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概要と使いどころ
Implements strict data minimization and retention limits for children's personal data under GDPR Art. 5(1)(c), Recital 38, UK AADC Standard 8, and COPPA Section 312.7. Covers strict necessity testing, shorter retention periods, limited profiling, parental dashboard design, and automated deletion. Keywords: data minimization, children, retention, necessity test, parental dashboard.
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。
Creates GDPR Article 30(1) Records of Processing Activities (RoPA) for data controllers with all seven mandatory fields: controller identity and contact details, processing purposes, data subject categories, personal data categories, recipient categories, third country transfers, and retention periods. Includes Python generator for automated RoPA creation. Activate for controller RoPA, Art. 30(1), processing records, data mapping.
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。
Automated cookie consent validation using Selenium and Playwright. Covers banner interaction testing, consent state verification, tag firing audit after consent choices, regression testing for cookie compliance, and CI/CD pipeline integration.
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。
Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training 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。
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。
Produce a standalone test plan by analyzing code for test coverage gaps and edge cases. Use when you need to create, generate, or draft a test plan for a branch, need to analyze test coverage, or need to identify what tests to write for specific files or directories. Does not produce a plain-language plan for a person to run tests by hand — use manual-test-planning for that. Does not write test code — use tdd to implement behavior test-first. Does not refine existing plans — use iterative-plan-review. Does not review code quality, security, or style — use code-review for full code review. Does not evaluate architectural testability or structural coupling — use architectural-analysis for architectural 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。
Write code through a disciplined, BDD-framed Test-Driven Development loop: build a behavior test list, then drive each behavior through red-green-refactor with an enforced observed-failure gate. Use when the user wants to implement, build, or write code test-first, "do TDD", follow "red-green-refactor", drive code from tests, choose the next test by the Transformation Priority Premise (TPP) or ZOMBIES ordering, or grow a feature behavior-by-behavior with tests leading. This skill writes and changes code; it does not produce a test plan document (use automated-test-planning, or manual-test-planning for a plan a person runs by hand), review or audit existing code (use code-review), restructure existing code outside a TDD loop (use refactor, or plan-a-change to plan a multi-module restructure), specify what a feature should do (use plan-a-feature), or find the root cause of a bug (use investigate). Runs its loop to completion without pausing for review; to review each behavior as it lands, use pairing.
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 asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
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 the bioengineering, mechanobiology, and lymphatic transport reasoning of J. Brandon Dixon, professor of mechanical and biomedical engineering at Georgia Institute of Technology. Reach for this skill whenever analyzing lymphatic biomechanics, active vessel contractility versus passive drainage, peristaltic fluid transport, microfluidic organ-on-a-chip design, preclinical lymphedema models, non-invasive functional imaging, targeted nanomedicine delivery, or automated disease staging. Use this skill to critique bioengineering assumptions, guide quantitative protocol design, evaluate biomechanical pump failure, and formulate interdisciplinary biomedical solutions.
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。
Shioaji Taiwan financial trading API guide. Use when trading stocks/futures/options on Taiwan markets, subscribing to real-time market data, querying account info, or building automated trading systems. Shioaji 台灣金融交易 API 指南。適用於：股票/期貨/選擇權交易、即時行情訂閱、帳務查詢、自動交易系統開發。
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。
Fill out web forms, submit data, and handle login or registration flows. Trigger when the user asks to: fill a form, submit data on a website, log in to a site, register an account, complete a checkout, enter information into fields, or automate form submission.
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。
Basecone integration. Manage data, records, and automate workflows. Use when the user wants to interact with Basecone 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。
Basin integration. Manage data, records, and automate workflows. Use when the user wants to interact with Basin 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。
Basiq integration. Manage data, records, and automate workflows. Use when the user wants to interact with Basiq 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。
Beacon integration. Manage data, records, and automate workflows. Use when the user wants to interact with Beacon 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。
Bearer integration. Manage data, records, and automate workflows. Use when the user wants to interact with Bearer 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。
Beekeeper integration. Manage data, records, and automate workflows. Use when the user wants to interact with Beekeeper 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。
Beeple integration. Manage data, records, and automate workflows. Use when the user wants to interact with Beeple 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。
Belco B.V. integration. Manage data, records, and automate workflows. Use when the user wants to interact with Belco B.V. 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。
BelugaCDN integration. Manage data, records, and automate workflows. Use when the user wants to interact with BelugaCDN 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。
Benchmark Email integration. Manage data, records, and automate workflows. Use when the user wants to interact with Benchmark Email 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。
Better Proposals integration. Manage data, records, and automate workflows. Use when the user wants to interact with Better Proposals 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。