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TabithaFanny
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auto-review-loop

Automates iterative review and improvement of research work using Codex MCP.

Updated May 29, 2026
Search Tags:#research automation#Codex MCP#iterative review#auto-review#research improvement
One-click install
npx skills add https://github.com/TabithaFanny/ThesisX --skill auto-review-loop-tabithafanny
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-review-loop
Source: https://github.com/TabithaFanny/ThesisX/tree/main/skills_imported/aris/skills/auto-review-loop
Command: npx skills add https://github.com/TabithaFanny/ThesisX --skill auto-review-loop-tabithafanny

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mcp__codex__codex, mcp__codex__codex-reply, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the iterative review and improvement of research work, streamlining the process of revising and refining until a positive assessment is achieved or the maximum number of review rounds is reached.

Core Features & Use Cases

  • Automated Review Loop: Automatically reviews research work multiple times, applying fixes and re-reviewing until a positive assessment or the maximum number of rounds is reached.
  • Multi-Round Review: Supports up to 4 rounds of review, with the option to pause after each round for user input.
  • Adaptive Review Difficulty: Offers three levels of review difficulty, from basic MCP-based review to more adversarial "nightmare" mode where GPT reads the repository directly.
  • Integration with Codex MCP: Utilizes Codex MCP for review and rebuttal, ensuring high-quality feedback and discussion.
  • State Persistence: Allows the skill to resume from the last saved state, making it suitable for long-running review processes.

Quick Start

To initiate an auto-review loop for a specific topic, use the command: auto-review-loop "topic" --compact: true, human checkpoint: true, difficulty: hard

Frequently Asked Questions about auto-review-loop

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

FAQPage Schema
How do I automate iterative research review and improvement?▼

Automated iterative research review uses Codex MCP to review and rebut research work, automatically applying fixes and re-reviewing until a positive assessment is reached or the maximum rounds are met. It supports adjustable difficulty levels and state persistence.

How does automated review loop difficulty adjustment work?▼

Automated review loop difficulty adjustment offers three levels, from basic Codex MCP review to an adversarial mode where the reviewer reads the repository directly, allowing users to scale review scrutiny based on research refinement needs.

Can I pause automated research review for human input between rounds?▼

Yes, automated research review supports human checkpoints, allowing you to pause after each of the up to four review rounds to provide manual input before the loop continues with the next iteration.

How do I resume an interrupted research review process?▼

You can resume an interrupted research review process using state persistence, which saves the loop's progress and allows the automated review to restart from the last saved round without losing previous rebuttal context.

Do I need Codex MCP to run automated iterative research reviews?▼

Yes, Codex MCP is required for automated iterative research reviews, providing the core review and rebuttal mechanisms that evaluate research quality and generate improvement feedback across multiple rounds.

What are the limitations of automated research review loops?▼

Automated research review loops are limited to a maximum of four rounds and depend on Codex MCP availability, meaning the process stops if a positive assessment is not reached within the configured iteration limit.

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