autoresearcher

Optimize prompts and scoring configurations through an autonomous generate-evaluate-keep loop.

4|2|Updated Apr 26, 2026

Search Tags:#research #optimization #evaluation #experimentation #autonomous #machine-learning

Ready to run

npx skills add https://github.com/IsNoobgrammer/skills-for-agents --skill autoresearcher

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearcher
Source: https://github.com/IsNoobgrammer/skills-for-agents/tree/main/skills/autoresearcher
Command: npx skills add https://github.com/IsNoobgrammer/skills-for-agents --skill autoresearcher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill solves the problem of inefficient, manual trial-and-error when optimizing measurable artifacts like prompts, scoring configurations, or algorithms, preventing the common pitfalls of Goodhart's Law and selection bias.

Core Features & Use Cases

Autonomous Research Loop: Executes a self-iterating generate-evaluate-keep cycle to improve any measurable artifact against a frozen evaluation.
Diagnosis-Driven Improvement: Uses specialized engines to diagnose failure points and dispatch targeted improvements rather than blind mutation.
Use Case: If you are trying to optimize a complex scoring model for medical diagnostics, this skill will autonomously run experiments, diagnose why specific cases fail, and iteratively improve the model weights until your target recall is met.

Quick Start

Invoke the autoresearcher skill to begin optimizing the target artifact against the frozen evaluation metric.

Frequently Asked Questions about autoresearcher

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

FAQPage Schema

How do I automate the optimization of prompts and scoring configurations?▼

You can automate the optimization of prompts and scoring configurations by running an autonomous research loop that executes a generate-evaluate-keep cycle against a frozen evaluation metric. This eliminates manual trial-and-error.

What is an autonomous research loop for artifact optimization?▼

An autonomous research loop for artifact optimization is a self-iterating process that diagnoses failure points and dispatches targeted improvements rather than blind mutation. It improves measurable artifacts until a target metric is met.

Do I need a held-out data split to prevent overfitting during experimentation?▼

Yes, you need a held-out data split to prevent overfitting during experimentation. The optimization process requires a structured environment with defined failure buckets and a frozen evaluation metric to validate candidate performance.

Why does manual trial-and-error fail when improving machine-learning heuristics?▼

Manual trial-and-error fails when improving machine-learning heuristics because it is inefficient and prone to Goodhart's Law and selection bias. Diagnosis-driven improvement engines solve this by targeting specific failure points.

When should I use an autonomous research loop for iterative improvement tasks?▼

You should use an autonomous research loop for iterative improvement tasks when a frozen evaluation metric exists to validate candidate performance. It excels at optimizing complex models, such as scoring configurations for medical diagnostics.