App Store keywords from the real competition
这是 Everton Carneiro 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
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 showcase by Everton Carneiro。作者自述，本站未独立复现。
Everton Carneiro
记录日期：2026-09-18。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。
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
I built a tool that finds App Store keywords by reading the competition, and uses Jev to judge them. What it actually does: 1. Turns the app's own listing into a handful of search queries, with Jev filtering out the ones nobody would type. 2. Runs those searches on the App Store. Whatever ranks is the candidate pool. 3. Jev judges each candidate: is this really an alternative to the app, or does it just share a word? The lookalikes get dropped. 4. The strongest survivors become the competitor set it mines for keywords. No competitor list to maintain, nothing hallucinated: competitors are whoever Apple already ranks, minus the ones Jev rules out.
来源原文
I built a tool that finds App Store keywords by reading the competition, and uses Jev to judge them. What it actually does: 1. Turns the app's own listing into a handful of search queries, with Jev filtering out the ones nobody would type. 2. Runs those searches on the App Store. Whatever ranks is the candidate pool. 3. Jev judges each candidate: is this really an alternative to the app, or does it just share a word? The lookalikes get dropped. 4. The strongest survivors become the competitor set it mines for keywords. No competitor list to maintain, nothing hallucinated: competitors are whoever Apple already ranks, minus the ones Jev rules out.
原记录的限制
- Task accuracy is bounded by the precision of the defined candidate choices
- Third-party external dependencies and network latency may affect total workflow duration