Skill UI

This detailed guide teaches authors how to structure and position their research paper within the highly interdisciplinary field of robot learning. It covers essential best practices, including reviewing multiple concurrent literature streams (ML methods, classical robotics, robot lineage, VLA models) and adhering to strict academic citation hygiene (e.g., handling PMLR year offsets).

A detailed guide on positioning a computer architecture paper within the broader academic literature. It teaches authors to write a 'lineage with structural deltas,' moving beyond mere citation lists. The guidelines cover sweeping multiple databases (like dblp and IEEE Xplore), identifying concrete structural improvements (deltas), and adhering to strict academic rules, such as citing all authors and maintaining un-blinded bibliography integrity.

This skill outlines a highly rigorous, receipt-driven workflow for managing defects and implementing code corrections. It guides public collaboration by enforcing strict controls over change authority, ensuring comprehensive lineage tracking, and mandating evidence-based validation. Key steps include CodeGraph impact mapping, black-box testing, and adherence to strict change budgets, ensuring every deployed change is fully traceable and robust.