# I spent hours on App Store screenshots, so I built a tool that makes them

> Source: <https://dev.to/oshio_0bffeb2fb927538e835/i-spent-hours-on-app-store-screenshots-so-i-built-a-tool-that-makes-them-6a9>
> Published: 2026-09-25 05:34:02+00:00

The most useful thing I learned building this: if you want an image model to

work well with text, don't let it touch the text at all. Print the text first,

then let the model paint around it.

When my app was ready for the App Store, I thought the hard part was over. Then

I got to the screenshots. Deciding what each one should say, finding a device

frame, lining everything up, exporting at the exact size Apple wants. None of

it was hard on its own, but it took me hours.

So I built sukuji. You upload a few screens from your app and get five App

Store screenshots back at 1290x2796, each with a headline, your screen in a

device frame, and a designed background.

Under the hood, Gemini plans the set and writes the headlines, and GPT Image

paints the artwork. Getting those two to cooperate taught me a few things.

Image models have gotten much better at text, especially in English. But I

still didn't want the model writing my headlines. I wanted the same typeface

and size across all five images, a font I chose rather than one it picked, and

text that doesn't change shape every time I regenerate. And a single wrong

letter means redoing the whole image. So the headline was always typeset in

code and added afterwards.

That created a different problem. The model had no idea where the headline

would go, so it happily put a floating object right where the words would

land. Telling it to "leave the top of the image empty" helped a little, but not

reliably.

What worked was flipping the order. Now the headline is printed onto the canvas

first, next to the phone, and only then does the model paint. It can see the

words, so it works around them instead of guessing.

Even with the text printed, objects kept crowding it. My prompt said nothing

may touch the letters. The model obeyed: things stopped just short of the

words, practically leaning on them.

Technically, nothing touched. The fix was to stop describing a line and start

describing a space: a clear zone around the text, measured out, with the rule

that coming close counts as failing. Once "close" was defined as a failure, the

crowding mostly stopped.

For the opening image there's no phone, just the character from your app on a

plain background. My prompt told the model to replace that plain background

entirely with a scene.

It did, headline and all. The text I had printed onto the canvas was gone. The

fix was one sentence: the printed headline is the one exception, and it stays

exactly as it is.

All three lessons are the same lesson, really. Image models take your words

literally, so the exceptions have to be spelled out.

Someone on Reddit pointed out that screenshot tools fail when the headline just

restates the icon: "Track your habits" for a habit app. They were right.

Now each headline has to come from something specific on that screen, and the

model asks itself one question before keeping a line: would this work just as

well for a competitor? If yes, it rewrites it.

I launched in Japan first and added English later. After two weeks:

That cost is why it's limited to two runs a day. It's free for now, with no

account needed.

I'd love to hear where it falls short, especially if the headlines come out

generic for your app.