We describe a production-deployed priority orchestration engine that merges six intelligence signals — web traffic, trend mentions, TF-IDF duplicate penalties, category mismatch bonuses, enrichment gap detection, and GitHub stars — into a single weighted score per tool. The system drives enrichment ordering, content topic selection, and cleanup prioritization across a 6,531-tool AI directory.
Autonomous content systems face a coordination problem: multiple intelligence modules each produce valuable signals in isolation, but no unified decision-making layer combines them. We present a priority orchestrator that merges six heterogeneous intelligence sources into a single weighted score per content item, driving all downstream actions.
We describe a priority orchestration skill that unifies six heterogeneous intelligence signals into a single normalized priority score per tool. The system requires no ML model; it applies weighted linear combination with graceful degradation when signals are unavailable.