Skill UI

Provides comprehensive tools for managing enriched lead data in compliance with global privacy regulations like GDPR and CCPA. This skill guides practitioners through data classification (identifying PII sensitivity), input validation, deduplication, implementing data retention metadata, and anonymizing sensitive fields for secure reporting and analytics.

This skill provides a comprehensive guide on implementing robust data governance patterns within Delta Lake, leveraging Unity Catalog. It covers essential compliance topics such as data classification tagging, handling GDPR 'right-to-deletion' requests for PII, and establishing automated data retention policies. Use this when designing secure, compliant, and auditable data pipelines in a data lake environment.

This skill provides comprehensive guidelines for handling sensitive people datasets from Juicebox AI. It covers critical data classification (PII, search results), required technical controls (AES-256 encryption, field-level masking), and adherence to international privacy regulations like GDPR and CCPA. It ensures users implement lawful data processing, proper consent tracking, and secure data export mechanisms.

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.

Amazon Macie is a fully managed data security service that automatically discovers, classifies, and protects sensitive data (PII, financial records, credentials) stored in Amazon S3. It uses advanced machine learning and pattern matching to identify compliance risks and enforce data governance policies. Ideal for organizations needing automated data discovery for regulatory compliance and improving overall cloud security posture.

This skill provides comprehensive guardrails for handling Protected Health Information (PHI) in US healthcare software. It guides development teams on data classification, security best practices, access control, and audit logging to ensure strict adherence to HIPAA regulations. Use it when dealing with patient data storage, transmission, or processing to prevent breaches and maintain compliance.

The Hugging Face Transformers library is a comprehensive toolkit providing access to thousands of pre-trained models across diverse domains including Natural Language Processing (NLP), Computer Vision, Audio, and Multimodal tasks. It enables developers to easily load models, perform high-level inference (e.g., text generation, classification, QA), and fine-tune models on custom datasets using streamlined APIs like Pipelines and the Trainer class. Ideal for building advanced, state-of-the-art AI applications.

This skill provides comprehensive methods for interacting with the Figma API. It covers retrieving and managing comments, accessing version history, and handling sensitive user data. Emphasis is placed on implementing robust privacy controls, PII redaction, data classification, and ensuring compliance with regulations like GDPR/CCPA.

Implements robust data governance for Canva Connect API interactions. This skill covers data classification (PII, Secrets, Business), token redaction, temporary URL expiry, and enforces strict data retention policies. It is essential for developers building integrations that must adhere to global privacy standards like GDPR and CCPA, ensuring secure data handling from ingestion to deletion.

This collection provides production-ready, idiomatic patterns for the @hubspot/api-client SDK, specifically tailored for TypeScript. It addresses critical development concerns including creating typed wrappers, implementing robust, structured error classification (handling rate limits, authentication issues, etc.), and managing efficient batch operations (create, read, upsert) for HubSpot CRM data. Use this when building scalable, reliable, and maintainable HubSpot integrations.

This guide teaches best practices for configuring robust, enterprise-grade Role-Based Access Control (RBAC) within Palantir Foundry. Learn how to define project roles (Viewer, Editor, Owner), manage organization-level groups to enforce least privilege, establish secure service user accounts for external integrations, and utilize marking-based data classification to ensure data governance and security across large datasets.