1,377 件 ・ 関連度順
概要と使いどころ
Generates a formal, structured AI Act compliance assessment report suitable for legal files, audit trails, and regulatory inquiries. This skill should be used when the user asks to "generate an AI Act report", "create a compliance assessment report", "document the AI Act analysis", "create a Prüfbericht", "export as Word document", or wants to consolidate prior AI Act skill outputs into a formal documented assessment.
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 multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.
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。
Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.
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。
Select the right formative assessment technique for a specific learning moment, purpose, and age group. Use when choosing how to check understanding during, between, or after 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。
Conducts retention impact assessments for new processing activities to determine appropriate data retention periods. Covers regulatory requirements scanning, proportionality review, purpose-based retention determination, and retention period documentation aligned with GDPR Article 5(1)(e) and Article 25 data protection by design. Activate for retention assessment, new processing retention, retention period determination queries.
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。
Guides privacy law change monitoring and impact assessment for multi-jurisdiction organisations. Covers regulatory tracking sources, change classification, impact scoring methodology, and implementation prioritisation. Keywords: law monitoring, regulatory tracking, change management, impact assessment, implementation priority.
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。
Links RoPA entries to Data Protection Impact Assessments and lawful basis assessments. Covers cross-reference systems, dependency tracking, and update cascade triggers between RoPA, DPIA register, and lawful basis documentation. Activate for RoPA-DPIA link, cross-reference, dependency tracking, impact assessment linkage, cascade updates.
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。
Guides conducting privacy law gap analysis for market entry into new jurisdictions. Covers target jurisdiction assessment, existing compliance mapping, remediation effort estimation, and implementation timeline planning. Keywords: gap analysis, market entry, jurisdiction assessment, remediation planning, compliance mapping.
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。
Conducts HIPAA risk analysis per 45 CFR §164.308(a)(1) following OCR guidance methodology. Covers threat identification, vulnerability assessment, likelihood and impact determination, risk scoring, and mitigation planning for electronic protected health information. Keywords: HIPAA risk analysis, OCR guidance, threat assessment, vulnerability, risk management, ePHI.
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。
Conducts a Data Protection Impact Assessment for automated decision-making and profiling systems under GDPR Article 35(3)(a), covering algorithmic transparency, meaningful human oversight, contestation mechanisms, and Art. 22 safeguards. Activate for DPIA automated decision, profiling DPIA, algorithmic impact assessment, Art. 35(3)(a), ADM risk assessment queries.
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。
Cloud service provider privacy assessment framework. Covers ISO 27018 cloud privacy controls, CSA STAR certification, SOC 2 Type II evaluation, shared responsibility model mapping, data residency verification, and cloud-specific privacy risk analysis.
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。
Guides compliance with China's Personal Information Protection Law (PIPL, effective 1 November 2021). Covers consent requirements, cross-border transfer mechanisms (CAC security assessment, standard contracts, certification), separate consent triggers, and critical information infrastructure obligations. Keywords: PIPL, China data protection, CAC security assessment, cross-border transfer, separate consent, CIIO.
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。
Preparing EU AI Act compliance documentation for high-risk AI systems. Covers Annex III classification, technical documentation under Art. 11, conformity assessment, risk management systems, and CE marking requirements. Keywords: EU AI Act, high-risk AI, Annex III, conformity assessment, CE marking.
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。
Guides assessment of third-country adequacy decisions under GDPR Article 45 for international data transfers. Covers the current EC adequacy decisions list, adequacy assessment criteria, partial adequacy handling, and monitoring of adequacy decision reviews. Keywords: adequacy decision, Article 45, third country, adequate protection, EC adequacy list.
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。
Think and work like an expert Life-Cycle Assessment Analyst. Use when a task calls for Life-Cycle Assessment Analyst judgment. Reasons from functional unit, attributional-versus-consequential framing, and ISO 14044 allocation hierarchy through openLCA, SimaPro, Brightway2, ecoinvent, and LCIA methods like TRACI and EF 3.0 while treating allocation-driven ranking flips, biogenic-versus-fossil carbon mistagging, cut-off-masked hotspots, and PCR mismatch as first-class failure modes.
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 review legal risk assessment deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for legal risk assessment planning or analysis.
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 MATLAB Grader assessment items that are suitability-gated, profile-driven, feedback-aware, and validated through MATLAB MCP. Produces Script, Function, Class Definition, Class Inheritance, Object Usage, and Class Methods items with MATLAB Grader assessment setup instructions.
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 instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.
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。
Select the right formative assessment technique for a specific learning moment, purpose, and age group. Use when choosing how to check understanding during, between, or after 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。
Design multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.
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。
Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.
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。
Conduct Fundamental Rights Impact Assessments (FRIA) for high-risk AI systems as required by EU AI Act Article 27. Use when (1) deployers of high-risk AI systems (Annex III) need to assess fundamental rights impacts before first use, (2) public bodies or private entities providing public services are deploying AI systems, (3) credit scoring or insurance risk assessment AI systems are being deployed (Annex III 5b, 5c), (4) evaluating impact on natural persons' fundamental rights (dignity, liberty, privacy, data protection, non-discrimination, children's rights, etc.), or (5) creating EU AI Act Article 27 compliance documentation with market surveillance notification.
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。
Assessing anthropogenic environmental impacts — pollution pathways, habitat destruction and fragmentation, land-use change, invasive species, overharvest, and extinction debt. Covers environmental impact assessment (EIA) methodology, exposure-effect relationships, population viability analysis, IPAT and ecological footprint frameworks, and strategic environmental assessment. Use when quantifying or forecasting human impacts on ecosystems, designing monitoring programs, or evaluating a proposed intervention against a baseline.
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。
AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex 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。