Skill UI

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.

This guide provides comprehensive instructions for setting up and authenticating the Together AI SDK. It covers installation using both Python and Node.js, configuring the necessary API key, and verifying connectivity to utilize the OpenAI-compatible API for powerful open-source model inference and fine-tuning tasks. Ideal for developers starting with Together AI.

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.

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.

A comprehensive guide to implementing production-ready patterns for interacting with the Together AI API. This includes a singleton client for seamless OpenAI-compatible integration, robust error wrappers with retry logic, and a fluent request builder. It covers best practices for LLM inference, model setup, and handling diverse response types (chat, embedding, fine-tuning).

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

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.

This utility handles asynchronous webhook events from Together AI, enabling developers to build robust, event-driven Machine Learning pipelines. Instead of constantly polling the API for status updates (e.g., fine-tuning completion, batch job status, model deprecation), you can subscribe to these webhooks. It includes logic for signature verification, event routing, and idempotency, making it ideal for mission-critical AI workflows.

A comprehensive guide for developers on integrating and tuning with Veeva Vault using its REST API, VQL (Veeva Query Language), and VAPIL Java SDK. This skill covers authentication methods, common data patterns, document lifecycle management, and detailed error handling, making it essential for clinical operations and document management in life sciences.

This guide provides comprehensive instructions for optimizing performance when interacting with Veeva Vault. It covers best practices for using the REST API, writing efficient VQL queries, and utilizing the VAPIL Java SDK. Ideal for developers working with Veeva Vault for clinical operations, document management, and CRM integration.

A comprehensive guide to optimizing Webflow usage costs. This skill teaches advanced strategies for reducing API expenses by prioritizing CDN-cached reads (free and unlimited), implementing bulk endpoint operations, caching collection schemas, and replacing inefficient polling with Webhook-driven updates. It also provides methods for API usage monitoring and recommending the optimal site plan.

This guide provides advanced strategies for optimizing Webflow Data API v2 performance. Learn how to mitigate rate limits and reduce latency by utilizing CDN-cached live item reads, implementing application-level or Redis caching, and leveraging bulk endpoints for efficient data writes. Essential for high-traffic, data-intensive Webflow projects that require robust scaling.