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auto-review-loop

Automate iterative research reviews with Codex MCP across phases A through E.

Updated Apr 2, 2026
Search Tags:#logging#autonomous#peer-review#state-persistence#iterative-improvement#review-loop#codex-mcp
One-click install
npx skills add https://github.com/dz306271098/ARIS_for_Robotics --skill auto-review-loop-dz306271098
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-review-loop
Source: https://github.com/dz306271098/ARIS_for_Robotics/tree/main/skills/auto-review-loop
Command: npx skills add https://github.com/dz306271098/ARIS_for_Robotics --skill auto-review-loop-dz306271098

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates autonomous, multi-round research reviews by orchestrating systematic critique and iterative fixes across rounds, reducing manual overhead and accelerating convergence to a high-quality outcome.

Core Features & Use Cases

  • Orchestrates review phases (A-E) with configurable MAX_ROUNDS and thresholds to determine readiness.
  • Applies deterministic fixes, re-evaluates with Codex MCP, and persists state to resume after interruptions.
  • Supports optional human checkpoints, collaborative escalation, and detailed round logging for governance and traceability.
  • Useful for research teams aiming to accelerate manuscript readiness, experiment review, and knowledge extraction with auditable history.

Quick Start

Start the autonomous review loop in your project by placing this skill at the root and invoking it to begin iterative, self-driving reviews.

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 peer review for research workflows?▼

Automating iterative peer review involves orchestrating multi-round critiques where fixes are applied and re-evaluated until success. This skill uses Codex MCP to drive autonomous, structured review phases with persistent state to resume after interruptions.

What is an autonomous review loop and when do I need it for research?▼

An autonomous review loop is a self-driving process that performs iterative critiques and improvements across rounds. You need it when accelerating manuscript readiness or experiment review requires reducing manual overhead and ensuring auditable progress.

Can I resume a multi-round research review after an interruption?▼

Yes, resuming a multi-round research review after interruptions is possible through state persistence. The skill saves execution progress across its structured phases, ensuring auditable logging and measurable progress are maintained.

How do I add human checkpoints to an automated research review process?▼

Adding human checkpoints to an automated research review process is supported through optional collaborative escalation. This allows researchers to intercept the iterative loop for governance and traceability before continuing autonomous fixes.

Does Codex MCP support autonomous iterative improvement with logging?▼

Yes, Codex MCP supports autonomous iterative improvement by re-evaluating applied fixes across configurable maximum rounds. Detailed round logging captures execution history to drive auditable governance and traceable progress.

What are the limitations of using a self-driving review loop for experiments?▼

Limitations of a self-driving review loop include dependency on configurable maximum rounds and success thresholds. If the maximum rounds are reached without meeting readiness thresholds, the autonomous process stops, requiring manual intervention.

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