Skill UI

A comprehensive guide detailing best practices for securing Groq API keys and managing data privacy. It covers critical steps such as using environment variables, implementing secure key rotation procedures, preventing key leaks via pre-commit hooks, and defending against prompt injection attacks for robust application development.

This comprehensive guide outlines Apple's Human Interface Guidelines (HIG) for various technologies and user flows (e.g., Siri, Apple Pay, HealthKit, ARKit, Generative AI). It teaches developers how to ensure their applications feel natively integrated, respect user privacy, and adhere to established platform patterns across iOS, macOS, and other Apple devices. Essential reading for building high-quality, compliant, and seamless user experiences.

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.

A comprehensive framework for assessing the security, privacy, and compliance posture of Kling AI integrations. It covers crucial areas such as data residency, credential management, content policy adherence, and GDPR compliance (e.g., right to erasure). Use this guide when preparing for security audits or implementing robust data handling protocols for AI-generated content.

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

This skill provides comprehensive patterns for handling sensitive data when integrating with Mistral AI. It covers PII redaction (emails, phone numbers, etc.), implementing data retention policies, and enforcing compliance with global privacy regulations like GDPR and CCPA. Key features include safe API wrappers, dataset sanitization for fine-tuning, and conversation history management with TTL and user erasure rights.

A comprehensive guide covering essential security practices for developing Obsidian plugins. Learn how to safely handle sensitive credentials (avoiding data.json), implement robust input validation, prevent Cross-Site Scripting (XSS), secure URI handlers, and adhere to modern Electron and Obsidian guidelines for data privacy and system integrity.

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 skill implements robust data privacy controls for interactions with OpenRouter APIs. It automatically detects and redacts Personally Identifiable Information (PII)—such as emails, SSNs, credit cards, and API keys—from prompts. It ensures compliance with privacy regulations like GDPR and CCPA, allowing developers to safely process sensitive data when using various upstream LLM providers.

This guide provides comprehensive strategies for implementing privacy-safe analytics using PostHog. It addresses critical compliance requirements like GDPR and CCPA, covering explicit consent management (opt-in/opt-out), real-time property sanitization (stripping PII like IP or tokens), and methods for handling Data Subject Access Requests (SAR) and the Right to Erasure. Use this to build highly compliant and privacy-preserving analytics pipelines.

Automatically scans code and configs using the data-privacy-scanner plugin to flag sensitive data exposure and compliance violations for privacy audits.

This skill provides a comprehensive guide for securely handling student audio data, assessment records, and learning progress within the Speak platform. It focuses on implementing robust data pipelines while ensuring compliance with international privacy regulations such as GDPR and COPPA. Essential for developers integrating or maintaining language learning applications.