Skill UI

Provides a comprehensive guide and Python implementation for creating container-based hosted agents in Azure AI Projects. It utilizes ImageBasedHostedAgentDefinition to define the agent's container image, required resources (CPU/memory), supported tools, and communication protocols. Essential for deploying robust, production-ready AI agents within the Azure ecosystem.

Map web inputs for reflected or stored HTML injection, send crafted tags via URLs, POST bodies, or visible fields, and document appearance/phishing manipulations for authorized security assessments.

An expert architect specializing in designing, implementing, and managing complex hybrid and multi-cloud environments. Expertise spans major public clouds (AWS, Azure, GCP), private clouds (OpenStack, VMware), and container platforms (Kubernetes). Focus areas include cross-cloud networking, Infrastructure as Code (IaC) using Terraform, workload optimization, zero-trust security implementation, and FinOps cost management.

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 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 utility designed to collect all necessary diagnostic evidence for troubleshooting Ideogram API interactions. It gathers environment details (Node, Python, OS), performs API connectivity tests, captures network information, and packages the results into a redacted tarball suitable for attaching to technical support tickets, ensuring all sensitive data is masked.

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 runbook for rapid incident response concerning Ideogram API outages, authentication failures, and performance degradation. It provides detailed triage scripts, decision flows based on HTTP status codes (401, 429, 5xx), immediate mitigation actions (e.g., enabling fallback mode), and structured templates for communication and postmortems.

A comprehensive guide to implementing robust observability for Ideogram AI image generation APIs. Learn how to track critical metrics such as generation duration (latency), estimated API costs, error rates, and content safety rejections. This skill involves instrumenting API calls using TypeScript, integrating cost estimation, and setting up Prometheus alerting rules to ensure stable and reliable AI operation.

This skill guide provides advanced techniques for optimizing Ideogram API usage. It covers selecting optimal model tiers (e.g., Turbo vs. Standard) based on desired quality and speed, implementing prompt-based caching to reduce API calls and costs, and managing parallel generation with concurrency limits for high throughput. Additionally, it demonstrates CDN integration for rapid asset delivery.

This skill guides developers on building scalable, event-driven pipelines around Ideogram's synchronous API. It demonstrates implementing robust async patterns, including using job queues (like BullMQ) for batch generation, handling webhook callbacks for post-processing, and setting up full asset modification pipelines. Ideal for production-grade marketing and content workflows.

A comprehensive framework for managing availability and reliability incidents (outages, degradations, failed deploys). It guides the entire lifecycle, from automated severity classification (SEV1-SEV4) and timeline reconstruction to multi-team coordination and structured post-incident review (PIR). Essential for SRE and DevOps teams leading major incident responses.