autoresearch

Name: autoresearch
Availability: InStock
Author: chrisliu298

Automate autonomous experimentation loops with versioned commits and metric evaluation.

1|Updated Mar 16, 2026

Search Tags:#automation #logging #benchmarking #ai-agent #version-control #experiments #autoresearch

Ready to run

npx skills add https://github.com/chrisliu298/dotfiles --skill autoresearch-chrisliu298

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/chrisliu298/dotfiles/tree/main/agents/extensions/skills/autoresearch
Command: npx skills add https://github.com/chrisliu298/dotfiles --skill autoresearch-chrisliu298

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables autonomous experimentation loops for AI agents, ensuring iterative development through a branch-based workflow that runs edits, commits, executions, and evaluations without constant human prompting.

Core Features & Use Cases

Autonomous iteration: agent continuously proposes, implements, runs experiments, and logs outcomes on a dedicated autoresearch branch.
Versioned experimentation: every change is committed before execution, preserving history and enabling rollback.
Safe, repeatable workflows: includes a guard mechanism and recovery prompts to manage failures and avoid regressions.
Use cases include optimizing code, testing model configurations, and gathering reproducible results across projects.

Quick Start

Configure the objective and baseline on a new autoresearch branch, then begin the perpetual cycle of edit, commit, run, measure, and keep or revert.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

How do I automate autonomous AI experiments to run iterative code optimizations?▼

To automate autonomous AI experiments, configure a baseline command, metric extraction, and evaluation protocol on a dedicated branch. The agent then continuously proposes, implements, runs experiments, and logs outcomes without constant human prompting to achieve repeatable research.

What is branch-based version control for autonomous AI agent workflows?▼

Branch-based version control for autonomous workflows commits every code change before execution on a dedicated branch. This preserves history and enables rollback, ensuring iterative development remains safe and repeatable across experiments.

How do I set up guard policies and recovery prompts for AI agent benchmarking?▼

Guard policies and recovery prompts manage failures and avoid regressions during AI agent benchmarking. You configure a guard mechanism alongside the baseline command to evaluate execution outcomes and automatically trigger recovery protocols when needed.

Can I use autonomous experimentation loops for testing model configurations?▼

Yes, you can use autonomous experimentation loops for testing model configurations. The workflow applies across codebases requiring baselines and metrics, running a perpetual cycle of edit, commit, run, measure, and keep or revert.

What is the best way to log metrics and gather reproducible results across projects?▼

The best way to log metrics and gather reproducible results is applying a version-controlled iteration loop. Every experiment is committed before execution, preserving version history while the agent continuously logs outcomes against a configurable baseline.