autoresearch-create

Configure and execute autonomous optimization experiments with version control and documentation.

7.5k|440|Updated Mar 11, 2026

Search Tags:#automation #git workflow #autonomous experiments #optimization setup #experiment scripting

Ready to run

npx skills add https://github.com/davebcn87/pi-autoresearch --skill autoresearch-create-davebcn87

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch-create
Source: https://github.com/davebcn87/pi-autoresearch/tree/main/skills/autoresearch-create
Command: npx skills add https://github.com/davebcn87/pi-autoresearch --skill autoresearch-create-davebcn87

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of establishing and executing autonomous experiment loops for any optimization target, reducing manual setup time.

Core Features & Use Cases

Session Configuration: Guides users through defining goals, metrics, scope files, and constraints for optimization tasks.
Automated Experiment Setup: Creates necessary files like autoresearch.md and autoresearch.sh, and manages git workflows.
Use Case: When asked to optimize a codebase for faster build times, this Skill sets up an autonomous loop that tests modifications, benchmarks results, and logs progress to improve the build process iteratively.

Quick Start

Ask the AI to set up an autonomous experiment loop for your project, including configuration files and baseline measurements.

Frequently Asked Questions about autoresearch-create

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

FAQPage Schema

How do I set up autonomous optimization experiments in my development workflow?▼

Autonomous optimization experiments are configured by defining goals, metrics, scope files, and constraints. This skill generates the necessary configuration files, manages git workflows, and initiates iterative testing across project files to continuously improve code or process metrics.

What is the best way to automate iterative testing and benchmarking for code optimization?▼

Automating iterative testing and benchmarking involves creating an experiment loop that tests modifications and logs progress automatically. This skill establishes the loop, takes baseline measurements, and benchmarks results iteratively to reduce manual setup time and improve the build process.

Can I use git workflow integration to manage autonomous experiment scripting?▼

Git workflow integration is fully supported for autonomous experiment scripting. The setup process automatically manages git workflows alongside the creation of experiment scripts and configuration files, ensuring version control is maintained throughout the optimization iterations.

Do I need to define constraints before running autonomous optimization loops?▼

Defining user-defined constraints is required before running autonomous optimization loops to ensure safety. The session configuration guides you through establishing goals, metrics, scope files, and boundaries that the automated testing and modification process must respect.

What files are needed to start an automated experiment loop for a codebase?▼

Starting an automated experiment loop requires configuration files like autoresearch.md and autoresearch.sh. These files are generated automatically during setup to document the experiment state, execute the optimization scripts, and manage baseline measurements.

When should I not use autonomous experiment loops for build optimization?▼

Autonomous experiment loops should not be used when you cannot establish clear user-defined constraints or safety boundaries. Without defined metrics, scope files, and specific optimization targets, the automated modifications and iterative testing cannot be safely controlled.