Skill UI

Automates guidance and code generation for managing Helm values within advanced DevOps workflows, including Kubernetes, Terraform, CI/CD, monitoring, and infrastructure-as-code best practices.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

Automates AWS IAM role generation by guiding requests, applying best practices, and producing production-ready code/configs with validation for compute, storage, networking, and serverless workloads.

This comprehensive guide serves as a quick reference for diagnosing and resolving common Ideogram API errors and exceptions. It covers critical issues such as authentication failures (401), rate limiting (429), safety check failures (422), and incorrect parameter formats (400). Use this resource when debugging failed image generation requests or integrating Ideogram functionality into an application.

This workflow executes the primary Ideogram API calls for advanced text-to-image generation. Ideogram excels at rendering legible text within images, a crucial capability for creating realistic signs, menus, or branding elements. Use this for generating high-quality social media graphics, marketing banners, product mockups, and posters that require accurate and stylized typography combined with illustration.

This skill provides advanced secondary workflows for Ideogram, enabling comprehensive image manipulation beyond simple text-to-image generation. Key functions include Magic Fill (inpainting/editing), Remix (style transfer), Upscaling, describing image content (image-to-text), and re-framing. Ideal for professional image refinement, style transfer, and content enhancement.

This skill provides comprehensive methods to significantly reduce expenditure when using the Ideogram API for image generation. Techniques include implementing two-phase generation workflows (drafting with cheap models, finalizing with premium models), batching multiple images in a single API call, caching identical prompts, and setting up robust budget tracking alerts. Ideal for developers managing large-scale creative AI projects.

A comprehensive skill designed to manage, track, and persist image assets generated by Ideogram's API. This tool addresses the critical issue of expiring image URLs by implementing a complete lifecycle pipeline. It handles metadata tracking (prompt, seed, model, style, etc.), immediate downloading, and robust storage to both local filesystems and cloud solutions like S3. Features include asset persistence, generation history logging, and image reproduction using stored seed values.

This comprehensive guide provides a multi-platform deployment solution for Ideogram-powered image generation APIs. It includes detailed code and setup steps for Vercel, Google Cloud Run, and self-hosted Docker containers. The solution addresses key production concerns, including secure secret management (API keys), handling generation timeouts, and ensuring generated images are persisted to durable cloud storage (like S3) with CDN integration for reliable service operation. Use this when migrating Ideogram prototypes to a production environment.

A comprehensive guide demonstrating how to generate your first AI image using the Ideogram API. It provides runnable examples in cURL, TypeScript, and Python, covering both legacy and V3 endpoints. Users will learn essential API parameters, such as prompt structuring, style types, and aspect ratios, making it ideal for initial integration testing and setup.

A comprehensive guide to setting up and authenticating the Ideogram API for AI image generation. This skill covers obtaining the necessary API key, configuring environment variables, and providing verifiable code snippets (Python/TypeScript) to establish a successful connection. It is essential for integrating image generation, editing, and remixing capabilities into any development project.

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.