「evaluation」の検索結果

Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689). Use when asked to determine GPAI model provider obligations, assess Code of Practice compliance, evaluate transparency or copyright requirements for GPAI models, check systemic risk obligations, build a GPAI compliance roadmap, or prepare for AI Office requests. Covers all three Code chapters (Transparency, Copyright, Safety & Security), the 12 commitments and their measures, systemic risk classification (Article 51), provider obligations (Articles 53 and 55), open-source exemptions, enforcement timeline, Commission/AI Office supervision, and DACH-specific overlays such as BSI cybersecurity and works council implications. Triggers on phrases like "GPAI compliance", "Code of Practice assessment", "GPAI model obligations", "systemic risk evaluation", "AI Act transparency requirements", "GPAI copyright policy", "general-purpose AI compliance".

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。

Assess whether a Fundamental Rights Impact Assessment (FRIA) is required under Article 27 EU AI Act, and structure or draft that assessment for a specific high-risk AI deployment. Covers deployer scope gating (public bodies and private entities providing public services), affected group mapping, Charter rights analysis, proportionality, safeguards evaluation, residual risk, DPIA/FRIA cross-referencing under the amended Article 27(4), the unconditional notification duty under Article 27(3), and DACH-specific considerations. Use when asked about FRIA obligations, Article 27 scope, fundamental rights and AI, or deployer assessment duties.

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。

Generate scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent 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。

Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.

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。

Design a critical thinking task targeting specific skills like evaluating evidence, identifying bias, or analysing arguments. Use when embedding critical analysis into subject lessons.

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。

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

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 code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

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。

Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth-assisted relighting, grayscale depth-video conversion, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.

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 wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial selection.

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 SwiftUI performance issues including unnecessary re-renders, view identity problems, and slow body evaluations. Use when SwiftUI views are slow, janky, or re-rendering too often.

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 a structured accessibility audit on an iOS/macOS app — automated XCUITest audits, Accessibility Inspector, manual VoiceOver/Dynamic Type passes, and App Store Accessibility Nutrition Label evaluation. Use before release, when preparing Nutrition Label declarations, or for EU Accessibility Act compliance.

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。

standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.

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。

Analyze website traffic, global rank, engagement metrics, and traffic sources for any domain. Use this skill to evaluate affiliate program websites, compare competitor traffic, assess advertiser strength, or understand where an audience comes from. Triggers on: "analyze traffic for [domain]", "how much traffic does [site] get", "compare traffic between [site A] and [site B]", "is [program] worth promoting based on traffic", "traffic analysis", "website analytics for [domain]", "where does [site] get traffic", "check if [advertiser] is legit", "evaluate [program] website health", "SimilarWeb analysis", "traffic sources for [domain]", "how popular is [site]", "website rank", "domain authority check", "compare affiliate program websites".

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。

Score product remarkability 1-10 to decide if it's worth promoting. Seth Godin's Purple Cow test. Triggers on: "is this product worth promoting", "should I promote", "product audit", "purple cow", "remarkable product", "is it remarkable", "rate this product", "product quality check", "worth my reputation", "product evaluation", "would I recommend without commission", "product remarkability score", "evaluate this affiliate product", "quality gate for promotion".

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。

Catalog of high-value award redemptions where points dramatically outvalue cash. Tiered by legendary/excellent/good with current rates, devaluation history, and booking caveats. Includes Oceania and South Pacific plays (Australia, New Zealand, Tahiti, Fiji) and transatlantic business-class sweet spots.

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。

Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning

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。

Generates 3-5 divergent design directions through JTBD analysis, competitive research, structured brainstorming, and taste evaluation before convergence. Use when the team has a validated problem but hasn't chosen a solution approach.

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。

Structured divergent thinking techniques — HMW framing, SCAMPER, Crazy 8s mechanics, and option diversity guarantees. Enforces strict separation of generation and evaluation phases.

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。

Comprehensive architecture patterns, methodologies, quality frameworks, and evaluation methods for solution architects. Load when designing system architecture or selecting 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。

Evaluation criteria and scoring for data engineering artifact reviews

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 authorized OT/ICS security assessment covering Purdue model zoning, PLC/SCADA exposure, industrial protocol discovery, and safe passive-first evaluation.

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。

Standardizes the creation and evaluation of high-density Agent Skills (Claude, Cursor, Windsurf). Ensures skills achieve high Activation (specificity/completeness) and Implementation (conciseness/actionability) scores. Use when: writing or auditing SKILL.md, improving trigger accuracy, or refactoring skills to reduce redundancy and maximize token ROI.

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。

Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training 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。

Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.

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。