llm-inference

Generate text and summaries via OpenAI-compatible LLM inference endpoints.

8|Updated Oct 24, 2025

Search Tags:#ai #llm #model selection #natural language processing #inference #text generation

Ready to run

npx skills add https://github.com/dave1010/tools --skill llm-inference

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-inference
Source: https://github.com/dave1010/tools/tree/main/.agents/skills/llm-inference
Command: npx skills add https://github.com/dave1010/tools --skill llm-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a unified interface to interact with various Large Language Models (LLMs), simplifying the process of leveraging AI for diverse text-based tasks.

Core Features & Use Cases

Access to Multiple Models: Supports a range of LLMs including Cerebras, Llama, Qwen, and Zai-GLM, each with different strengths and token limits.
Flexible Inference: Can be used for standard chat interactions or for arbitrary string processing, allowing for custom formatting and output requirements.
Use Case: You need to summarize a long document. Use this Skill to send the document text to the gpt-oss-120b model for a concise summary.

Quick Start

Use the llm-inference skill to generate a poem about the ocean using the llama3.1-8b model.

Frequently Asked Questions about llm-inference

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

FAQPage Schema

How do I interact with multiple LLMs using a single inference endpoint?▼

You can interact with various LLMs through a unified OpenAI-compatible inference endpoint, enabling text generation and arbitrary string manipulation across models like Llama, Qwen, and Cerebras using a consistent API format.

What is the best way to select an LLM for summarizing a long document?▼

The best way to summarize a long document is routing the text to a high-capacity model like gpt-oss-120b via the inference endpoint, ensuring the chosen LLM has the context length and token limits required for your input.

Can I use this for arbitrary string processing and custom text formatting?▼

Yes, you can use this for arbitrary string processing and custom formatting. The inference endpoint supports flexible text generation, allowing you to define custom output requirements and manipulate arbitrary strings programmatically.

Does the OpenAI-compatible inference endpoint support Llama and Qwen models?▼

Yes, the OpenAI-compatible inference endpoint supports Llama and Qwen models. It provides a standardized interface to access these various LLMs, enabling text generation and processing based on your specific model selection.

When do I need to consider token limits and context length for LLM inference?▼

You need to consider token limits and context length during LLM inference when processing large text inputs. Choosing a model with insufficient limits will truncate input, directly affecting your text generation and summarization results.