Knowledge Base
Digital Karma Scoring Methodology
Understanding the transparent trust and quality model that helps AI systems judge how complete, current, and reliable a Digital Karma implementation appears.
The Seven Public Signals
- Schema Coverage (20%) ... how completely the site explains page meaning with Schema.org.
- Content Freshness (15%) ... whether the public discovery layer still reflects the current site.
- AI Endpoints (25%) ... completeness and validity of the required machine-readable files.
- Federation Presence (15%) ... quality of peer relationships and topology signals.
- External Links (10%) ... relevance and quality of wider web references.
- Technical Quality (10%) ... HTTPS, responsive behavior, and general implementation health.
- Dataset Quality (5%) ... usefulness and completeness of published structured assets.
Badge Levels
- Constellation Member ... all six required artifacts present plus root
/robots.txtand/sitemap.xml. - Karma Certified ... score of 0.70 or higher.
- Karma Pro ... score of 0.85 or higher.
- Karma Elite ... score of 0.95 or higher.
In the v7 line the correct name is Karma Certified, not Karma Bronze.
How Owners Raise the Score in Practice
The biggest gains usually come from fixing the boring but important gaps: complete the required endpoints, keep the root discovery files in the right place, add Schema.org to primary pages, publish at least one useful dataset, and make sure the federation relationships are real instead of decorative.
A score is useful because it turns vague quality work into something reviewable. It helps owners see whether the site is becoming easier for AI systems to trust and use over time.