Skill UI

A comprehensive reference guide for common errors encountered when interacting with the Together AI API. This includes error codes and solutions for inference, fine-tuning, and model deployment, covering authentication, rate limits, validation failures, and billing issues. Essential for developers integrating OpenAI-compatible LLM services.

A comprehensive guide to optimizing operating costs when utilizing Together AI's OpenAI-compatible API. This resource details strategies for cost reduction across various AI tasks, including inference, fine-tuning, and model deployment. Learn how to select appropriate model sizes, implement caching, leverage batch inference, and utilize Turbo variants to maximize efficiency and minimize spending.

This debug bundle provides comprehensive guidance for working with Together AI's OpenAI-compatible API. It supports critical LLM lifecycle tasks, including inference, fine-tuning, and model deployment for over 100 open-source models (e.g., Llama, Mixtral). It also includes detailed troubleshooting steps and solutions for common API errors (like 401 Unauthorized or 429 Rate limit), ensuring smooth development.

This skill provides a production-grade, containerized integration service for connecting to the Together AI platform. It facilitates high-performance inference, fine-tuning, and model deployment across over 100 open-source models using Together's OpenAI-compatible API. Includes comprehensive Docker setup, environment variable management, health checks, and zero-downtime rolling update strategies for robust, real-time LLM serving.

Provides comprehensive guidance for performance tuning, inference, and fine-tuning using the Together AI platform. This skill supports working with Together AI's OpenAI-compatible API, covering model deployment, utilizing various open-source models (like Llama, Mixtral), and implementing best practices for efficient and cost-effective batch inference.

This comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

This guide provides comprehensive strategies for migrating applications using the Together AI platform. It addresses common issues like model ID deprecations, API schema changes (for fine-tuning or chat completions), and best practices for robust inference and deployment using their OpenAI-compatible API.

Provides comprehensive, enterprise-grade patterns for setting up and managing Veeva Vault across multiple environments. This guide is essential for implementing advanced, compliant deployment strategies in the regulated life sciences sector, covering API usage (VQL, VAPIL) and best practices for data validation and security.

A comprehensive guide and workflow for deploying static websites (e.g., Vue, React, Angular, Hugo, Jekyll) from a local environment to a production server using VSCode SFTP. This skill covers setting up secure SFTP connections, generating production-ready Nginx configurations (including security headers, caching, and HTTPS redirects), and providing a complete deployment checklist for smooth CI/CD-like workflows.

A comprehensive guide to setting up continuous integration and continuous deployment (CI/CD) pipelines for Webflow Data API v2 integrations using GitHub Actions. This setup automates unit testing (mocked SDK), robust integration testing with real API tokens, CMS schema validation, and reliable publish-on-merge workflows, ensuring code quality and consistency in your Webflow projects.

This skill provides comprehensive API access to manage all aspects of a Webflow project. It covers site operations (publishing, listing), page metadata (SEO, Open Graph), handling form submissions, and full e-commerce functionality (products, SKUs, inventory, orders). Use this when automating site deployments, extracting form data, or managing product catalogs programmatically via the Data API v2.