autoresearch

Name: autoresearch
Availability: InStock
Author: mrwest3

Automate iterative experiment cycles to improve metrics through measured changes.

Updated Aug 23, 2026

Search Tags:#metrics #learning #measurement #hypothesis #experimentation #autoresearch

Ready to run

npx skills add https://github.com/MrWest3/west-command-center --skill autoresearch-mrwest3

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/MrWest3/west-command-center/tree/main/templates/analyst/.claude/skills/autoresearch
Command: npx skills add https://github.com/MrWest3/west-command-center --skill autoresearch-mrwest3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Autoresearch formalizes the process of turning data-driven hypotheses into measurable improvements. It replaces ad-hoc experimentation with a disciplined loop of hypothesis, targeted changes, measurement, and a decision to keep or discard each change.

Core Features & Use Cases

Structured experiment loop: define a metric, surface, direction, and measurement window to iterate until an improvement is observed.
Guided decision-making: automatically decide to keep or discard changes based on measured results.
Comprehensive logging: record learnings for every experiment to build a knowledge base and accelerate future iterations.
Theta-wave integration: aligns autoresearch with the theta-wave cycle management for end-to-end workflow guidance.

Quick Start

Initiate an autoresearch cycle by following the onboarding steps and start your first hypothesis experiment.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

How do I automate structured experimentation to improve metrics?▼

Automated structured experimentation improves metrics by running iterative cycles that define a metric, formulate hypotheses, execute targeted changes, and measure results to decide whether to keep or discard each change.

What is the process for defining a hypothesis and measuring its impact?▼

The hypothesis measurement process involves defining a target metric, stating a hypothesis, executing targeted changes, waiting for a defined measurement window, evaluating outcomes, and logging learnings for future iterations.

How do I decide whether to keep or discard changes after running an experiment?▼

Deciding to keep or discard changes after an experiment relies on automated guided decision-making that evaluates measured results against the initial metric definition and direction to determine if an improvement occurred.

Can I log experiment learnings to build a knowledge base for future iterations?▼

You can log experiment learnings to build a knowledge base by recording comprehensive outcomes of every hypothesis and measured result, which accelerates future iterations and informs subsequent metric improvement cycles.

What is the best way to replace ad-hoc data-driven experimentation with a disciplined loop?▼

Replacing ad-hoc data-driven experimentation requires a disciplined loop that formalizes turning hypotheses into measurable improvements by automating the end-to-end experiment cycle from context gathering to learning logs.