auto-review-loop-minimax

Automate multi-round research review and iterative fixes via MiniMax API.

Updated May 29, 2026
One-click install
npx skills add https://github.com/Mang30/myskills --skill auto-review-loop-minimax-mang30
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-review-loop-minimax
Source: https://github.com/Mang30/myskills/tree/main/skills/auto-review-loop-minimax
Command: npx skills add https://github.com/Mang30/myskills --skill auto-review-loop-minimax-mang30

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It reduces the risk of publishing weak research by running an autonomous multi-round loop that sends your work for external review, applies the reviewer’s fixes, and re-submits for re-evaluation until quality criteria are met.

Core Features & Use Cases

  • Autonomous multi-round review loop: review → implement fixes → re-review until a positive assessment or MAX_ROUNDS is reached.
  • MiniMax-based external reviewer integration: uses an MCP tool when available or a curl-based API fallback when it is not.
  • State persistence for recovery: writes and overwrites review-stage/REVIEW_STATE.json to resume safely after context compaction or interrupted runs.

Use case: You have a research project that needs ICML/NeurIPS/ICLR-level critique; run the loop to collect a ranked list of weaknesses, implement the minimum fixes, and document each round in review-stage/AUTO_REVIEW.md.

Quick Start

Trigger an autonomous review loop for your current research topic by asking: auto review loop minimax <topic-or-scope>.

Frequently Asked Questions about auto-review-loop-minimax

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

FAQPage Schema
How do I automate paper revision for conference standards using an external reviewer?▼

Automating conference-level paper revision involves an autonomous loop that sends your work to an external reviewer, implements the minimum necessary fixes, and re-submits for re-evaluation until quality criteria are met or a maximum round limit is reached.

Can I use MiniMax API for automated research review and experiment automation?▼

Yes, automating research review with the MiniMax API uses an MCP tool for reviewer calls with a curl fallback. It drives experiment automation by repeatedly assessing claims, methods, and results, then applying fixes within an iterative loop.

What is state recovery in an autonomous review loop?▼

State recovery in an autonomous review loop persists the current review stage as a JSON file. This allows the automated paper revision process to safely resume after context compaction or interrupted runs without losing the iterative critique progress.

How do I run an iterative critique loop for ICML or NeurIPS level papers?▼

To run an iterative critique loop for ICML or NeurIPS level papers, trigger the autonomous review process with your topic. The loop collects a ranked list of weaknesses, implements minimum fixes, and documents each round in an auto review markdown file.

Does the automated paper revision loop work without MCP?▼

Yes, the automated paper revision loop works without MCP by falling back to a curl-based API call for the MiniMax reviewer. This ensures the autonomous multi-round review and experiment automation process remains functional across different environments.