Skill UI

This mode acts as a structured thinking partner, guiding users through ambiguous problems, complex decisions, and open-ended strategy questions. Instead of providing direct answers, it focuses on challenging assumptions, asking clarifying questions, surfacing multiple perspectives (e.g., stakeholder views), and helping the user structure their thought process using advanced techniques like reframing, pre-mortem analysis, and identifying critical constraints. Ideal for product managers and leaders who need to explore ideas without a clear path forward.

This skill takes large plans, specifications, or Product Requirement Documents (PRDs) and systematically breaks them down into small, independently verifiable, and actionable GitHub issues. Utilizing the 'vertical slice' (tracer bullet) methodology, it ensures that each generated task cuts through all integration layers (UI, API, Schema, Tests) end-to-end. It manages dependencies, identifies required human interaction (HITL) versus autonomous implementation (AFK), and prepares a structured, executable backlog ready for immediate ticketing.

This skill synthesizes a comprehensive Product Requirements Document (PRD) from the current conversation context and understanding of the codebase. It guides the user through structuring the problem statement, defining detailed user stories, outlining implementation decisions, and suggesting test strategies. The final PRD is formatted and submitted directly as a GitHub issue, ensuring a structured handover from discussion to development specification.

Automate the Continuous Integration and Continuous Deployment (CI/CD) workflow for applications leveraging Together AI's OpenAI-compatible API. This skill sets up comprehensive GitHub Actions workflows to run unit tests (mocking completions and embeddings) on every Pull Request, and full integration tests (live API calls) upon merging to the main branch. It ensures robust validation of prompt formatting, model selection, and API connectivity for various LLMs.

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.

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.

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.