working-the-queue

Automating moderation queue triage involves systematically processing reports using an observe-orient-decide-act framework. This skill aggregates rule hits, profile data, and thread context from Ozone and ClickHouse to recommend content classifications.

The OODA loop approach for content moderation applies an observe-orient-decide-act framework to systematically process reports, appeals, and proactive filters. It automates evidence gathering so analysts can focus on high-level classification decisions.

Yes, integrating with Ozone and ClickHouse is required to retrieve rule hits, moderation history, and account data. These dependencies provide the contextual evidence necessary for automated report processing and appeal review.

You can use specialized AI subagents to gather context for moderation triage. The system delegates rapid data collection and mechanical action execution to distinct subagents, maintaining context window efficiency while aggregating evidence.

The best way to handle high-volume moderation queues is to automate the observation and orientation phases of triage. By using intelligent delegation to process proactive filters and aggregate thread context, analysts can focus on high-level classification decisions.

Manual moderation triage creates a bottleneck because analysts must spend time on observation and evidence gathering before making classification decisions. Automating these phases with an OODA framework eliminates the delay and streamlines policy enforcement workflows.