Skill UI

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.

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 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 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.

This tool executes the core Webflow workflow by managing CMS collections and items through the Data API v2. It enables the full CRUD lifecycle—create, read, update, delete, and publish dynamic content like blog posts, team profiles, or product listings programmatically. Use this when integrating with Webflow CMS to automate content management and publishing tasks.

This API module provides comprehensive tools and abstractions for managing scheduled actions within the webiny ecosystem. It covers the entire lifecycle of asynchronous tasks, including scheduling actions for future execution, retrieving details, listing existing tasks, executing them immediately, and canceling them. Key components include dedicated Use Cases and a core SchedulerService, enabling robust background processing and reliable task management for enterprise applications.

This module provides core abstractions for handling long-running, asynchronous background tasks within an API context. It introduces `TaskDefinition` to define the complete lifecycle and hooks for a background task, and `TaskService` to programmatically trigger and manage these operations. It is essential for backend development requiring robust, non-instantaneous processing.

A powerful tool for executing complex, multi-step, and multi-agent workflows defined in YAML format directly within the AI session. It acts as an orchestration engine, simulating various roles (agents) to complete tasks sequentially or in parallel, without requiring external API keys or configuration. Ideal for complex business process automation and multi-role content generation.

This skill is designed for tackling complex, multi-step software development tasks. It generates highly detailed, structured implementation plans that strictly adhere to best practices like TDD, YAGNI, and DRY. The plan breaks down features into small, atomic, executable tasks, specifying required file changes, writing initial failing tests, implementing minimum necessary code, and committing. It ensures the resulting plan is ready for efficient, autonomous execution by AI agents or human teams.

The official skill for the XcodeBuildMCP CLI, providing a unified interface for complex Apple development tasks. It streamlines build, test, run, debug, and UI automation across iOS, macOS, watchOS, tvOS, and visionOS. Use this tool to simplify multi-stage workflows and manage project lifecycles without relying on raw, complex xcodebuild commands.