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 workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.

This guide provides a comprehensive tutorial on running various AI inference tasks using the Together AI API, which is compatible with the OpenAI standard. It demonstrates key functionalities including chat completions, streaming responses, image generation, and creating embeddings. This is ideal for developers who need to test open-source models, compare performance across different LLMs (like Llama or Mixtral), or integrate powerful generative AI features into their applications using Python or Node.js.

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.

This tool provides a local development loop for integrating with Together AI's OpenAI-compatible API. It mocks key functionalities like chat completions, embeddings, and model listing, allowing developers to build and test AI applications rapidly without consuming live API credits. Developers can toggle between mock mode for rapid iteration and live mode for final model evaluation, ensuring a seamless workflow.

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.

This guide outlines best practices and structured workflows for automating complex tasks within the Unity Editor using the Model Context Protocol (MCP). It provides programmatic methods for managing GameObjects, applying script edits, analyzing the editor state, executing batch commands, and capturing detailed, multi-view screenshots. Ideal for developers needing robust, repeatable, and automated testing or operational tasks within Unity projects.

Comprehensive guidance for integrating with the Veeva Vault platform. This skill covers using the REST API, executing VQL queries, and utilizing the VAPIL Java SDK for managing documents and automating clinical operations. Ideal for developers working in the life sciences and pharmaceutical industry who need deep access to Veeva Vault data and workflows.

This guide provides essential knowledge for handling common errors encountered when integrating with Veeva Vault. It covers REST API usage, VQL querying patterns, and VAPIL Java SDK concepts, helping developers manage document lifecycles and resolve issues like invalid sessions, insufficient permissions, or data validation conflicts in a life sciences CRM environment.

Provides comprehensive guidance for implementing core workflows within Veeva Vault. This skill covers using the REST API, performing VQL queries for data retrieval, and utilizing the VAPIL Java SDK. It is essential for developers working on document management, clinical operations, and CRM integrations within the life sciences sector.

A comprehensive guide to implementing core workflows within Veeva Vault, covering REST API interaction, VQL query language patterns, and the VAPIL Java SDK. This skill is essential for developers working with Veeva Vault's document management, CRM, and clinical operations processes, detailing authentication, object querying, and state lifecycle management.

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.