Autoresearch — Autonomous Goal-directed Iteration

Autonomous iteration loop: modify, verify, keep/discard against any metric

概览

Autoresearch is a research-focused AI agent skill available within the mxyhi/ok-skills repository. It facilitates an autonomous iteration loop designed to refine outputs based on user-defined metrics. The process involves a continuous cycle of modification and verification, where the agent evaluates changes and decides whether to retain or discard them based on performance against a specific goal. Compatible with agents like Claude-code, Cursor, and OpenClaw, this tool streamlines complex workflows in fields such as security and ROS (Robot Operating System). By leveraging the broader popularity of the mxyhi/ok-skills project, which has garnered 423 stars on GitHub, Autoresearch provides a structured framework for goal-directed experimentation and systematic improvement without requiring manual intervention at every step.

使用场景

Automated code optimization by iterating through modifications and verifying performance gains.
Security vulnerability research through systematic testing and verification of potential patches.
Refining Robot Operating System (ROS) configurations by testing parameters against specific operational metrics.

安装说明

# Review source first
open https://github.com/mxyhi/ok-skills/blob/main/autoresearch/SKILL.md

Copy or clone the skill folder into your agent skills directory after reviewing its instructions and scripts.

安全提示

As a tool designed for autonomous iteration and verification, users should ensure the agent operates within a sandboxed environment, especially when testing security patches or ROS configurations. Reviewing the specific implementation details in the mxyhi/ok-skills repository is recommended to understand how the skill handles system permissions during the modification and verification cycles.

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