autoresearch

Name: autoresearch
Availability: InStock
Author: sergiocoding96

Implement autonomous self-improving AI research loops with configurable experiments and policy updates.

13|Updated Apr 5, 2026

Search Tags:#agent workflows #autoresearch #autonomous research #self-improving agents #ai research loops #test-time compute #karpathy framework

Ready to run

npx skills add https://github.com/sergiocoding96/hermes-multi-agent --skill autoresearch-sergiocoding96

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/sergiocoding96/hermes-multi-agent/tree/main/skills/autoresearch
Command: npx skills add https://github.com/sergiocoding96/hermes-multi-agent --skill autoresearch-sergiocoding96

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the manual effort of building and maintaining autonomous AI research agents that can iteratively improve their performance without human intervention, streamlining the development of self-optimizing research systems.

Core Features & Use Cases

Autoresearch Framework Implementation: Implements Karpathy's self-improving agent loop for autonomous experiment execution, result analysis, and policy updates.
Flexible Configuration: Supports custom search spaces, reward signals, and policy types for math, code, and reasoning research tasks.
Use Case: For example, you can use this Skill to run autonomous math reasoning experiments that iterate on their approach to improve accuracy on benchmarks like GSM8K without manual tuning.

Quick Start

Use the autoresearch skill to implement an autonomous self-improving research loop for your target AI task, including environment setup, search space configuration, and iterative experiment execution.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

What is a self-improving AI research agent loop?▼

A self-improving AI research agent loop autonomously executes experiments, analyzes results, and updates policies without human intervention. This Skill implements that exact mechanism to enable continuous optimization across math, code, and reasoning tasks.

How do I set up an autonomous research agent for math reasoning tasks?▼

To set up an autonomous research agent, you must configure the experiment environment, define the search space, and set reward signals. This Skill provides the functional requirements to iteratively execute math reasoning experiments and improve benchmark accuracy automatically.

Can I use this framework for test-time compute scaling in agent workflows?▼

Yes, you can use this framework for test-time compute scaling in agent workflows. It applies autonomous self-improving research loops to dynamically optimize AI system performance and scale computational resources during reasoning and execution phases.

Does this autonomous research loop require manual tuning for benchmark experiments?▼

No, this autonomous research loop does not require manual tuning for benchmark experiments. It iteratively analyzes results and updates policies automatically, allowing AI agents to independently improve their accuracy on benchmarks like GSM8K.

How do I configure custom search spaces and reward signals for AI research?▼

You configure custom search spaces and reward signals by defining your target policy types and environment parameters. This Skill supports flexible configuration to tailor autonomous experiment execution and result analysis for specific math, code, or reasoning research tasks.

Does this autonomous agent framework work with multi-agent orchestration systems?▼

Yes, this autonomous agent framework integrates with Hermes multi-agent orchestration systems. It satisfies functional requirements for experiment environment setup and search space definition while operating within broader multi-agent workflow architectures.