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.

The Hugging Face Hub CLI (`hf`) is a comprehensive command-line tool for managing the entire lifecycle of AI assets. It allows users to download models, upload datasets, synchronize files to buckets, manage authentication tokens, and interact with collections and discussions. This tool is essential for automating MLOps workflows and managing resources directly from the terminal.

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 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.

Provides structured guidance and resources for writing any internal communication, from 3P updates and newsletters to FAQs, incident reports, and leadership summaries, matching the formats preferred by the organization.

This comprehensive guide addresses common technical and business logic errors encountered when integrating with the Juicebox API. It covers HTTP status codes (e.g., 401, 403, 429), including authentication failures, quota exhaustion, rate limiting, data validation issues, and analysis timeouts. The resource provides detailed causes, specific fixes, and best practices for building reliable and robust integrations into recruiting and research workflows.

A comprehensive guide and expert resource for mastering Julia 1.10+ development. Covers advanced topics including multiple dispatch, performance optimization, type stability, and modern tooling. Ideal for building production-ready applications in scientific computing, machine learning, data science, and web development.

This skill provides step-by-step guidance for creating production-ready Kubernetes manifests. It covers essential resources including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Use this skill when defining complete, robust containerized application architectures, managing network connectivity, stateful workloads, and ensuring adherence to Kubernetes best practices like resource limits and security contexts.

This guide provides a comprehensive reference for diagnosing and resolving common errors encountered when interacting with the Kling AI API. It covers various HTTP status codes (e.g., 400, 401, 429), task-level failures (e.g., content policy violations, low image quality), proper JWT token management, and implementing rate limit handling with exponential backoff. Use this resource when troubleshooting failed video generation or API integration issues.

This utility provides robust cost control mechanisms for integrating AI generation APIs. It implements daily budget enforcement, per-request cost estimation, and usage alerting. Developers can use it to prevent unexpected overspending and effectively manage credits and resources when building AI-powered applications.

This skill provides a comprehensive guide to the credit-based pricing model of Kling AI. It details costs for video generation (Standard/Professional modes), image creation (Kolors), and API resource packs. Users can utilize this guide to accurately budget, estimate total project costs, and implement cost-saving strategies, ensuring the efficient and economical execution of their AI content generation workflow.

This tool provides comprehensive usage analytics and reporting for Kling AI video generation. It logs detailed events (submission, completion, errors) into structured JSONL files. Users can track performance metrics, analyze success rates, monitor total resource consumption (credits), and generate cost summaries over defined periods. Ideal for building dashboards, cost auditing, and understanding usage patterns.