Skill UI

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

This skill provides explicit operations for managing durable OpenViking memories and resources. Users can `recall` past decisions or preferences, `read` linked `viking://` URIs to expand context, or `remember` information for explicit retention. It is essential for maintaining accurate context in advanced AI workflows.

EverOS OpenClaw Plugin integrates persistent, long-term memory capabilities into OpenClaw using the ContextEngine API. It goes beyond simple memory slots by automatically recalling relevant past context before a reply and saving new conversation content after a turn. This allows users to chat naturally while ensuring context, preferences, and key details persist across multiple sessions, eliminating the need for manual memory calls.

This skill generates and edits high-quality images by utilizing various advanced AI models (including Gemini, FLUX, GPT-Image, and Seedream) through the OpenRouter API. It is ideal for creating photorealistic artwork, concept art, logos, detailed illustrations, or performing complex image compositing and editing using reference images. Supports diverse parameters like resolution, seed, and aspect ratio for precise creative control.

This skill integrates LangChain 1.0 and LangGraph 1.0 into standard OpenTelemetry (OTEL) backends (e.g., Jaeger, Honeycomb, Tempo). It solves critical observability gaps by ensuring correct prompt content capture, propagating spans across complex subgraphs, and providing five LLM-specific Service Level Objectives (SLOs) like latency and cost. Ideal for multi-tenant, compliance-sensitive, or existing OTEL stack environments where LangSmith is unsuitable.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

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 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.

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.