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Beauty Everywhere Oracle Cards

Beauty Everywhere Oracle Cards

Oceanhouse Media

Lifestyle免费v1.6.20
App Store
评分

4.6

794 条评分

星级

★★★★★

最近更新

2026年4月3日

发布日期

2022年8月6日

更新内容

v1.6.20

New content! Bug fixes and improvements New Special Offers Redemption feature

应用信息

开发者
Oceanhouse Media
分类
Lifestyle
价格
免费
版本
1.6.20
App ID
1540183831

简介

Get over 85 of our bestselling beautifully illustrated oracle card decks in one app. No more tedious searches in the App Store sorting through millions of oracle and tarot card apps to find new releases or products from your favorite authors. Now you can easily access all the Beauty Everywhere and Blue Angel oracle card apps all in one place! All Blue Angel and Beauty Everywhere products in one app! - *NEW* Filter My Library and Browse screens by author - Find the latest newly released and featured oracle card decks - Browse all oracle card decks complete with details and screenshots - Purchase with ease within the app - Enjoy savings with regular oracle deck sales and promotions - Sort oracle card apps by your favorite authors, titles, artists, and publishers - View screenshots and more details of each product before purchase - Launch products already owned or purchase new apps easily - Quickly access your oracle decks in one place in ‘My Library’ Oracle app features: – Give readings anywhere, anytime – Choose between different types of readings – Save your readings to review at any time now on iCloud! – Email readings to friends – Browse the entire deck of cards – Flip cards over to read each card’s meaning – Get the most out of your decks with the informational guidebook – Set a daily reminder for a reading Authors Featured: Alana Fairchild Alberto Villoldo Angela Hartfield Claudio Olivos Colette Baron-Reid Denise Jarvie Izzy Ivy Jade Sky Jill Pyle John Holland Karen Kripalani Kelly Sullivan Walden Lucy Cavendish Melissa Virtue Michelle Buchanan Radleigh Valentine Stacey Demarco Suzy Cherub Toni Carmine Salerno

下载量预测

专业 · 预览

预估总下载量

59K40K113K
保守估计乐观估计

863

低 / 月

1K

预估 / 月

2K

高 / 月

基于794 条评分
假设评分率1.4%
应用年龄46 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。