Skill UI

This skill provides a comprehensive guide and implementation for handling API rate limiting and network retries (429 errors). It utilizes advanced patterns like the Sliding Window Limiter for per-endpoint rate control, exponential backoff with jitter, and request queuing with concurrency control. Ideal for building robust, resilient applications that interact with rate-limited external APIs like Apollo.io.

This module provides production-ready TypeScript patterns for interacting with the Apollo.io REST API. It implements critical best practices including type-safe client setup (using Zod), custom error handling, exponential backoff retry logic, asynchronous pagination, and rate-aware bulk processing. Use this when building reliable, scalable, and robust integrations with Apollo data.

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.

Automate Beeminder tasks via the Composio Beeminder toolkit exposed through Rube MCP, always discovering current tool schemas via RUBE_SEARCH_TOOLS before connecting and executing with RUBE_MANAGE_CONNECTIONS and RUBE_MULTI_EXECUTE_TOOL flows.

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.

This guide provides comprehensive strategies for managing rate limits and quotas when interacting with the Clerk API. It covers implementing robust retry logic using exponential backoff for 429 errors, optimizing bulk operations through batching, establishing multi-level caching (in-memory/Redis) to reduce API calls, and monitoring usage to ensure stability in high-traffic production environments.

Implements robust mechanisms for handling API rate limits (e.g., 429 errors) when interacting with services like Customer.io. It utilizes the Token Bucket algorithm for precise throttling and combines it with exponential backoff, jitter, and queue processing (p-queue) to ensure high reliability and prevent API overloads.

This guide details production-ready patterns for integrating the Customer.io SDK (customerio-node). It covers best practices including building type-safe clients, implementing robust retry logic with exponential backoff for network stability, performing efficient event batching for high-volume data streams, and managing singleton lifecycles. Use these patterns to build scalable, reliable, and maintainable user behavior tracking systems.