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auto-review-loop-minimax

Automate iterative research artifact reviews via MiniMax API with state persistence.

Updated Jun 10, 2026
Search Tags:#api-integration#research-review#minimax#state-persistence#autonomous-review#external-feedback
One-click install
npx skills add https://github.com/xqinag/ARIS-new --skill auto-review-loop-minimax-xqinag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-review-loop-minimax
Source: https://github.com/xqinag/ARIS-new/tree/main/skills/auto-review-loop-minimax
Command: npx skills add https://github.com/xqinag/ARIS-new --skill auto-review-loop-minimax-xqinag

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Autonomous multi-round research review loop using MiniMax API to replace traditional external reviews and expedite high-stakes feedback cycles.

Core Features & Use Cases

  • Autonomous review loop: review → fix → re-review, carrying forward prior round summaries
  • Flexible API integration: MCP tool when available or curl fallback to MiniMax
  • Robust state persistence: resumes after interrupts via review-stage/REVIEW_STATE.json
  • Configurable rounds and stop conditions: MAX_ROUNDS and STOP CONDITIONS govern when to end a cycle
  • Detailed audit trail: updates to review-stage/AUTO_REVIEW.md after each round

Quick Start

Start an autonomous MiniMax review cycle by invoking the trigger phrase 'auto review loop minimax'.

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 iterative review and feedback cycles for research artifacts?▼

Automate iterative review by running an autonomous loop that evaluates research artifacts, applies fixes, and re-reviews until a positive assessment or maximum rounds is reached. It carries forward prior round summaries to maintain context throughout the cycle.

Can I use the MiniMax API to replace traditional MCP-based research reviews?▼

Yes, you can replace traditional MCP-based reviews by integrating the MiniMax API. The system uses the MCP tool when available and automatically falls back to curl requests to ensure the external review loop executes.

How does state persistence work when an autonomous review loop is interrupted?▼

State persistence saves progress to a review-stage/REVIEW_STATE.json file. This allows the autonomous review loop to resume execution seamlessly after unexpected interrupts without losing the current evaluation context.

How do I configure stop conditions and maximum rounds for an autonomous review loop?▼

Configure stop conditions and maximum rounds by setting explicit MAX_ROUNDS and STOP_CONDITIONS parameters. These govern when the cycle ends, ensuring the review loop terminates upon a positive assessment or reaching the iteration limit.

Does the autonomous review loop generate an audit trail for multi-round evaluations?▼

Yes, the autonomous review loop generates an audit trail by updating a review-stage/AUTO_REVIEW.md file after each round. This provides a detailed log of all feedback and fixes implemented during the evaluation workflow.

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