ARCHITECTURE DIAGRAM
CODEX / CLAUDE CODEOFFICIAL TYPESAFE SKILLWORKFLOW CODE + DOCS
Overview & Result
MatchCN lets a developer describe the UI they need, then uses Jev-backed classifications and confidence signals to search existing components and return an install command.
How Jev fits in the loop
- The user supplies a natural-language component request.
- Jev helps classify candidate components across bounded design dimensions.
- The search layer ranks candidates and returns an existing component for the coding agent to install.
How to reproduce
- Open the live demo and inspect the indexed component metadata and result explanations.
- Run the same UI request against several registries and record which dimensions drive the match.
- Compare retrieval quality with a name-only or keyword-only baseline.
Why this build matters
It demonstrates Jev as a semantic retrieval and ranking layer that helps coding agents reuse existing code instead of generating everything from scratch.
Limitations
- The reviewed post does not provide a public repository or ground-truth retrieval set.
- The registry inventory and classifications may change over time.
- It should be treated as a demo and pattern example until a reproducible evaluation is available.