# Autoresearch: Autonomous Iteration for LLM-Powered Code Improvement

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Autoresearch is a powerful GitHub repository that transforms large language models like Claude Code, OpenCode, and OpenAI Codex into relentless improvement engines. Inspired by Karpathy's autoresearch, it enables autonomous, goal-directed iteration, following a "Modify, Verify, Keep/Discard, Repeat" loop to achieve compounding gains across various domains. This project offers a structured approach to automate development, security, and operational tasks with AI.

GitHub: https://github.com/uditgoenka/autoresearch
OSRepos URL: https://osrepos.com/repo/uditgoenka-autoresearch

## Summary

Autoresearch is a powerful GitHub repository that transforms large language models like Claude Code, OpenCode, and OpenAI Codex into relentless improvement engines. Inspired by Karpathy's autoresearch, it enables autonomous, goal-directed iteration, following a "Modify, Verify, Keep/Discard, Repeat" loop to achieve compounding gains across various domains. This project offers a structured approach to automate development, security, and operational tasks with AI.

## Topics

- ai
- autonomous-agent
- autoresearch
- claude
- github
- automation
- development
- productivity

## Repository Information

Last analyzed by OSRepos: Mon Aug 24 2026 20:41:31 GMT+0100 (Western European Summer Time)
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## Content

## Introduction

The `uditgoenka/autoresearch` repository introduces a groundbreaking skill for AI agents, particularly those powered by Claude Code, OpenCode, and OpenAI Codex. This project takes inspiration from Andrej Karpathy's original autoresearch concept, which demonstrated how a simple script could autonomously enhance machine learning models. Autoresearch generalizes this principle, turning any LLM into a "relentless improvement engine" capable of autonomous, goal-directed iteration. It operates on a core loop: modify, verify, keep/discard, and repeat, allowing for continuous, compounding gains in various tasks, not just limited to machine learning.

## Installation

Getting started with Autoresearch is straightforward, with options tailored for different platforms:

*   **For Claude Code (Recommended via npx):**
    bash
    npx skills add uditgoenka/autoresearch
    
    After installation, restart Claude Code to access all 14 commands.

*   **For OpenCode (Recommended via Guided Installer):**
    bash
    git clone https://github.com/uditgoenka/autoresearch.git
    cd autoresearch
    ./scripts/install.sh --opencode --global
    
    OpenCode commands use underscore naming, e.g., `/autoresearch_debug`.

*   **For OpenAI Codex (Recommended via Guided Installer):**
    bash
    git clone https://github.com/uditgoenka/autoresearch.git
    cd autoresearch
    ./scripts/install.sh --codex --global
    
    Codex commands are invoked using mention syntax, e.g., `$autoresearch debug`.

Detailed instructions and alternative manual installation methods are available in the official repository's README.

## Examples

Autoresearch offers a comprehensive suite of commands designed to automate various development and operational tasks. Here are a few highlights:

*   **`/autoresearch` (Core Loop / Orchestrator):** The central command. In its classic mode, you define a goal, metric, and verification step, and it iteratively modifies code to improve the metric. The new autonomous orchestrator mode allows you to simply state a plain-language goal, and it intelligently selects and chains subcommands until the goal is met.
    
    /autoresearch
    Goal: Increase test coverage from 72% to 90%
    Scope: src/**/*.test.ts, src/**/*.ts
    Metric: coverage % (higher is better)
    Verify: npm test -- --coverage | grep "All files"
    Iterations: 25
    
    Or, using the orchestrator:
    
    /autoresearch fix the login bug
    

*   **`/autoresearch:plan`:** Helps define the crucial Goal, Scope, Metric, and Verify steps by converting a plain-language goal into a validated configuration.
    
    /autoresearch:plan
    Goal: Make the API respond faster
    

*   **`/autoresearch:debug`:** An autonomous bug hunter that iteratively finds and classifies bugs using falsifiable hypotheses. It can be chained with `/autoresearch:fix`.
    
    /autoresearch:debug
    Scope: src/api/**/*.ts
    Symptom: API returns 500 on POST /users
    Iterations: 15
    

*   **`/autoresearch:fix`:** Designed to crush errors one-by-one, automatically detecting and prioritizing broken tests, types, or linting issues until the error count hits zero.
    
    /autoresearch:fix
    Iterations: 20
    

*   **`/autoresearch:security`:** Performs a read-only security audit using STRIDE threat modeling, OWASP Top 10 sweeps, and red-team adversarial analysis.
    
    /autoresearch:security
    Iterations: 15
    

*   **`/autoresearch:ship`:** A universal shipping workflow that guides any release (code, deployment, content) through 8 phases, with mechanically verifiable checklists.
    
    /autoresearch:ship --auto
    

The project emphasizes "8 Critical Rules" for effective autonomous iteration, including bounded iterations, read-before-write, one change per iteration, mechanical verification, automatic rollback, and using Git as memory.

## Why Use It

Autoresearch stands out by providing a robust framework for leveraging LLMs to achieve continuous, measurable improvements. Its key advantages include:

*   **Autonomous Improvement**: It automates the tedious cycle of modifying, testing, and refining, allowing agents to work tirelessly towards a defined goal.
*   **Versatility**: Applicable across virtually any domain, from code optimization and bug fixing to security audits, documentation generation, and even product research, as long as a measurable metric or verifiable predicate can be established.
*   **Efficiency**: The v2.1.0 architecture rebuild significantly reduced token usage, making operations more cost-effective and faster.
*   **Safety and Reliability**: Features like automatic rollback, bounded iterations, and comprehensive hooks (e.g., `privacy-block`, `dangerous-cmd-block`) ensure that autonomous changes are controlled and safe.
*   **Structured Workflow**: It provides a clear, rule-based approach to problem-solving, ensuring that progress is systematic and traceable through detailed TSV logs and Git commits.

## Links

*   **GitHub Repository**: [https://github.com/uditgoenka/autoresearch](https://github.com/uditgoenka/autoresearch){:target="_blank"}
*   **Author's Website**: [https://udit.co](https://udit.co){:target="_blank"}
*   **Karpathy's Autoresearch (Inspiration)**: [https://github.com/karpathy/autoresearch](https://github.com/karpathy/autoresearch){:target="_blank"}
*   **Claude Code Documentation**: [https://docs.anthropic.com/en/docs/claude-code](https://docs.anthropic.com/en/docs/claude-code){:target="_blank"}
*   **OpenCode**: [https://opencode.ai](https://opencode.ai){:target="_blank"}
*   **OpenAI Codex**: [https://developers.openai.com/codex](https://developers.openai.com/codex){:target="_blank"}