How Two Custom Skills Eliminate Manual Photo Downloads

After events, organizers often share a cloud album link containing dozens or hundreds of photos, and manually opening each picture to save the original image is time‑consuming, especially on mobile or in a browser.

Previously the author would search GitHub for open‑source scripts and run a Python program, which works for developers but requires reading a README, installing dependencies, configuring parameters, and handling environment issues—steps that are unfriendly for ordinary users.

To solve this, the author created two Skills that encapsulate the entire workflow: alltuu-downloader: batch‑downloads albums from alltuu / m.alltuu. photoplus-downloader: batch‑downloads albums from PhotoPlus.

Using an AI Agent that supports Skill installation, you simply send the album URL and a target directory to the Agent. The Skill then parses the album page, extracts all image URLs, and downloads the original files to the specified local folder.

The Skills are hosted in a public repository (https://github.com/chujianyun/skills). Installation is done by sending the Agent a command like:

帮我安装这个 Skill: https://github.com/chujianyun/skills/tree/main/skills/alltuu-downloader

or

帮我安装这个 Skill: https://github.com/chujianyun/skills/tree/main/skills/photoplus-downloader

After installation, the usage is straightforward: send the album link and the desired save path, for example:

帮我下载这个相册的原图：[这里粘贴相册链接]，保存到：[你想要保存的路径]

The Agent will start downloading, and you can query progress with a simple message such as “现在下载进度怎么样了？” When the Agent reports completion, the images are ready in the target directory.

Additional tips include preserving the full URL (including parameters) when copying links, specifying the save directory in advance, expecting slower downloads for large original files, respecting access permissions, and noting that the Skills depend on the current album page structure—if the platform changes, the Skill may need updating.

The author frames this as an example of the “AI First” mindset: for repetitive, rule‑based tasks, a small AI‑driven tool can reclaim fragmented time and improve efficiency.