Skill UI

This skill provides guidance and patterns for generating structured JSON files that define complex workflow pipelines. It is essential for chaining multiple distinct AI models together, enabling multi-step, automated data processing. Use this when a single task requires a sequence of interconnected AI operations or services.

This guide provides minimal working examples demonstrating the LangChain Expression Language (LCEL). It shows how to chain together diverse components—such as prompts, LLMs, and output parsers—using simple pipe syntax (e.g., .pipe() or |). Ideal for developers starting a new LangChain project, understanding core AI workflow patterns, or mastering structured data output.

This guide details how to use Shopify Metafields and Metaobjects via the GraphQL Admin API. Metafields allow attaching key-value custom data to existing resources (like products or orders), while Metaobjects enable defining entirely new, structured content types. Together, they provide a powerful way for developers to model complex custom data, often eliminating the need for external databases to extend e-commerce functionality.

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 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 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.

A comprehensive guide and implementation demonstrating how to handle API rate limits when interacting with AI services like Together AI. It details the token bucket algorithm for client-side throttling and provides robust retry mechanisms (exponential backoff) to manage 429 (Rate Limit Exceeded) and 5xx server errors, ensuring stable, high-throughput batch processing.

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.