2,186 件 ・ 関連度順
概要と使いどころ
Manages legal hold and data preservation processes including triggering events, custodian notification, hold-in-place technical implementation, release procedures, and interaction with retention schedules. Covers litigation hold registers, compliance monitoring, and Art. 17(3)(e) exception documentation. Activate for legal hold, litigation preservation, data freeze, e-discovery hold 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。
Manages RoPA for complex multi-entity corporate groups including entity-level versus group-level records, intra-group transfer documentation, and shared processing coordination. Activate for group RoPA, multi-entity, corporate group, intra-group transfers, subsidiary records, holding company.
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 HIPAA workforce training requirements under 45 CFR §164.530(b) (Privacy Rule) and 45 CFR §164.308(a)(5) (Security Rule). Covers initial onboarding training, periodic refresher cadence, role-based content differentiation, documentation of training completion, and sanction policy integration. Keywords: HIPAA training, workforce training, security awareness, privacy training, §164.530(b), §164.308(a)(5).
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 creation of organisational privacy policy hierarchy aligned to GDPR chapters including top-level policy, supporting procedures, operational guidelines, and training materials. Activate when building or updating policy frameworks. Keywords: policy framework, privacy policy, procedures, guidelines, policy hierarchy.
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 GDPR Article 36 prior consultation process with supervisory authorities when a DPIA indicates high residual risk. Covers timeline requirements, documentation, and outcome handling. Activate when DPIA residual risk remains high or when preparing regulatory submissions. Keywords: prior consultation, Article 36, DPIA, high risk, supervisory authority.
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 data subject and stakeholder consultation requirements during Data Protection Impact Assessments under GDPR Article 35(9). Covers consultation planning, data subject engagement methods, DPO involvement per Art. 35(2), and documentation of views received. Keywords: DPIA consultation, stakeholder engagement, Art. 35(9), data subject views, DPO advice, public consultation, representative groups.
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 appointment of GDPR Article 27 EU representative for non-EU controllers or processors. Covers criteria, responsibilities, and documentation. Activate when a non-EU entity processes EU data. Keywords: EU representative, Article 27, non-EU controller, territorial scope.
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 methodology for auditing website cookies and tracking technologies. Covers automated scanning, cookie categorization, lifecycle documentation, and compliance gap analysis referencing the Planet49 CJEU ruling (C-673/17).
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。
Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
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 AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
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 AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated 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。
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。
Publishes a local Markdown file to a user-specified Confluence location, creating a new page or updating an existing one through the Atlassian MCP server. Use when the user wants to post, publish, push, or sync a Markdown file to a Confluence space or page. Requires a configured Atlassian MCP server. Does not write or generate the Markdown itself — point it at an existing file, or use project-documentation-to-confluence for the document-then-publish flow, or plan-a-feature-to-confluence for the plan-then-publish flow. Does not publish to Jira — use work-items-to-jira.
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。
Builds a feature specification from scratch with plan-a-feature and publishes it to a user-specified Confluence location, posting the spec as a parent page and each companion artifact (decision log, team findings, technical notes) as a child page beneath it. Use when the user wants a new feature planned, designed, scoped, or specified AND posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not plan to local files only — use plan-a-feature. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features to Confluence — use project-documentation-to-confluence.
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。
Produces a progressive-disclosure overview of unfamiliar code or a pull request's changes with code-overview and publishes the resulting overview to a user-specified Confluence location. Use when the user wants code or a PR explained, oriented, or made sense of AND the overview posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not produce the overview to a local file only — use code-overview. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not document an already-understood feature to Confluence — use project-documentation-to-confluence. Does not root-cause a bug to Confluence — use investigate-to-confluence. Does not plan or specify a new feature to Confluence — use plan-a-feature-to-confluence. Does not publish to Jira — use work-items-to-jira.
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。
Builds a feature specification from scratch through a relentless, evidence-based interview that walks the design tree decision-by-decision, resolving dependencies as it goes. Use when the user wants to plan, design, scope, specify, or flesh out a new feature, capability, or system behavior before implementation. Produces a feature specification focused on system behaviors, not implementation detail. Does not plan a restructure of code that already exists — use plan-a-change. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features — use project-documentation. Does not design the contract for an interface — use design-an-api. Does not research open-ended options before there is a feature to specify — use research. Does not map the bounded contexts of existing code — use ddd-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。
Runs an evidence-based investigation of a bug, failure, or unexpected behavior with investigate and publishes the resulting investigation report to a user-specified Confluence location. Use when the user wants something debugged, diagnosed, or root-caused AND the findings posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not investigate to a local file only — use investigate. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not document an already-understood feature to Confluence — use project-documentation-to-confluence. Does not plan or specify a new feature to Confluence — use plan-a-feature-to-confluence. Does not publish to Jira — use work-items-to-jira.
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 this skill when the user requests current information, documentation, coding examples, or web research beyond the knowledge cutoff. Queries Google's AI Search mode to retrieve comprehensive AI-generated overviews with source citations from 100+ websites. Returns markdown with inline footnoted references [1][2][3]. You will receive a detailed Markdown file with references and information summarized directly from Google's AI search. Ideal for you to get new information and clues for further research.
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 the user wants detailed travel itineraries with activities, logistics, day-by-day schedules, vacation planning, trip organization, or travel documentation.
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 the user needs product management workflows such as RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, go-to-market strategy, feature prioritization, research synthesis, or requirements documentation.
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 creating or maintaining UI design systems, design tokens, component documentation, responsive calculations, quality checks, or design-dev handoff materials.
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。
Technical specification and design document expert. Use when writing design docs, RFCs, ADRs, or evaluating technology choices. Covers C4 model, system design, and architecture documentation.
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, produce, or diagnose evidence-backed product demo videos and screen-recorded promotional walkthroughs. Use when the user asks to make a product demo, launch video, feature showcase, app walkthrough, demo reel, or polished recording; wants every important capability shown without a slow feature tour; asks to remove dead time or fix narration-to-action pacing; or needs a repeatable script, capture plan, and verified final media. Do not use for fictional commercials with no real product proof, general-purpose video editing, or documentation-only tutorials that do not need a promotional narrative.
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。