Traction scoreGitHub stars can be faked, so popularity alone can be misleading. Traction Score looks for broader signs of recent attention, adoption, and active maintenance.
This skill provides a structured way to discover and compare public resources for Jev and TypeSafe. It helps developers identify SDKs, applications, and integrations necessary for building typed-decision workflows.
By filtering community projects and cross-referencing them with official documentation, users can verify evidence for Jev claims and evaluate the maintenance and data flow of specific tools against their technical requirements.
WHO IT'S FOR
AI agent / automation builders
selecting public resources for typed-decision workflows
ML / AI researchers
checking evidence for Jev model claims
Technical leads / architects
comparing Jev SDKs and integrations for production
Backend / infrastructure engineers
finding official Jev API documentation and patterns
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.
Selection requirements or constraints, such as SDK language, license, data flow, maintenance, or evaluation evidence
Check the repository for this skill’s setup.
Use the awesome-jev skill to find up to three public Jev or TypeSafe resources for [describe your typed-decision workflow and requirements]. Check each promising resource's underlying source for current functionality, maintenance, license, data flow, and evaluation evidence. For each candidate, provide a short fit reason, one material limitation, and direct links. Clearly distinguish TypeSafe's own claims from independent findings and state when evidence is missing.
Claude
Codex
OpenCode
Hermes
DeepSeek
ZCode
Cline