narrative-convergence

Cross-skill narrative convergence is detected by analyzing local files from 3+ independent skills to identify entities or themes surfacing across multiple categories within 48 hours. This indicates high-confidence write opportunities.

Cross-skill signal detection works by measuring cross-category diversity among independent operational skills to spot emergent narratives. It requires local files from other skills and an operator-editable signal-category mapping for analysis.

Yes, you need a signal-category mapping to perform convergence analysis. This operator-editable mapping is required to measure cross-category diversity and identify converging signals from multiple independent skills.

Yes, you can use convergence detection to identify emerging narratives by measuring cross-category diversity. It identifies entities or themes independently detected by 3+ operational skills within a 48-hour timeframe.

Limitations of narrative convergence detection include its dependency on local files from other skills and a signal-category mapping. It also requires signals from 3+ independent skills within 48 hours to identify write opportunities.

To start running cross-skill convergence analysis, execute the narrative-convergence skill to analyze the current state of cross-skill convergence. Ensure local files from other skills and your signal-category mapping are available.