1,514 件 ・ 関連度順
概要と使いどころ
Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback 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。
Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.
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 a criterion-referenced rubric with descriptive performance levels for a task or objective. Use for marking guides and general curriculum contexts. For Manning programmes where Competent = success, use coherent-rubric-logic-builder instead.
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。
Expert blueprint for GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.
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。
Dependency auditing, CVE detection, configuration security review, CVSS scoring, and prioritized vulnerability reporting
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。
OWASP Top 10 testing, injection vulnerability detection, API security assessment, authentication testing, and web vulnerability reporting for authorized assessments
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。
Assessment: read-only inspection, codebase overview, value analysis, health checks, ADR consultation, decision analysis, multi-perspective critique.
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。
Security auditing and vulnerability assessment specialist. Use when conducting security reviews, analyzing code for vulnerabilities, performing OWASP assessments, or creating security audit reports.
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。
Meta-cognitive reasoning specialist for evidence-based analysis, hypothesis testing, and cognitive failure prevention. Use when conducting reviews, making assessments, debugging complex issues, or any task requiring rigorous analytical reasoning. Prevents premature conclusions, assumption-based errors, and pattern matching without verification.
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。
Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.
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。
Pre-contract vendor privacy due diligence per GDPR Article 28(1). Covers risk questionnaires, technical controls assessment, certification review, data flow analysis, and documented sufficiency decisions for processor engagement.
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 implementation of UK international data transfer mechanisms post-Brexit including the International Data Transfer Agreement (IDTA), UK Addendum to EU SCCs, UK adequacy assessments, and ICO transfer risk assessment tool. Keywords: UK IDTA, UK addendum, ICO TRA, post-Brexit transfers, UK GDPR.
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。
Ongoing vendor privacy compliance monitoring program. Covers annual reassessment procedures, continuous monitoring signals, contract renewal privacy triggers, performance metrics, KPIs, and vendor governance reporting dashboards.
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。
Preparation guide for ISO 31700 privacy by design for consumer goods certification. Covers the 30 requirements across design, production, and disposal phases. Includes gap assessment methodology, remediation planning, and mapping to GDPR Article 25 data protection by design obligations for consumer-facing products and services.
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。
Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for privacy risk identification.
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 implementation of the NIST Privacy Framework IDENTIFY function covering ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Maps NIST PF controls to GDPR requirements for dual-framework compliance. Keywords: NIST Privacy Framework, IDENTIFY function, ID.BE, ID.DA, ID.IM, ID.RA, privacy risk assessment, data actions.
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 impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation privacy.
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 and use of the EU-US Data Privacy Framework adequacy decision for transatlantic data transfers. Covers DPF self-certification with the Department of Commerce, DPF principles compliance, Data Protection Review Court, and annual EC review. Keywords: DPF, EU-US, adequacy, Privacy Shield, transatlantic transfers.
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。
Manages the organisational DPIA register tracking all Data Protection Impact Assessments across the enterprise. Covers DPIA lifecycle management, status tracking, review scheduling, Art. 35(11) periodic reassessment, and supervisory authority reporting. Implements a centralised register linking DPIAs to RoPA entries, risk registers, and mitigation plans. Keywords: DPIA register, DPIA tracking, Art. 35(11), review schedule, DPIA lifecycle, centralised register, DPIA portfolio management.
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 a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.
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。
Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.
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 the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.
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 market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.
60-Second Ad Readiness Snapshot — quick assessment without subagents
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。