skills.rest
memohai
memohaiOFFICIAL
Explore all skills by this author →
GitHub

twilight-ai

Abstract provider-specific AI APIs into a unified Go SDK.

45|15|Updated Mar 17, 2026
Search Tags:#ai#llm#sdk#go#mcp#streaming#tool-calling
One-click install
npx skills add https://github.com/memohai/twilight-ai --skill twilight-ai-memohai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: twilight-ai
Source: https://github.com/memohai/twilight-ai/tree/main/skill
Command: npx skills add https://github.com/memohai/twilight-ai --skill twilight-ai-memohai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the integration of AI capabilities into Go applications by providing a unified, provider-agnostic SDK that handles complex tasks like tool calling, streaming, and multi-step execution.

Core Features & Use Cases

  • Unified API: Interact with OpenAI, Anthropic, Google, and other providers using a consistent set of Go interfaces.
  • Advanced AI Workflows: Implement multi-step tool calling, MCP integration, and typed channel-based streaming with minimal boilerplate.
  • Use Case: Use this Skill to build a coding agent that leverages Anthropic's thinking models or to create a RAG pipeline using Google's embedding models while maintaining the ability to swap backends without rewriting your core logic.

Quick Start

Use the twilight-ai skill to generate a text response using the openai completions provider with the gpt-4o-mini model.

Frequently Asked Questions about twilight-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI applications in Go that support multiple LLM providers?▼

You can implement type-safe streaming in Go by using an SDK that provides typed channel-based streaming, ensuring structured data flow for chat completions and multi-step execution workflows.

Does this Go LLM SDK support tool calling and multi-step execution workflows?▼

Yes, this Go LLM SDK supports advanced AI workflows including multi-step tool calling, enabling you to execute complex agentic logic and MCP integration with minimal boilerplate across various backends.

Can I use a single Go interface for chat completions and image generation across OpenAI and Anthropic?▼

Yes, you can use a single Go interface for chat completions and image generation across OpenAI and Anthropic, as the SDK provides a provider-agnostic configuration for multiple AI backends.

What is the best way to implement a RAG pipeline in Go using embedding models?▼

The best way to implement a RAG pipeline in Go is using a provider-agnostic SDK that handles embedding workflows, allowing you to integrate Google's embedding models while maintaining the ability to swap backends seamlessly.

Are there limitations when swapping AI backends for coding agents in Go?▼

When swapping AI backends for coding agents in Go, limitations depend on the specific features supported by each provider, though the unified SDK standardizes interfaces to minimize compatibility issues across providers.

Related Skills

Explore More Software Engineering Skills→
PostHogPostHog
58

instrument-integration

Instrument PostHog SDKs across popular web and mobile frameworks with verified configuration.

Official
Advanced
JhabbigJhabbig

langchain-architecture

Build LangChain and LangGraph applications with agents, memory, and tool integration.

Community
Advanced
0xPlaygrounds0xPlaygrounds
8.2k

rig

Build LLM-powered Rust applications with a unified API for providers and vector stores.

Official
Advanced
marcusmarcus
3

openrouter-python-streaming

Stream OpenRouter LLM responses in Python using LiteLLM with async streaming and retry logic.

Community
Intermediate