「terms」の検索結果

Assess obligations under Regulation (EU) 2023/2854 for connected products, related services, statutory B2B data sharing, unfair data-contract terms, exceptional-need B2G requests, data-processing-service switching, international governmental access, interoperability, smart contracts, enforcement, and non-EU representatives. Use for Data Act scope analysis, gap assessments, product and service design, request handling, contract review, cloud exit planning, or cross-regulation mapping.

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EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.

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Connect new gene expression profile with the relevant information from the existing databases, such as previous publications, MeSH terms, and gene sets.

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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

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This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enric

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Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and '

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Turn drafted legal docs (privacy policy, terms) into hosted pages and set the App Store Connect Privacy Policy / Support / Marketing URLs via the ASC REST API. Use at Phase 6 / submission, after legal drafts exist. The App Privacy "nutrition label" stays manual (Apple exposes no API) — this prints the exact answers to click.

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Generate privacy policies, terms of service, and EULAs for Apple platform apps. Detects data collection patterns, third-party SDKs, and generates region-specific legal documents with Apple Privacy Nutrition Label mapping. Use when user needs legal documents or data collection disclosure for App Store submission.

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Legal document generation and compliance guidance for indie Apple developers. Covers privacy policies, terms of service, EULAs, GDPR/CCPA/DPDP compliance, and Apple App Store legal requirements. Use when user needs legal documents or compliance guidance.

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Amazon backend search term optimization and strategy. 250-byte limit management, keyword deduplication, prioritization frameworks, and search visibility optimization. Use when the user asks about backend keywords, search terms, Amazon SEO, or keyword optimization.

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Use when briefing or managing an EXTERNAL agency, freelancer, or production vendor — scope of work, deliverable specs, revision rounds, approval process, USD payment milestones, IP assignment and confidentiality terms, communication rules, and a post-project vendor scorecard. Trigger on 'brief an agency', 'hire a freelancer', 'scope of work', 'the agency is not delivering', 'onboard a new vendor', 'we keep paying for work we have to redo'. Also use when the user is about to sign a supplier with nothing written down. Not for — briefing an internal team member, see `64-team-brief-global`; the campaign brief itself, see `02-campaign-brief-global`; the asset list the vendor produces, see `41-campaign-asset-list-global`.

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日本サービス向け（일본 서비스용） プライバシーポリシー・利用規約・同意モーダル・Cookieバナー 자동 생성. 個人情報保護法(APPI)·消費者契約法·特定商取引法 반영. Next.js 13~16 프로젝트 대상.

한국 서비스용 처리방침·이용약관·회원가입 동의 모달·쿠키 배너 자동 생성. 개인정보보호법 §30, 2025.4.21 작성지침, 2026.3 개정법, 공정위 전자상거래 표준약관 10023호 반영. Next.js 13~16 프로젝트 대상.

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미국 CCPA/CPRA + 주요 주법(VCDPA·CPA·CTDPA·UCPA·ICDPA·KCDPA·RIDPA) 기반 Privacy Policy 자동 생성. 2026.1.1 CPPA 갱신 규정, Sensitive Personal Information, Do Not Sell/Share, ADMT 공개, GPC 브라우저 신호 대응. 캘리포니아 거주자 서비스·100K records 초과 서비스 대상.

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한국+EU 병기 처리방침·이용약관 자동 생성. 한국 본사가 EU 사용자까지 대상으로 서비스할 때 사용. 한국어(PIPA)·영문(GDPR) 두 세트 문서를 동시 생성하고, Footer에 언어·관할 전환 링크 자동 삽입.

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한국+일본 병기 처리방침·이용약관 자동 생성. 한국 본사가 일본 사용자까지 대상으로 서비스할 때 사용. 한국어(PIPA)·일본어(APPI) 두 세트 문서를 동시 생성하고, Footer에 언어·관할 전환 링크 자동 삽입.

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。

Identify negotiable contract terms and draft options with commercial tradeoffs and legal review questions. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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Scans lyrics for pronunciation risks and prevents Suno mispronunciations. Use when writing lyrics with proper nouns, technical terms, homographs, or non-English words.

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。

Normalize terminology across a draft (canonical terms + synonym policy) without changing citations or meaning.

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Settle what domain terms mean per context, and which repository conventions or language standards (Go, Python) apply. Use when: names disagree or a rename is proposed.

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Trigger for: OT project description, OTA scope, Research OT under 10 U.S.C. 4021, Prototype OT under 10 U.S.C. 4022, follow-on production scope under 10 U.S.C. 4022(f), milestone-based project scope, prototype objective, phase or go/no-go structure, BAA white-paper conversion, SOO conversion, transition planning, or scope reduction before OT cost analysis. Produce a milestone-based .docx plus a separate chat-only handoff to OT Cost Analysis. Use TRL only when the project supports it. Never place cost estimates, funding profiles, contribution calculations, prices, or skill-routing content in the document. Do NOT use for a FAR SOW/PWS, OT cost analysis, IGCE, agreement terms and conditions, or an Agreements Officer's authority, participant-status, successful-completion, or follow-on determination.

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Retrieve mouse phenotype data from the Jackson Laboratory Mouse Phenome Database (MPD) via its REST API. Browse 520+ projects, look up per-project measure metadata, pull strain-level means (raw or LS-mean adjusted) and per-animal values, find measures by MP/VT ontology terms, and resolve strain nomenclature or gene coordinates. Use for QTL support, cross-strain comparison, mouse model selection, and ontology-driven phenotype discovery. Use monarch-database for disease-gene-phenotype knowledge graphs; ensembl-database for mouse genome annotations.

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Query EBI QuickGO REST API for GO terms and protein annotations. Fetch term metadata by ID, search by keyword, walk ancestor/descendant hierarchies, download annotations filtered by taxon, evidence code, aspect. Use for GO resolution, ontology traversal, annotation retrieval before enrichment. Use gseapy-gene-enrichment for enrichment; uniprot-protein-database for proteins.

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smina molecular docking CLI. AutoDock Vina fork with customizable scoring functions, native SDF/MOL2/PDB ligand input, autoboxing, local energy minimization, and per-atom score breakdowns. Pipeline: receptor PDBQT prep -> ligand prep (RDKit/OpenBabel) -> dock via autobox or explicit grid -> rescore/minimize with custom scoring -> rank poses by affinity. Choose smina over Vina when you need custom scoring terms (--custom_scoring), local optimization of an existing pose (--local_only), per-atom contributions (--atom_term_data), or SDF/MOL2 ligands without manual PDBQT conversion. For unknown binding sites use diffdock; for the Python-bindings/Vinardo workflow use autodock-vina-docking.

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。