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auto-review-loop-minimax

Automate multi-round external research reviews using the MiniMax API.

Updated Mar 1, 2026
Search Tags:#automation#minimax#state-persistence#external-review#multi-round#research-improvement
One-click install
npx skills add https://github.com/hve4638/hve-cc-marketplace --skill auto-review-loop-minimax-hve4638
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-review-loop-minimax
Source: https://github.com/hve4638/hve-cc-marketplace/tree/main/aris/skills/auto-review-loop-minimax
Command: npx skills add https://github.com/hve4638/hve-cc-marketplace --skill auto-review-loop-minimax-hve4638

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates autonomous external research reviews by simulating expert critique via the MiniMax API, reducing manual back-and-forth and accelerating feedback cycles.

Core Features & Use Cases

  • Multi-round evaluation loop: cycles through review, fixes, and re-review up to MAX_ROUNDS.
  • MCP-enabled primary path or curl fallback for external review via MiniMax API.
  • State persistence: RECOVERABLE state via REVIEW_STATE.json and context restoration from AUTO_REVIEW.md.
  • Documentation and traceability: stores raw reviewer responses for audit and accountability.
  • Use Case: ideal for refining research manuscripts, proposals, or code with external validation needs.

Quick Start

Run the skill to start an autonomous MiniMax external-review loop on your project.

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 multi-round external research reviews?▼

The review loop works by persisting state in a local JSON file and restoring context from a markdown file, allowing the automated MiniMax API critique process to resume evaluation rounds across sessions.

Does the MiniMax API review loop work without MCP?▼

You need MiniMax API access and a local state store to persist review rounds. It is ideal for refining research manuscripts, proposals, or code submissions where simulated expert external validation is desired.

How does state persistence work in an automated review loop?▼

State persistence in the review loop saves recoverable state via a JSON file and restores context from a markdown file, ensuring multi-round evaluation cycles remain intact across interruptions.

What is the best way to simulate expert critique for research proposals?▼

The best way to simulate expert critique for research proposals is automating an external review loop via the MiniMax API, which handles multi-round evaluation workflows and stores raw reviewer responses for auditability.

Are there limitations to using MiniMax API for autonomous code review?▼

A limitation of using the autonomous review loop is the dependency on MiniMax API access and a local state store for state persistence, meaning it requires constant external API connectivity and local storage management.

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