content intelligence", "find winning formats".

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 the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".

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 marketing numbers look bad and the user needs to know WHY — layered root-cause diagnosis across tracking, delivery, creative, landing page, offer, and audience quality, 5-Whys, regional benchmarks for US, EU, SEA, and LATAM, and a 48-hour action plan. Covers dropshipping ROAS and BE-ROAS. Trigger on 'why is CPA so high', 'my ads are not converting', 'ROAS dropped', 'we are burning budget', 'performance is bad this month', 'diagnose the funnel'. Also use when the user pastes a screenshot of numbers with no question attached. Not for — auditing account configuration and scoring health, see `21-ads-audit-global`; writing a report someone else reads, see `07-marketing-report-global`; turning a raw export into insight, see `13-data-analysis-global`.

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 an ad account needs a systematic CONFIGURATION audit — 84 checkpoints across account, campaign, ad set, ad, tracking, and optimization, scored into a Health Score out of 100, quick wins ranked by impact, regional benchmarks, and a dropshipping checklist, covering Meta, Google, and TikTok. Trigger on 'ads audit', 'audit my ad account', 'is my Meta setup correct', 'health score for the account', 'review my Google Ads', 'we inherited this account and it is a mess'. Not for — diagnosing why the numbers moved this week, see `03-performance-eval-global`; planning fresh budget, see `54-media-plan-global`; rebuilding the campaign hierarchy, see `52-account-structure-global`.

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, update 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。

Use the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.

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Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

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Archived DESIGN.md impact experiment compatibility gate. Use when an old Lab #01 run is requested, but do not generate new legacy 9/12-section documents; direct new experiments to the Core v2 benchmark track.

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Mobil uygulama paywall strateji rehberi. 14 kategori benchmark database, 4 paywall modeli, trial optimizasyonu, placement mapping, pricing psychology, regional pricing (PPP) ve Apple/Google compliance checklist.

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。

Competitive intelligence - extract features, pricing, tech stack from competitor sites

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。

Go testing patterns including table-driven tests, subtests, benchmarks, fuzzing, and test coverage. Follows TDD methodology with idiomatic Go practices.

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。

Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.

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。