autoresearch

Generate hypotheses, design experiments, and analyze results for research topics.

2|Updated Mar 17, 2026

Search Tags:#automation #research #data-analysis #ai-agent #hypothesis #experiments #loop

Ready to run

npx skills add https://github.com/jeremylongshore/oss-agent-lab --skill autoresearch-jeremylongshore

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/jeremylongshore/oss-agent-lab/tree/main/agents/specialists/autoresearch
Command: npx skills add https://github.com/jeremylongshore/oss-agent-lab --skill autoresearch-jeremylongshore

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end research workflows by automatically generating hypotheses, designing experiments, and synthesizing results, reducing manual workload and accelerating scientific inquiry.

Core Features & Use Cases

Hypothesis generation: produces testable hypotheses with rationale for a given topic.
Experiment design: selects appropriate methods (literature review, simulation, or ablation) and tracks results.
Result analysis: summarizes findings, confidence, and next steps for iterative research.
Use Case: A researcher asks a question; the Autoresearch Specialist generates hypotheses, runs a simulated experiment, and returns an analysis with recommended next steps.

Quick Start

Provide a topic to initiate an autoresearch loop and review the generated hypotheses and results.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

How do I automate hypothesis generation and experiment design for research?▼

Automated research loops generate multiple hypotheses, select a primary one, run simulated experiments, and produce evidence-based analysis with recommended next steps. This applies across domains by automating hypothesis generation, experiment design, and result synthesis to accelerate scientific inquiry.

What is an automated research loop and how does it work for data analysis?▼

Automated research loops generate multiple testable hypotheses, select a primary one, run simulated experiments, and synthesize evidence-based analysis with recommended next steps. This self-improving workflow automates hypothesis generation, experiment design, and result synthesis across research domains.

Can I use automated experiment design for any research domain?▼

Yes, automated research loops apply to research questions across domains by generating multiple hypotheses, selecting a primary one, and running simulated experiments. The system selects appropriate methods like literature review, simulation, or ablation to produce evidence-based analysis regardless of field.

How do I start a self-improving research loop for a specific topic?▼

Provide a specific topic through the specialist interface along with optional parameters like method and context to start a research loop. The system generates hypotheses, designs experiments, runs simulations, and returns structured results including the topic, experiment details, and evidence-based analysis.

What is the best way to synthesize simulated experiment results and next steps?▼

Use an automated research workflow to synthesize simulated experiment results by summarizing findings, confidence levels, and recommended next steps. This produces structured evidence-based analysis that enables iterative research and continuous insight refinement across domains.

What are the limitations of simulated experiments in automated research loops?▼

Simulated experiments in automated research loops rely on generated hypotheses and methods like ablation or literature review rather than physical testing. While they accelerate insight generation and reduce manual workload, results are evidence-based simulations requiring real-world validation for definitive conclusions.