Methodology
How SkillRank evaluates AI tools
SkillRank is built to be useful, transparent, and correctable. This page explains which signals are automated, which are editorial, and how readers should interpret rankings.
What SkillRank measures
SkillRank compares AI models, coding agents, AI skills, and developer-tool repositories by practical usefulness. Scores are directional signals for product teams, not universal benchmarks or procurement advice.
Editorial versus verified data
Closed vendor models are curated from official product pages, documentation, and release notes. GitHub-backed entries are verified through the GitHub REST API during the daily crawler so stars, forks, topics, license, language, and update dates come from a public source.
Daily history
The crawler records score, rank, and verified GitHub stars into a history file every day. Trend charts are built from recorded snapshots. New rows may show a current snapshot until enough daily records exist.
Corrections
Corrections are welcome when they include an official source, repository link, release note, or pricing page. SkillRank favors conservative updates and labels uncertain signals as editorial context.
Detailed guide
For the full data policy, read the SkillRank Data Methodology guide.