Skill UI

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.

This comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

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.

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.

TweetClaw provides comprehensive automation capabilities for X/Twitter using the Xquik REST API. It allows users to perform full read and write operations—including posting, replying, liking, following, and sending DMs. Key features include advanced tweet searching, bulk data scraping (23 tools), account monitoring, running structured giveaways, and composing algorithm-optimized content. Ideal for social media managers, digital marketers, and data analysts needing deep platform insights.

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.