Evaluates and categorizes content based on idea density and relevance to human flourishing and AI impact.
This skill implements the popular 'label_and_rate' Fabric pattern to provide objective, structured evaluations of text-based content. It acts as an ultra-wise judge, categorizing input using a specific taxonomy and assigning a quality tier based on the density of unique ideas and alignment with philosophical themes like human meaning in a post-AI world. By filtering out low-signal noise and political bias, it helps users identify high-value information that contributes to personal growth and intellectual development.
Key Features
01Qualitative tier-based ranking from D to S Tier
02Quantitative idea density scoring (1-100 scale)
031 GitHub stars
04Automatic filtering of low-signal or overtly political content
05Standardized JSON output for easy integration into workflows
06Strict taxonomy-based labeling using 25+ predefined categories
Use Cases
01Automating high-signal content curation for newsletters or research databases
02Evaluating the depth and intellectual value of long-form articles or transcripts
03Filtering reading lists to prioritize content focused on human meaning and innovation
