Screen
AI-assisted screening surfaces projects with measurable momentum across GitHub and Hugging Face—not merely loud launch-day claims.
Runeval methodology
The signal is only the start.
Popularity tells us where to look. Careful research, source verification, and a final quality check determine what deserves to be published.
SignalEvidenceQuality
Our process
Three steps between momentum and publication.
Research
Primary technical sources are reviewed, material claims are traced, and conflicts are preserved instead of smoothed away.
Quality check
One AI-assisted quality-control stage checks the finished report against a documented rubric before publication.
What counts as momentum
Measured signals.
No invented heat.
Runeval only presents metrics captured during ingestion. Commit frequency will appear only after the pipeline retrieves and stores it reliably.
Popularity combined with recent repository activity.
Adoption signals from the model’s source metadata.
A bounded score using popularity, freshness, and documentation depth.
Whether the candidate is genuinely new to the directory.
AI-assisted quality control
One check before publication.
AI is used to screen potential subjects and to run one quality-control stage on the finished report. The check scores the report against the rubric shown here; passing requires at least 85/100 and no critical grounding failure. Reports that do not pass are held from publication.
Fabricated measurements, invented capabilities, or broken attribution force failure regardless of score.
Factual grounding30
Technical depth15
Decision utility15
Topic completeness10
Clarity + engagement10
Code quality5
Visualization quality10
Search quality5
See the method in practice