autoresearch-create

Configure goal, metric, and scope to run autonomous optimization loops.

6|1|Updated Nov 23, 2023

Search Tags:#automation #logging #optimization #reproducibility #experiments #autoresearch

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Set up and run an autonomous experiment loop that configures goal, metric, and scope, then starts looping immediately.

Core Features & Use Cases

Autonomous experiment loop: initialize, run, log, and iterate with auto-reversion of changes on failure; supports baselines and reconfiguration.
Reproducible workflow: uses autoresearch.md, autoresearch.sh, and optional config for controlled iterations.
Use Case: A data scientist wants to optimize a model's hyperparameters by running rapid experiments and keeping only successful changes.

Quick Start

Create a new autoresearch session by defining the goal, metric, and scope, then run the initial baseline and start the loop.

Frequently Asked Questions about autoresearch-create

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

FAQPage Schema

How do I automate hyperparameter optimization experiments with automatic logging?▼

Automate hyperparameter optimization by defining a goal, metric, and scope to initialize an autonomous experiment loop. The loop runs rapid experiments, logs results, and automatically reverts changes on failure, keeping only successful modifications for continual refinement.

What is an autonomous optimization loop and when do I need it for machine learning experiments?▼

An autonomous optimization loop is an iterative experimentation process that automatically configures baselines, logs metrics, and refines code, data, or models. You need it when running rapid, reproducible experiments that require continual refinement and automatic reversion of failed changes.

How do I set up a reproducible workflow for iterative experimentation across code and models?▼

Set up a reproducible workflow by generating dedicated files including autoresearch.md, autoresearch.sh, and autoresearch.config.json. These files control iterations, log results automatically, and apply optional quality checks to ensure experimental consistency across code, data, and model changes.

Can I configure a custom goal and metric scope for autonomous experiment loops?▼

Yes, you can configure a custom goal, metric, and scope to define the boundaries of your autonomous experiment loop. This configuration initializes the session, runs the baseline, and starts iterating immediately within your specified parameters.

Does the autonomous experiment loop support automatic reversion of failed changes?▼

Yes, the autonomous experiment loop supports automatic reversion of changes on failure. When an experiment iteration fails to improve the defined metric, the loop automatically reverts the modifications and continues iterating with new variations.

What are the limitations of running autonomous optimization sessions for iterative experimentation?▼

Autonomous optimization sessions require clearly defined goals, metrics, and scope to function effectively, and rely on dedicated configuration files for reproducibility. Without optional quality checks enabled, failed iterations may not be properly guarded against introducing unintended side effects.