narrative-convergence

Cross-skill convergence signals are detected by aggregating outputs from multiple skills and memory logs to identify when 3 or more independent skills flag the same entity or theme within a 48-hour window.

Cross-skill signal aggregation is the process of compiling outputs across independent skills to reveal overlooked trends. You need it to surface high-confidence write opportunities when multiple skills flag the same entity.

Generate write angles by running a convergence scan that scores and ranks convergent entities, producing a prioritized list of top signals with operator-friendly framing, hooks, and memory-update artifacts.

No, surfacing high-signal write opportunities requires 3 or more independent skills to flag the same entity or theme within 48 hours to ensure high confidence and reduce false positives.

No specific dependencies are required. Convergence scoring evaluates how many independent skills and categories converge on a single entity to prioritize and rank the top signals for write opportunities.

Convergence signals will not appear unless the 48-hour threshold and 3-skill minimum are met. The system updates memory with convergence results only after successfully identifying these strict overlaps.