Skill UI

This guide provides a comprehensive technical deep dive for developers migrating image generation workflows from services like DALL-E, Midjourney, and Stable Diffusion to Ideogram. It covers API parameter mapping, architectural patterns (like the Adapter pattern), and provides code examples to ensure a stable, gradual, and feature-rich transition to Ideogram's platform.

A comprehensive guide for configuring API access across development, staging, and production environments. This setup ensures strict isolation of API keys and environment-specific parameters (like models, speed, and caching). It covers best practices for secret management using major CI/CD and secret services (GitHub Actions, AWS Secrets Manager, GCP Secret Manager).

A comprehensive checklist and guide for deploying and maintaining Ideogram image generation services in a live production environment. It covers essential steps including API key management, request handling best practices, ensuring image persistence in durable storage, comprehensive error handling (401, 422, 429), and defining robust rollback procedures.

Provides comprehensive patterns for managing API rate limits and handling throttling when interacting with services like Ideogram. This guide demonstrates robust techniques including implementing exponential backoff with jitter, utilizing concurrency-limited queues (e.g., p-queue), and implementing token bucket algorithms. Use this skill to ensure stable, high-throughput, and reliable batch API calls.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.

This skill guide provides comprehensive instructions for deploying Instantly.ai's API v2 webhook receivers and automation services to major cloud platforms. It covers setting up resilient, public HTTPS endpoints using Vercel serverless functions, Google Cloud Run containers, or Fly.io services. Use this when moving from local development to production or setting up robust webhook endpoints that handle real-time event processing.

This skill provides robust patterns for handling API rate limits (e.g., 429 errors) when building high-throughput integrations. It demonstrates best practices including exponential backoff with jitter, request queuing, concurrency control, and specific endpoint throttling mechanisms, ensuring reliable service connectivity.

Automates Istio service mesh configuration guidance for DevOps Advanced users, offering best-practice patterns, production-ready code, validation, and infrastructure-as-code support across Kubernetes, Terraform, and monitoring workflows.

Specialized skill for designing, building, and optimizing modern, cloud-native enterprise Java applications. Covers microservices architecture, Spring Boot 3.x, reactive programming (WebFlux), advanced data persistence (JPA optimization, R2DBC), and robust security implementation (OAuth2/JWT). Use this when implementing DDD patterns, ensuring high test coverage, and managing complex transaction boundaries.

Defines Java 17+ coding standards for Spring Boot services, covering naming, immutability, Optional usage, streams, exceptions, generics, project layout, and testing practices to ensure readable, maintainable code.

An expert guide for building scalable, cloud-native enterprise applications using the latest Java 21+ features, including Virtual Threads and Pattern Matching. Deep mastery of the Spring Boot 3.x ecosystem, Spring Cloud, and microservices architecture. Covers full development lifecycle, from JVM performance tuning (GraalVM, ZGC) to robust testing (Testcontainers) and secure DevOps practices (Kubernetes, CI/CD).

This comprehensive checklist guides the operational validation of the Juicebox AI talent search and analysis platform in a production environment. It ensures system reliability, data security, and service continuity by reviewing critical areas such as API key management, rate limit handling, robust error detection (circuit breakers, exponential backoff), data encryption (GDPR/CCPA compliance), and comprehensive monitoring setup. It is essential for preventing service failures and maintaining search data integrity.