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概要と使いどころ
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
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Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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Implements data protection compliance for whistleblowing systems under EU Directive 2019/1937 and GDPR. Covers anonymous reporting channels, identity protection for whistleblowers and accused persons, retention limits, access restrictions, and retaliation prevention. Addresses national transpositions and DPA guidance. Keywords: whistleblower, Directive 2019/1937, anonymous reporting, identity protection, retaliation, retention, reporting channel.
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Design and implement Purpose-Based Access Control (PBAC) architecture including purpose ontology definition, policy engine configuration, audit logging of purpose verification at query time, and integration with existing IAM systems. Enforces GDPR Article 5(1)(b) purpose limitation technically.
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Links RoPA entries to Data Protection Impact Assessments and lawful basis assessments. Covers cross-reference systems, dependency tracking, and update cascade triggers between RoPA, DPIA register, and lawful basis documentation. Activate for RoPA-DPIA link, cross-reference, dependency tracking, impact assessment linkage, cascade updates.
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Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.
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Addresses healthcare AI privacy at the intersection of HIPAA and the EU AI Act for clinical decision support systems. Covers training data PHI handling, model transparency and explainability, patient rights in algorithmic decisions, FDA/OCR regulatory coordination, and bias monitoring. Keywords: healthcare AI, HIPAA, AI Act, clinical decision support, PHI training data, model transparency.
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Configures privacy settings for enterprise HR systems including SAP SuccessFactors, Workday, and BambooHR. Covers role-based access controls, automated data retention enforcement, cross-border transfer configurations, audit logging, data subject rights facilitation, and field-level security. Keywords: HR system, SAP SuccessFactors, Workday, BambooHR, RBAC, retention automation, cross-border transfer, privacy configuration.
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Design privacy-preserving analytics systems using differential privacy, k-anonymity, l-diversity, and t-closeness. Covers privacy budget allocation with epsilon tracking, references Google DP library, OpenDP, and Apple PPML. Includes Python differential privacy implementation for GDPR-compliant statistical analysis.
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Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
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Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.
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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.
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Implements data classification labels and tagging systems including metadata tagging, DLP integration, automated label propagation, user-applied labels, and label inheritance rules. Covers Microsoft Purview sensitivity labels and enterprise labeling architecture. Keywords: data labeling, sensitivity labels, metadata tagging, DLP integration, label propagation, Purview, classification.
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Conducts Data Protection Impact Assessments for employee monitoring systems per EDPB Guidelines 3/2019 on workplace data processing. Covers video surveillance, email monitoring, GPS tracking, keystroke logging, and productivity tools. Applies proportionality testing under Art. 35 GDPR. Keywords: DPIA, employee monitoring, surveillance, proportionality, EDPB, workplace privacy, keystroke logging, GPS tracking.
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Automated enforcement of GDPR Article 5(1)(e) storage limitation principle. Covers TTL-based deletion, retention policy engines, archival workflows, legal hold exemptions, and lifecycle automation. Includes technical implementation patterns for automated data expiry and defensible deletion across distributed systems.
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Implements automated data deletion workflows for GDPR Article 17 right to erasure and retention period expiry. Covers cascading deletion across dependent systems, dependency handling for referential integrity, confirmation logging, and audit trail generation. Activate for automated deletion, erasure automation, data purge, retention expiry queries.
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Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.
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Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Covers Art. 9 special category requirements, Art. 35(3)(b) mandatory DPIA triggers for large-scale biometric processing, and EDPB Guidelines 3/2019 on video surveillance. Keywords: biometric, facial recognition, fingerprint, DPIA, Art. 9, special category, EDPB Guidelines 3/2019.
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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.
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Implements data subject rights mechanisms for AI systems including right to explanation of AI decisions, contestation procedures, human review, model output correction, and training data access. Covers GDPR Arts. 15-22 and AI Act Art. 86. Keywords: data subject rights, AI explanation, contestation, human review, training data access, model correction.
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Implements GDPR Art. 22 automated decision-making and AI Act Art. 14 human oversight requirements for AI systems. Covers identification of solely automated decisions, meaningful human intervention design, logic explanation mechanisms, and contestation procedures. Keywords: Art. 22, automated decision, human oversight, AI Act, profiling, contestation.
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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.
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Creates and maintains project documentation for features, systems, and components. Use when documenting how a feature, system, or component works — including writing, updating, or organizing docs. Does not scan or detect the project's technology stack — use project-discovery for repository analysis and config detection. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not create or update coding standards — use coding-standard instead. Does not generate PR descriptions — use update-pr-description for that. Does not produce runbooks for operational scenarios — use runbook for that. Does not rewrite existing prose for readability — use edit-for-readability for that. Does not produce an ephemeral, understand-now overview of code or a PR — use code-overview for that.
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Modern Zig project architecture guide. Use when creating Zig projects (systems programming, CLI tools, game dev, high-performance services). Covers explicit allocators, comptime, error handling, and build system.
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