Acta Automatica Sinica Artifact Evaluation
Structures anonymous, reproducible code and data artifacts for submissions to Acta Automatica Sinica to maximize manuscript credibility during peer review.
Structures anonymous, reproducible code and data artifacts for submissions to Acta Automatica Sinica to maximize manuscript credibility during peer review.
The aas-artifact-evaluation skill guides researchers through preparing transparent, anonymous, and reproducible code and data packages for manuscripts submitted to Acta Automatica Sinica (《自动化学报》). It clarifies the journal's peer-review context, double-blind review requirements, and AI disclosure rules while providing standardized directory layouts for control simulations, robotics experiments, and pattern recognition models. Researchers can systematically address non-public industrial datasets, lock down execution environments, and map manuscript figures directly to runnable scripts for rigorous review.