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概要と使いどころ
Transform podcast transcripts into comprehensive content marketing suites including blog posts, social media content, newsletters, SEO-optimized articles, and timestamps. Use when user provides podcast transcripts or wants to repurpose podcast content.
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。
Analyze meeting transcripts to extract decisions, action items, blockers, sentiment, and generate follow-up emails. Use when user provides meeting notes, transcripts, or recordings and needs structured summaries or action tracking.
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。
Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
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。
Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact 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。
UX and visual design consultant for iOS apps using Apple's Liquid Glass design system (iOS 26+). Use when asking about element positioning, layout decisions, visual hierarchy, or whether a design choice is "Apple-approved." Provides design rationale and guidance—not code. Helps create Apple Design Award-worthy apps.
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。
Provides weighted scoring, rubrics, and decision-threshold patterns. Use when designing quality gates, evaluation systems, or decision frameworks.
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。
Provides sanitization guidelines for external content in skills and hooks. Use when loading GitHub Issues, PRs, WebFetch results, or any untrusted input.
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。
Provides auth patterns for API keys, OAuth, and token management. Use when implementing or reviewing service authentication and credential handling.
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。
Provides error classification, recovery, and graceful-degradation patterns. Use when implementing error handling or debugging resilience failures in any skill.
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。
Provides templates and lifecycle patterns for storage and documentation systems. Use when organizing knowledge storage, config lifecycle, or naming conventions.
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。
Provides review-workflow scaffolding for context, evidence, and output. Use at the start of any detailed review to ensure consistent, comparable findings.
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。
Provides standardized pytest config, reusable fixtures, and CI integration patterns. Use when setting up or auditing a Python plugin's test infrastructure.
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。
Provides sem semantic-diff detection, install-on-first-use, and fallback patterns. Use when building skills that consume git diff output.
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。
Implement the NIST Privacy Framework PROTECT function covering PR.AC access control, PR.DS data security, and PR.PO protective policies. Provides technical control implementation guidance, encryption standards, access management architectures, and security-privacy integration patterns.
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。
Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification implementation guidance.
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。
Implement the NIST Privacy Framework COMMUNICATE function covering CM.AW awareness raising and CM.PO communication policies. Provides transparency mechanisms, stakeholder engagement frameworks, privacy notice templates, and communication workflow guidance.
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。
Implement the NIST Privacy Framework GOVERN function covering GV.AT awareness and training, GV.MT monitoring and review, GV.PO policy development, and GV.RR roles and responsibilities. Provides governance structure templates, training programs, and accountability frameworks for privacy governance.
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。
Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for privacy risk identification.
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。
Audits Records of Processing Activities against supervisory authority templates from CNIL, ICO, and BfDI. Provides completeness scoring, gap identification, and remediation tracking. Activate for RoPA audit, completeness check, supervisory authority readiness, CNIL template, ICO template, BfDI template, gap 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。
Design privacy API patterns including data subject API for DSAR endpoints, consent API for preference management, deletion API with cascading delete orchestration, and audit API for compliance reporting. Provides OpenAPI specifications, error handling, rate limiting, and authentication patterns.
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 Privacy Threshold Analysis screening process to determine whether a full DPIA is required. Provides a quick-screen questionnaire, threshold criteria based on WP248rev.01, escalation triggers, and documentation requirements. Activate when evaluating new processing activities, system changes, or procurement decisions. Keywords: PTA, privacy threshold analysis, DPIA screening, quick-screen, threshold criteria, WP248, escalation triggers.
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。
Provides GDPR Article 14 information for personal data obtained from sources other than the data subject, covering timing requirements (within reasonable period, max one month), source disclosure, all required elements, and exemptions under Art. 14(5). Activate for Art. 14, indirect collection, third-party data source, indirect data notice 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。
Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.
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。
Provides GDPR Article 13 information at the point of direct data collection, covering all required elements under Art. 13(1)(a)-(f) and Art. 13(2)(a)-(g), layered notice design, and timing requirements. Activate for Art. 13, direct collection notice, privacy notice at collection, data collection information 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。