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Wike-CHI
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autoresearch

Automate iterative hypothesis testing and metric-driven evaluation of AI agent configurations.

2|Updated Mar 31, 2026
Search Tags:#git#ai#metrics#hypothesis#experimentation#ablation#autoresearch
One-click install
npx skills add https://github.com/Wike-CHI/acquisition-agent --skill autoresearch-wike-chi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/Wike-CHI/acquisition-agent/tree/main/skills/autoresearch
Command: npx skills add https://github.com/Wike-CHI/acquisition-agent --skill autoresearch-wike-chi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Autoresearch enables disciplined, memory-backed autonomous experimentation to systematically explore design spaces and converge on effective configurations.

Core Features & Use Cases

  • Discipline: Ensure single-variable changes per experiment and maintain a rigorous hypothesis-driven loop.
  • Memory & Traceability: Git-based history plus results.tsv track experiments and outcomes.
  • Use Cases: Hyperparameter tuning, ablation studies, configuration optimization, and iterative protocol development for AI agents.

Quick Start

Initialize a new autoresearch session and run the first baseline experiment to establish a starting metric.

Frequently Asked Questions about autoresearch

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

FAQPage Schema
How do I automate hyperparameter tuning and ablation studies for AI agents?▼

Autoresearch automates hyperparameter tuning and ablation studies by enforcing single-variable changes per run and recording outcomes in a results.tsv file. It maintains a rigorous hypothesis-driven loop to systematically explore and optimize AI agent configurations.

What is disciplined autonomous experimentation in machine learning?▼

Disciplined autonomous experimentation is an iterative process of hypothesis testing and metric-driven evaluation. It ensures single-variable changes per run to systematically explore design spaces and converge on effective AI configurations.

How do I track experiment history and metrics for iterative AI configuration testing?▼

You can track experiment history and metrics using git-backed records and a results.tsv file. This provides memory and traceability for every iterative hypothesis test and configuration comparison executed.

Can I run ablation studies and configuration comparisons without external dependencies?▼

Yes, you can run ablation studies and configuration comparisons without external dependencies. The session uses a context-driven SKILL.md entry point with optional scripts and references to manage extended tasks.

What is the best way to ensure single-variable changes during metric-driven evaluation?▼

The best way to ensure single-variable changes during metric-driven evaluation is to use a disciplined autonomous experiment loop. This approach enforces single-variable modifications per run and logs results systematically.

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