1,321 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。
Implements CPRA Section 1798.135 opt-out preference signal handling, covering Global Privacy Control (GPC) technical detection, automated signal honoring, cross-device consistency, and the relationship between browser signals and explicit consumer choices. Activate for GPC, opt-out preference signal, CPRA 1798.135, browser privacy signal, Do Not Sell 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 DPIA for migrating personal data to cloud infrastructure covering controller-processor analysis under Art. 28, international transfer assessment, encryption requirements, and shared responsibility model evaluation. Activate for cloud adoption, SaaS procurement, or data centre migration projects. Keywords: cloud migration, DPIA, Art. 28, processor, encryption, shared responsibility, SaaS, IaaS, PaaS.
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。
Complete CCPA/CPRA compliance implementation covering California Civil Code §1798.100-199. Includes consumer rights framework, business obligations, service provider and contractor requirements, enforcement mechanisms, and CPPA rulemaking. Triggers on CCPA, CPRA, California privacy, consumer rights.
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 California Consumer Privacy Act (CCPA) consumer rights requests under Civil Code sections 1798.100-125, covering the right to know, right to delete, right to opt-out of sale, and non-discrimination. Includes 45-day response window and identity verification requirements. Activate for CCPA, California privacy, right to know, right to delete, opt-out of sale 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。
Complete guide to LINDDUN privacy threat modeling methodology covering seven threat categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat tree catalogs, mitigation mapping to privacy design patterns, and step-by-step process.
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-deployment privacy compliance checklist for AI/ML systems covering DPIA completion, lawful basis verification, transparency notices, human oversight mechanisms, bias testing, and post-deployment monitoring setup. Keywords: AI deployment, privacy checklist, go-live, model deployment, compliance gate.
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 structured post-breach remediation using a lessons learned framework covering root cause remediation, control gap closure, policy updates, training modifications, monitoring enhancements, and regulatory follow-up. Provides a systematic approach to preventing breach recurrence and demonstrating accountability to supervisory authorities. Keywords: post-breach, remediation, lessons learned, root cause, control gap, policy update, training.
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 digital forensics investigations following a personal data breach, covering evidence preservation, chain of custody documentation, log analysis, scope determination, and root cause analysis. References industry-standard tools including Splunk, ELK Stack, and Wireshark. Provides forensic workflow from initial evidence collection through final investigation report. Keywords: digital forensics, breach investigation, evidence preservation, chain of custody, root cause analysis, Splunk, ELK, Wireshark.
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。
API security testing workflow for REST and GraphQL APIs covering authentication, authorization, rate limiting, input validation, and security best 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。
Expert guidance for building modern Android UIs with Jetpack Compose, covering state management, navigation, performance, and Material Design 3.
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。
Production-grade Android app development guide covering native (Kotlin/Java), cross-platform (Flutter, RN, KMM), and hybrid architectures.
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。
Compiled CLI covering all 52 endpoints of the Anytype local API — objects, properties, tags, search, chat, files — one binary, no MCP server needed.
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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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。
Indie horror aesthetic pack for Blender covering Silent Hill, Fear to Fathom, Puppet Combo, corporate horror, cabin/forest/hospital horror, and related looks.
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。
Visual era art direction for Blender covering PS1–PS5 stylistic cues, retro arcade, DOS, CRT, VHS, and film-grain presentation.
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。
Biome and setting art direction for Blender covering underwater, desert, arctic, jungle, underground, and floating-island environments.
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。
Fantasy world-building art direction for Blender covering medieval, high fantasy, dark fantasy, and urban fantasy asset language.
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。
Character archetype art direction for Blender covering fantasy races, horror creatures, roles (knight, assassin, detective), and body types (mecha, android, chibi, etc.).
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。
Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.
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。
Plan and verify software releases with versioning, changelogs, release branches, feature flags, canaries, migration gates, rollback, deployment checks, and release readiness. Use when preparing a release, shipping a risky PR, coordinating app/backend/database rollout, recovering from a bad deploy, or defining release policy for a repo.
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。
Complete testing strategy covering TDD workflow, test pyramid, unit/integration/E2E/property testing, framework best practices (Jest, Vitest, pytest), mock strategies, and CI integration. Use when writing tests, reviewing test quality, or establishing testing standards.
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。
n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.
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。