简体中文 ·工作原理 ·已知的限制

Jev 浏览器是CodexQA中的一项技能：本地代理技能，用于检查代码写入后是否真正良好。该仓库是独立项目。同样的技能也安装在CodexQA目录中。

GUI模型浏览器自动化在每一步都会向视觉模型发送截图。识别会消耗视觉代币，循环等待模型读取图像，点击最终停在坐标上。

CodexQA Jev 浏览器从页面内置的索引中查找控件，并将可见的页面证据作为结果处理。回放、进球跑动、案件生成和网站探索共享该索引。浏览器名为 Playwright Chromium。该仓库没有针对愿景图形界面模型的基准测试。下面的行是这些成本的结构性答案。

根据TypeSafe发布的System One对比，Jev的决策比前沿LLM在同类问题上快40×–200×（对多秒LLM调用约70–500毫秒）。他们的工作流程演示速度是193.6×快，444.6%×更便宜：0.000081美元（0.114秒），0.013880美元（8.566秒）。TypeSafe调用匹配了现实世界收益的高端。Jev的输入值为**$0。

CodexQA Jev Browser

简体中文 · How it works · Known limitations

Jev Browser is one skill in CodexQA: local Agent Skills that check whether code is actually good after it is written. This repository is the standalone project. The same skill is also installed from the CodexQA catalog.

GUI-model browser automation sends a screenshot to a vision model on every step. Recognition spends vision tokens, the loop waits for the model to read the image, and the click lands on coordinates.

CodexQA Jev Browser finds controls from an index built inside the page and treats visible page evidence as the result. Replay, goal runs, case generation, and site exploration share that index. The browser is Playwright Chromium. This repository has no benchmark against vision GUI models. The rows below are the structural answers to those costs.

On TypeSafe’s published System One comparison, a Jev decision is 40×–200× faster than a frontier LLM on the same kind of question (70–500 ms, against multi-second LLM calls). Their workflow demo is 193.6× faster and 444.6× cheaper: $0.000081 in 0.114 s versus $0.013880 in 8.566 s. TypeSafe calls that pair the high end of real-world gains. Jev lists input at $0.042 per million tokens, 238× lower than Claude Fable 5.1, and does not bill output tokens. These figures are for the decision call, not for loading the page or saving the report. Source: TypeSafe and the launch post.

What raises the pass rate

The knowledge base is the main lever. Jev only chooses an indexed control, and the characters it can type are phrases already in the goal. It does not know that a Baidu cite is an ad, that the left city box is the departure city, or that a login dialog means stop. Those facts belong in knowledge//*.md.

A failed live run is usually a missing or wrong note, not a missing selector. Read the report, name the control the model should have used or avoided, and add that sentence to the matching note. hosts matches the site. keywords match the goal. general: true is attached on every run, such as the login-dialog stop. Notes are reference. The model still has to pick an index that was observed. Do not put that site’s fill rules into src/policy.ts.

Where traditional automation gets stuck