Skill UI

Comprehensive guide for deploying applications powered by the Groq inference API. Covers best practices and specific setup instructions for multiple production environments, including Vercel Edge Functions, Google Cloud Run, Docker containers, and other containerized platforms. Essential for setting up robust, low-latency, production-ready AI services.

Automated assistance for http client setup, offering best-practice guidance, code samples, and validation when integrating APIs, webhooks, SDKs, and OAuth services.

Automates Google Cloud IAM binding tasks by guiding users through best practices, generating production-ready code and configs, and validating outputs across compute, storage, BigQuery, Vertex AI, Cloud Run, Firebase, and other GCP services whenever iam binding creator is mentioned.

This solution implements robust Role-Based Access Control (RBAC) and multi-tenancy for third-party AI services. By utilizing an application proxy layer, it allows organizations to manage multiple teams, assign separate API keys, enforce granular content policies (e.g., filtering prohibited terms), and track daily budgets. This ensures secure, governed, and scalable resource usage in enterprise environments.

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.

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.