7,325 件 ・ 関連度順
概要と使いどころ
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。
Assesses lawful basis for AI training data processing per EDPB April 2025 report on LLMs and general-purpose AI. Covers legitimate interest balancing tests, consent challenges for ML training, public dataset assessment, and web scraping lawfulness. Keywords: AI training data, lawful basis, EDPB LLM, legitimate interest, consent, web scraping.
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。
Validates and optimizes Blender assets for polycount, topology, UV efficiency, material count, naming, collections, and game-ready performance. Use before export on all production assets via MCP.
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。
Assertible integration. Manage data, records, and automate workflows. Use when the user wants to interact with Assertible 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。
Assembled integration. Manage data, records, and automate workflows. Use when the user wants to interact with Assembled 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。
AssetSonar integration. Manage data, records, and automate workflows. Use when the user wants to interact with AssetSonar 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。
AssemblyAI integration. Manage Transcripts, Speakers, Jobs. Use when the user wants to interact with AssemblyAI 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。
Assembla integration. Manage Organizations, Leads, Deals, Pipelines, Users, Goals and more. Use when the user wants to interact with Assembla 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。
Fix "ref.listen can only be used within the build method of a ConsumerWidget" assertion error in Flutter Riverpod 3.x. Use when: (1) ConsumerStatefulWidget crashes with "Failed assertion: line 492 pos 7: 'debugDoingBuild'" in flutter_riverpod consumer.dart, (2) ref.listen is called inside initState, addPostFrameCallback, or any lifecycle method other than build(), (3) All widget tests fail with this assertion from scheduler callback. Applies to flutter_riverpod 3.0+ with ConsumerStatefulWidget.
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。
Fix silent video/media processing failures caused by URL extraction code that filters on file extensions (.mp4, .webm, .webp). Use when: (1) Media moderation, transcoding, or analysis silently skips files from Blossom or content-addressed storage servers, (2) URL extraction from Nostr event tags (imeta, r tags) drops URLs without recognized extensions, (3) CDN fallback URLs append .mp4 but the actual server uses extensionless content-addressed paths like /{sha256}. Common in Nostr video events (kind 34236) where different clients use different URL formats.
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。
Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with RTL designs that need formal property generation, when adding assertions to existing RTL, or when users ask to infer properties, generate assertions, or create formal specifications from hardware designs.
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 design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.
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。
Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.
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 tailored sales assets (landing pages, decks, one-pagers, workflow demos) from your deal context. Describe your prospect, audience, and goal — get a polished, branded asset ready to share with customers.
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 and classify legal risks using a severity-by-likelihood framework with escalation criteria. Use when evaluating contract risk, assessing deal exposure, classifying issues by severity, or determining whether a matter needs senior counsel or outside legal review.
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, assess, and mitigate operational risks. Trigger with "what are the risks", "risk assessment", "risk register", "what could go wrong", or when the user is evaluating risks associated with a project, vendor, process, or decision.
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。
Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted issues.
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。
Validate email addresses for deliverability, check reputation scores, verify MX records, and assess email-sending safety. Covers deliverability checks, reputation scoring, bounce risk assessment, and bulk validation for outreach campaign preparation.
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。