Skill UI

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.

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.

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.

This skill provides a comprehensive framework for customizing and managing AWS cloud infrastructure using Pulumi handlers and declarative components. Developers can configure regions, apply resource tags, manage OpenSearch, set up VPC networks, deploy custom domains, and implement advanced blue-green deployment strategies. It fully supports environment-conditional configuration, ensuring robust infrastructure deployment across dev, staging, and production environments.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

A comprehensive tool for generating OpenAPI/Swagger documentation for Go (Golang) APIs. Developers annotate Go code comments with tags (@Summary, @Param, @Router, etc.) to define API contracts. The tool generates the full specification and provides integration handlers for major Go frameworks (Gin, Echo, Fiber), making the Swagger UI the single source of truth for API consumers. Ideal for maintaining robust, documented backend services.

A comprehensive methodology for executing external Open Source Intelligence (OSINT) reconnaissance and authorized red-team operations. It guides users through a multi-stage attack surface assessment pipeline, covering asset discovery, confidence scoring, vulnerability prioritization, breach correlation, and structured client reporting for professional security assessments.

Search over 2500 curated open-source repositories related to ChatGPT, LLMs, RAG, agents, and AI development. This skill helps users find tools, libraries, and frameworks by filtering categories, languages, or keywords. It relies on a bundled database rather than live web searches.