Skill UI

A Java exporter that facilitates sending OpenTelemetry telemetry data—including traces, metrics, and logs—from Java applications directly to Azure Monitor or Application Insights. It is crucial for achieving centralized, comprehensive observability into enterprise applications.

This library serves as a low-level exporter for Python applications, enabling the reliable transfer of comprehensive OpenTelemetry telemetry data—including traces, metrics, and logs—to Azure Application Insights. It provides robust configuration options, including connection string setup, Azure AD authentication, custom sampling, and offline storage management, ensuring complete and reliable observability.

This SDK provides comprehensive support for automatically instrumenting Node.js applications to collect observability data. It integrates distributed tracing, metrics, and logs using the OpenTelemetry standard, allowing seamless data export and monitoring within Azure Monitor. It supports various resource types and connection methods.

This SDK provides full management plane capabilities for Azure Cache for Redis within the .NET ecosystem. It allows developers to provision, configure, and manage various aspects of the Redis resource using the Azure Resource Manager (ARM) framework. Key functions include creating and updating cache instances, managing network firewall rules, rotating access keys, scheduling patch updates, and performing data import/export operations.

This skill provides comprehensive methods for managing user data and ensuring compliance with global privacy regulations like GDPR and CCPA. It covers essential features such as data export, implementing the "right to be forgotten" (user deletion), managing user consent via metadata, and maintaining detailed audit logs. Use this for building robust, legally compliant user data management systems in modern web applications.

A comprehensive guide for migrating user authentication systems from major providers like Auth0, Firebase, Supabase, or NextAuth to Clerk. It covers essential steps including user data export, bulk import, updating database references, and replacing client and server-side authentication code for a smooth, phased transition.

A comprehensive guide and toolkit for managing complex, large-scale data migration to and from Evernote. It covers essential processes including exporting notes in ENEX, Markdown, and JSON formats, importing structured data via ENML, and implementing robust migration runners with progress tracking and failure checkpointing. Ideal for developers handling massive data archives or cross-platform system integrations.

This skill manages the complete lifecycle of Fireflies.ai meeting transcripts. It allows users to fetch raw data, export in various formats (JSON, CSV, SRT), sanitize sensitive personal information (PII Redaction), and enforce data retention policies to ensure compliance with regulations like GDPR and CCPA. Ideal for data governance and compliance workflows.

A comprehensive mechanism for managing data privacy, retention, and compliance when integrating with Gamma APIs. This skill guides users on handling Personally Identifiable Information (PII), sanitizing user content and logs, securely archiving exported files (e.g., via S3), and implementing precise data retention policies to meet standards like GDPR and CCPA. Essential for building compliant, production-ready applications.

This comprehensive guide covers the entire data lifecycle management for Granola meeting notes. Users learn how to export data using local cache scripts or Enterprise APIs, set customized data retention policies, and handle data subject access requests (SARs). It is essential for maintaining GDPR/CCPA compliance and ensuring data portability.

A comprehensive guide detailing the strategy for migrating from competing meeting note tools (such as Otter.ai, Fireflies, Fathom) to a new platform like Granola. This skill covers assessing migration scope, performing source-specific data exports (TXT, JSON, Markdown), selecting the optimal migration strategy (Fresh Start, Selective, Full Archive), and executing a phased transition through parallel runs and final cutover procedures. It ensures historical data preservation and minimizes operational disruption.

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.