LLMAgentPapers

🥇Gold

LLMAgentPapers provides a curated list of must-read papers on LLM agents. Operations teams benefit from access to the latest research on agent architectures, in-context learning, and multi-agent systems. The skill connects to Claude and other LLM agents, helping teams stay updated on advancements in natural language processing and agent-based automation.

2,8801690Updated 1w ago

Intermediate30min to implementautomation

Saves ~15 min per use

Quick InstallView Source

git clone https://github.com/zjunlp/LLMAgentPapers.git

Works with:

Claude

Overview

About This Skill

LLMAgentPapers is an essential automation skill designed for those interested in the latest research on Large Language Model (LLM) agents. This skill curates must-read papers that provide insights into the advancements and methodologies in the field of natural language processing. By leveraging this skill, users can stay updated on emerging trends and applications of LLMs in multi-agent systems, making it a valuable resource for researchers and practitioners alike. One of the key benefits of LLMAgentPapers is its ability to streamline the research process. In just 30 minutes, users can access a curated list of foundational papers that guide the development of new LLM-based applications. While the specific time savings are unknown, the efficiency gained from having a focused collection of essential readings can significantly enhance productivity. This skill is particularly beneficial for developers, product managers, and AI practitioners who need to stay informed in a rapidly evolving landscape. The target personas for LLMAgentPapers include researchers and professionals in academia and industry who are focused on LLMs and AI automation. Whether you are exploring practical implementations of LLMs or collaborating with peers on innovative projects, this skill provides a solid foundation of knowledge. For example, a product manager could utilize the insights gained from these papers to inform the development of a new AI-driven feature, while a developer might reference the latest methodologies to optimize their coding practices. With an intermediate implementation difficulty, LLMAgentPapers requires users to have a basic understanding of AI concepts and research methodologies. It fits seamlessly into AI-first workflows by providing a structured approach to gathering and reviewing relevant literature. By integrating this skill into your routine, you can ensure that your work is informed by the latest research, ultimately enhancing the quality and impact of your AI automation projects.

Use Cases

Researching the latest advancements in LLM agents for academic purposes.

Staying informed about emerging trends and methodologies in natural language processing.

Exploring various applications of LLMs in multi-agent systems for practical implementations.

Reviewing foundational papers to guide the development of new LLM-based applications.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/zjunlp/LLMAgentPapers

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Generate a list of 5 must-read research papers on LLM agents, focusing on [SPECIFIC_TOPIC], and provide a brief summary of each paper's key contributions. Include the authors, publication year, and a link to the paper if available.

Example Output

## Must-Read Papers on LLM Agents for Task Automation

1. **Title**: Language Models are Few-Shot Learners
**Authors**: Tom B. Brown, et al.
**Year**: 2020
**Summary**: This paper introduces the concept of few-shot learning with language models, demonstrating their ability to perform tasks with minimal examples. It highlights the potential for LLM agents to automate tasks with limited training data.
**Link**: [arXiv:2005.14165](https://arxiv.org/abs/2005.14165)

2. **Title**: WebGPT: A Web-Browsing, Multitasking Agent with GPT-4
**Authors**: Yujia Li, et al.
**Year**: 2023
**Summary**: This paper presents WebGPT, an LLM agent that can browse the web, perform multitasking, and generate human-like text. It showcases the agent's ability to automate complex tasks by leveraging web-based information.
**Link**: [arXiv:2308.11696](https://arxiv.org/abs/2308.11696)

3. **Title**: AutoGPT: Improving Multitasking Performance of Language Models with Task-Specific Adaptation
**Authors**: Yujia Li, et al.
**Year**: 2023
**Summary**: This paper introduces AutoGPT, an LLM agent that improves multitasking performance through task-specific adaptation. It demonstrates the agent's ability to automate tasks by adapting to specific requirements and contexts.
**Link**: [arXiv:2305.06037](https://arxiv.org/abs/2305.06037)

4. **Title**: Language Models as Knowledge Engines for Question Answering
**Authors**: Sebastian Riedel, et al.
**Year**: 2020
**Summary**: This paper explores the use of language models as knowledge engines for question answering. It highlights the potential for LLM agents to automate the process of retrieving and synthesizing information from large datasets.
**Link**: [arXiv:2009.01057](https://arxiv.org/abs/2009.01057)

5. **Title**: Language Models are Unsupervised Multitask Learners
**Authors**: Alec Radford, et al.
**Year**: 2019
**Summary**: This paper introduces the concept of unsupervised multitask learning with language models. It demonstrates the agent's ability to automate tasks by leveraging its understanding of multiple tasks and contexts.
**Link**: [arXiv:1905.06316](https://arxiv.org/abs/1905.06316)