MatchCN
这是 whosfranki 公开的项目资料。本站按原始来源展示项目信息，用中文说明适用场景和阅读边界；项目名、源帖与代码保持原样，便于逐项核对。
这条案例记录了什么
Agent 评估、Agent 与模型路由
为已记录的输出或轨迹提供分类、分数或复核信号。
社区公开项目或作者演示
原始来源：Original X post and live demo。尚未独立核实。
whosfranki
记录日期：2026-09-22。日期与身份应以原始资料为准。
原始演示视频
视频来自此案例记录的原始媒体；播放内容和作者声明不等于本站复现。
怎样核对这个项目
- 先打开原始来源，确认作者、日期与 Jev 在项目中的具体用途。
- 如果提供仓库，再检查代码、运行要求和许可证；仓库存在不代表本站已经运行成功。
- 对速度、成本、准确率和规模数字，查看原文的任务、环境和计算口径。
- 是否有可观察的正确答案。
- 评估输入是否完整。
- 分数与人工复核的一致性。
- 路由候选是否完整。
原始文字与技术细节
以下内容保留原语言，供核对事实。中文页的场景说明是阅读提示，不是逐句翻译或实测结论。
展开英文项目摘要与原帖
项目摘要
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.
来源原文
Built matchcn: semantic component search for coding agents, powered by Jev. Describe the UI you need. It finds the component and returns the install command. No component name needed. No browsing 9 different libraries. 1,783 components across 9 @shadcn registries, classified across six dimensions: purpose, motion, density, interaction, data, and decoration. Jev returns calibrated probabilities instead of generated text, so every classification comes with confidence. Matching uses that confidence when scoring results. You can see exactly what matched, what didn't, and how much each dimension contributed. Currently indexing: React Bits @davidhaz, Magic UI @dillionverma, Aceternity @aceternitylabs , Kokonut UI @dorianbaffier, Animate UI @animate_ui, Motion Primitives @Ibelick, assistant-ui @assistantui, bundui @bunduidotio, shadcn-dashboard. (your components, your registries, your install commands, straight from you) The goal: give coding agents a way to search before they generate. @matchcndev http://matchcn.dev
原记录的限制
- 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.