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autoresearch

Orchestrate end-to-end autonomous AI research projects with a two-loop architecture.

6|3|Updated Jan 29, 2026
Search Tags:#project management#research synthesis#autonomous research#experiment orchestration#two-loop architecture
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill autoresearch-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/0-autoresearch-skill
Command: npx skills add https://github.com/jonnabio/ace-framework --skill autoresearch-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude-code, openclaw, python, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps users manage and automate end-to-end autonomous AI research projects, from initial idea to final published paper, using a two-loop architecture that optimizes experiments and synthesizes results.

Core Features & Use Cases

  • Two-Loop Architecture: Automates the iterative process of running experiments and synthesizing results.
  • Experiment Orchestration: Manages experiments and data, ensuring continuous, autonomous operation.
  • Research Synthesis: Analyzes results, identifies patterns, and steers research direction.
  • Domain-Specific Skills: Routes tasks to specialized skills for data processing, model training, and more.
  • Continuous Agent Operation: Maintains agent continuity through Claude Code and OpenClaw heartbeat mechanisms.
  • Progress Presentations: Generates research presentations and papers for review.

Quick Start

Initialize your research workspace and set up the agent continuity loop. Start the research process by searching the literature, identifying gaps, and forming hypotheses. Run experiments, analyze results, and reflect on findings to guide future research.

Frequently Asked Questions about autoresearch

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

FAQPage Schema
How do I automate end-to-end AI research experiments from hypothesis to paper?▼

To automate autonomous AI research experiments, this Skill uses a two-loop architecture that manages rapid iterations, synthesizes results, identifies patterns, and steers research direction toward generating final papers and presentations.

What is a two-loop architecture for autonomous research and how does it work?▼

A two-loop architecture for autonomous research automates the iterative process of running rapid experiments and synthesizing the results. It continuously analyzes findings to identify patterns and steer the ongoing research direction.

How do I set up a project workspace for autonomous research orchestration?▼

To set up a project workspace for research orchestration, you must initialize a structured directory containing research-state.yaml, research-log.md, and findings.md to maintain agent continuity and track experiment progress.

Do I need Claude Code and Python to run continuous agent research operations?▼

Yes, continuous agent operation for autonomous research requires Claude Code and OpenClaw heartbeat mechanisms alongside Python. These dependencies maintain agent continuity and route tasks to domain-specific skills.

Can I route autonomous AI research tasks to specialized domain skills?▼

Yes, autonomous research orchestration routes tasks to domain-specific skills for specialized processing. It supports continuous agent operation to manage experiments, synthesize data, and generate research presentations.

What are the limitations of using a two-loop architecture for experiment orchestration?▼

The two-loop architecture requires a structured project workspace with specific files like research-state.yaml and findings.md. Without proper initialization of these tracking files, the autonomous research agent cannot maintain continuity or steer experiments effectively.

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