Skill UI

This tool provides comprehensive cost management for Apollo.io API usage. It optimizes credit expenditure by implementing a daily budget tracker, preventing double-enrichment through deduplication, and using a lead scoring mechanism to ensure credits are only spent on high-potential leads. Ideal for large-scale data enrichment tasks to minimize API costs.

Implements enterprise-grade Role-Based Access Control (RBAC) for Apollo.io API integrations. This skill is essential for managing team permissions, ensuring data segregation, and implementing robust security controls by proxying API calls. It defines granular roles (Viewer, Analyst, Admin) and maps specific API operations to required permissions, preventing all-or-nothing key usage.

An advanced guide for optimizing API interaction with Apollo.io. This skill covers critical performance enhancements including implementing connection pooling, setting per-endpoint time-to-live (TTL) caching, utilizing bulk enrichment endpoints for efficiency, and controlling concurrency during parallel searches. It also demonstrates how to slim down large API response payloads to reduce memory and bandwidth usage.

A comprehensive utility designed to manage and automate the safe migration of applications using the Apollo.io API. It guides developers through auditing current API usage, identifying deprecated endpoints and authentication methods, and implementing feature-flagged changes. Key features include generating migration maps and running shadow tests for parallel comparison between old and new API versions, ensuring zero downtime during major system upgrades.

⚠️ This SDK is deprecated and used for maintaining legacy code only. It provides functionality for Azure Communication Services calling servers in Java. For all new development, developers must migrate to the updated 'Call Automation' SDK (`azure-communication-callautomation`). This guide details the necessary class and usage changes for a smooth transition.

⚠️ This SDK is deprecated and used for maintaining legacy code only. It provides functionality for Azure Communication Services calling servers in Java. For all new development, developers must migrate to the updated 'Call Automation' SDK (`azure-communication-callautomation`). This guide details the necessary class and usage changes for a smooth transition.

This guide provides comprehensive strategies to optimize costs associated with Clerk authentication services. It details understanding the MAU pricing model, implementing route-level authentication controls, utilizing caching techniques to reduce API calls, and scripting the cleanup of inactive users, helping maintain compliance and controlling expenditure.

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.

This skill provides comprehensive patterns and scripts for managing rate limits when automating workflows involving CodeRabbit and the GitHub API. It covers both CodeRabbit's internal review limits and GitHub's core API limits, including best practices for safe pagination, GraphQL usage, and local caching to ensure robust, reliable automation.

This comprehensive guide identifies and explains the most common anti-patterns and mistakes when integrating with Customer.io. It covers crucial development topics, including proper API key usage, Unix timestamp formatting, ensuring correct event tracking order (Identify before Track), and optimizing API calls for performance. Use this as a definitive checklist for code review and developer onboarding.

This skill provides comprehensive methods to optimize Deepgram API usage and manage costs. It implements budget-aware transcription pipelines that preemptively check projected spending against set limits, utilizes audio preprocessing techniques (like silence removal via ffmpeg) to minimize billable minutes, and includes API calls to monitor current usage and spending against defined budgets. Ideal for cost-sensitive, production-grade deployments.

Implements a comprehensive observability stack for Deepgram integrations. It utilizes Prometheus metrics for tracking request rates, latency, and costs, OpenTelemetry for distributed tracing, Pino for structured logging, and AlertManager rules for proactive alerting. Essential for production monitoring, dashboard setup, and performance analysis of Deepgram API usage.