「assess」の検索結果

Use when reviewing Java enterprise evidence for MiFID II investment services, investment activities, client classification, suitability, appropriateness, order-handling evidence, best-execution evidence, algorithmic-trading governance evidence, market-access governance evidence, transaction evidence, record keeping, monitoring, or compliance-owner handoff. This should trigger for requests such as Review a Java investment-service platform for MiFID II evidence; Assess suitability, appropriateness, order-handling evidence, or best-execution evidence; Document audit, monitoring, or clock-synchronisation gaps for algorithmic-trading governance; Assess investment-service engineering evidence before production release. Part of Plinth Toolkit

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 a UX lens to a user-facing change: whether it serves the user's real goal and whether the path through it holds together, using the Understanding, Bridging, and Flowing contexts. Use when scoping, planning, or assessing any change that affects what a user sees or does. Loaded as a lens during planning and 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。

Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".

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。

Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.

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 risk metrics for a stock or position including volatility, beta, VaR, and drawdown analysis. Use when user asks about risk, volatility, beta, VaR, value at risk, drawdown, or position sizing.

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 organization's digital transformation readiness. Evaluate data culture, technology adoption, and process maturity.

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。

Evaluates which SaaS tools can be replaced with AI agents. Takes a list of current SaaS subscriptions with costs, assesses replacement feasibility, estimates build vs buy economics, identifies Claude+MCP alternatives, and generates a comprehensive replacement plan with priority matrix, ROI analysis, implementation timeline, and risk 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。

Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.

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。

Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.

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。

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。

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。

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。

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。

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。

Assesses children's data protection in educational technology. Covers COPPA school exception under Section 312.5(c)(4), FERPA intersection, parental rights, teacher consent authority, data deletion at year-end, and Student Privacy Pledge compliance. Keywords: edtech, COPPA school exception, FERPA, student privacy, teacher consent, educational data.

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。