skywatch-osprey-sml-reference

Writing SML rules for the Osprey moderation framework involves using specific syntax to define conditions and effects, guided by comprehensive documentation on type systems and labeling patterns to ensure robust moderation logic.

Common anti-patterns when defining moderation logic with SML include improper variable naming and misaligned time constants, which you can identify and prevent using a structured reviewer checklist and established naming conventions.

ML-scored labeling in Osprey SML rules integrates machine learning scores into your moderation conditions, enabling dynamic content classification based on model confidence thresholds and established labeling patterns.

Yes, you can check a proposed moderation rule against repository standards by validating it through structured reviewer checklists, ensuring variable naming and type system conventions align with Osprey SML best practices.

You need to use established labeling patterns for spam detection rules when developing new moderation logic, ensuring your SML conditions effectively identify spam while maintaining consistency with the Osprey repository standards.