auto-research

Mutate example-bank.ts, run online evaluation, and log outcomes to improve map generation.

Updated Mar 12, 2026

Search Tags:#evaluation #reproducibility #experimentation #machine-learning #auto-research #map-generation #online-eval

Ready to run

npx skills add https://github.com/arvidfcjarfalla-prog/atlas --skill auto-research-arvidfcjarfalla-prog

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: auto-research
Source: https://github.com/arvidfcjarfalla-prog/atlas/tree/main/.claude/skills/auto-research
Command: npx skills add https://github.com/arvidfcjarfalla-prog/atlas --skill auto-research-arvidfcjarfalla-prog

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Autonomous optimization loop: mutate examples → eval → keep if better, revert if worse.

Core Features & Use Cases

Online evaluation drives end-to-end improvements by testing prompts against the AI API.
Invariants protect critical system files while allowing mutation of the example bank for exploration.
Reproducible experimentation with baselines, train/holdout splits, and mutation logs.

Quick Start

Start the online auto-research loop by launching the dev server and running the online evaluation workflow as described.

Frequently Asked Questions about auto-research

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

FAQPage Schema

How do I automate map quality optimization using online evaluation?▼

Automate map quality optimization by mutating the example bank, running online evaluation against the AI API, and keeping only improvements to continuously optimize map generation quality. The system reverts unsuccessful mutations automatically.

What is an autonomous optimization loop for machine learning experimentation?▼

An autonomous optimization loop for machine learning experimentation mutates examples, evaluates them online, and keeps improvements while reverting failures. It ensures reproducibility using baselines, train/holdout splits, and mutation logs for full traceability.

Do I need a running dev server to perform online evaluation of map generation?▼

Yes, online evaluation requires a running dev server. The workflow applies to online map-generation processes that rely on real AI prompts and a running dev server to test and validate mutations against the live system.

How are critical system files protected during example bank mutation?▼

Invariants protect critical system files during example bank mutation. This allows safe exploration and mutation of the example-bank.ts file while preventing unintended modifications to essential system configurations or workflows.

How do I ensure reproducibility when automating map generation experiments?▼

Ensure reproducibility in map generation experiments by using baselines, train/holdout splits, and mutation logs. The workflow logs mutation outcomes for traceability and consistent result verification across runs.