autoresearch

Name: autoresearch
Availability: InStock
Author: MayStudios

Orchestrate iterative prompts, validations, and actions across agents with git-backed history.

10|1|Updated Feb 24, 2026

Search Tags:#workflow #planning #optimization #verification #ai-agent #autonomous #loop

Ready to run

npx skills add https://github.com/maystudios/maxsimcli --skill autoresearch-maystudios

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/maystudios/maxsimcli/tree/main/templates/skills/autoresearch
Command: npx skills add https://github.com/maystudios/maxsimcli --skill autoresearch-maystudios

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Autonomous optimization loops enable autonomous refinement of tasks by orchestrating iterative prompts, validations, and actions across multiple agents.

Core Features & Use Cases

Reference-driven workflows: Powers /maxsim:improve, /maxsim:fix-loop, /maxsim:debug-loop, and /maxsim:security to streamline complex iterations.
Bounded and transparent iteration: Tracks iterations, baseline, and results with git-backed history for reproducibility.
Use Case: Ideal for iterative optimization in software projects, bug hunts, security audits, or complex task orchestration that benefits from repeatable loops.

Quick Start

Invoke the improvement loop with /maxsim:improve to start autonomous optimization.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

How do I set up autonomous optimization loops for iterative debugging?▼

Bounded iteration is an autonomous optimization loop design that tracks iterations, baselines, and results with git-backed history. This ensures mechanical verification and transparent traceability for reproducible debugging and security audits.

Can I use autonomous agents for repeatable security audits?▼

To start an autonomous optimization loop, invoke the improvement command to trigger iterative prompts and validations. This initiates continuous refinement across multiple agents, applying mechanical verification and tracking results with git-backed history for reproducibility.

What is the best way to orchestrate iterative repairs across multiple AI agents?▼

You should use an autonomous optimization loop when your task requires iterative optimization, such as software improvement, debugging, or security audits. It is ideal for complex task orchestration that benefits from repeatable loops and continuous refinement.

Why does my AI agent workflow lack traceability during continuous improvement?▼

Autonomous optimization loops are limited by bounded iterations, meaning they enforce a strict maximum number of runs to prevent infinite loops. You should anticipate these constraints when planning complex task orchestration to ensure mechanical verification completes within set boundaries.