signal-scan

To cross-reference qualitative client feedback with performance data, this Skill correlates signals like corrections, wins, and tone shifts from Obsidian markdown documents against quantitative metrics retrieved via SQL queries from Supabase to identify actionable patterns.

Validating client corrections using content performance metrics involves cross-referencing qualitative correction signals from an Obsidian vault with quantitative engagement data in Supabase to determine if implemented changes are actively improving results.

Yes, you need SQL queries to retrieve quantitative performance data from Supabase, which is then cross-referenced with structured analysis of markdown documents from your Obsidian vault to interpret qualitative client signals.

You can discover emergent patterns in audience behavior and content attributes by linking qualitative signals stored in Obsidian notes with quantitative performance metrics in Supabase to reveal correlations between client sentiment and content effectiveness.

Tone-performance correlation analysis works by examining qualitative tone shifts documented in Obsidian markdown and comparing them against quantitative content metrics in Supabase to explore the relationship between client sentiment and overall content effectiveness.

Limitations include the requirement for structured analysis of markdown documents and accurate SQL queries for Supabase data retrieval, meaning unstructured notes or poorly formed database queries will reduce the accuracy of discovered patterns and correlations.