Skill UI

This skill automates the validation of AWS environments against critical industry compliance benchmarks. It performs detailed checks for CIS AWS Foundations, PCI-DSS, HIPAA, and SOC 2 standards, covering key areas like Identity and Access Management (IAM), logging, data protection, and network security. Essential for preparing for regulatory audits and maintaining continuous secure operations.

This skill provides expert guidance for modern, multi-cloud database administration. It covers comprehensive knowledge across AWS, Azure, and GCP platforms, spanning relational, NoSQL, and graph databases. Key areas include Infrastructure as Code (Terraform, Flyway), achieving high availability, disaster recovery, advanced performance optimization, and ensuring strict regulatory compliance (HIPAA, GDPR). It is essential for managing complex, highly reliable cloud data architectures.

Automates compliance and security audit reporting for standards such as PCI DSS, HIPAA, SOC 2, and ISO 27001 by using the compliance-report-generator plugin to collect details, format documentation, and streamline regulatory proof.

Configure robust role-based access control (RBAC) for Granola workspaces, essential for enterprise-level data security and governance. This guide details setting up role hierarchies (Owner, Admin, Member, Viewer), defining granular permissions, integrating SSO/SCIM for automated provisioning, and implementing least-privilege access principles. It also covers advanced features like per-workspace sharing policies and detailed audit logging to ensure regulatory compliance.

A comprehensive, compliant wrapper for integrating with the Groq API. This module automatically sanitizes input prompts and filters model responses to redact sensitive PII (emails, phone numbers, SSN, etc.). It also tracks detailed token usage and generates robust audit logs, ensuring data privacy and regulatory compliance (GDPR, etc.) in enterprise AI applications.

A comprehensive guide for implementing and managing Role-Based Access Control (RBAC) within the Guidewire InsuranceSuite (PolicyCenter, ClaimCenter, BillingCenter). It details role hierarchies (Admin, Underwriter, Claims Adjuster), permission checking logic using APIs, and best practices for ensuring regulatory compliance (NAIC, DOI). Includes code examples for user provisioning and audit logging.

A comprehensive skill for implementing and managing Information Security Management Systems (ISMS) aligned with ISO 27001:2022 and global regulatory standards. This tool guides users through critical security processes, including risk assessments, control implementation, gap analyses, and preparing for audits. Essential for HealthTech and MedTech companies ensuring data privacy and compliance.

This module provides a compliance-grade, tamper-evident logging solution for tracking all Kling AI operations. It records task submissions, status changes, and credential usage into structured, hashed logs. Use this when meeting regulatory compliance requirements or preparing for internal/external security audits.

This skill provides comprehensive methods for managing the entire data lifecycle of LLM observability platforms like Langfuse. Functions include exporting detailed traces and scores, configuring retention policies (e.g., automatic data cleanup), handling GDPR subject requests (access and erasure), and anonymizing Personally Identifiable Information (PII) for secure analytics. Essential for maintaining regulatory compliance in AI applications.

A comprehensive guide for achieving compliance with the EU MDR 2017/745. It covers crucial aspects including medical device classification (Annex VIII), developing technical documentation (Annex II/III), planning clinical evaluation (CER), and establishing post-market surveillance (PMS/PSUR) systems. Ideal for regulatory specialists preparing for Notified Body audits or needing to gap-check technical files.

This comprehensive guide defines the secure data lifecycle management for OpenEvidence, an AI-powered platform for clinical evidence synthesis. It outlines data classification (e.g., clinical queries, patient responses), strict adherence to global regulations (HIPAA, GDPR, FDA), and technical requirements for handling Protected Health Information (PHI). Key processes include PHI de-identification, data validation, secure API integration, and compliant data export, ensuring the system maintains the highest level of patient privacy and regulatory compliance.

This guide outlines the strict multi-environment setup for the OpenEvidence clinical AI system, crucial for maintaining HIPAA compliance. It details how to configure and validate separate environments (Development, Staging, Production) to manage different data classifications—synthetic, de-identified, and full PHI. The process emphasizes environment validation checks, BAA adherence, and robust audit logging to prevent regulatory violations and PHI exposure.