auto-review-loop

Automating iterative research reviews requires orchestrating an autonomous multi-round loop that scores a project, identifies weaknesses, and implements fixes until a positive assessment or maximum rounds are reached. This skill executes that exact review cycle.

An autonomous review loop persists state between rounds by saving progress in REVIEW_STATE.json and updating AUTO_REVIEW.md with round results. This structured documentation ensures auditability and supports recovery after interruptions.

You can configure the maximum rounds and passing threshold for automated research analysis by setting the MAX_ROUNDS and POSITIVE_THRESHOLD parameters. These define the autonomous loop's stopping criteria and required positive assessment score.

The autonomous review loop supports compact recovery after interruptions by persisting its state in REVIEW_STATE.json. It reads review logs and experiment results to decide next actions and resume the iterative assessment seamlessly.

Preparing a project for publication readiness using AI-assisted research analysis is best achieved through autonomous critique and progressive validation. The review loop repeatedly scores and remediates weaknesses until structured output meets publication standards.

The iterative review loop stops before reaching a positive assessment when the configured MAX_ROUNDS limit is reached. This constraint prevents infinite looping when prescribed minimal fixes fail to achieve the POSITIVE_THRESHOLD score.