One Prompt Engine, 39 Products: The Scripts That Package Stock, Stickers, Templates

The moment I nearly quit was a Tuesday night in front of Adobe Stock's upload screen. I had 58 assets ready, I'd spent weeks making them, and the CSV got rejected in about four seconds. Wrong header. Adobe wants a 5-column format. Vecteezy wants 4 columns. Dreamstime wants 15. I had written one CSV and assumed a stock site is a stock site. They are not. I re-ran my batch upload to "fix it" and double-posted half the folder, which is exactly the kind of thing that gets an account flagged. That night I stopped making art for a week and wrote scripts instead.

Here is what I learned, with the numbers attached.

Every rejection I got early on was a header problem. Adobe's bulk upload expects exactly 5 columns in a fixed order. Vecteezy expects 4. Dreamstime expects 15. Get one wrong and the file is rejected before anyone looks at your art.

So I wrote make_metadata.py. It takes a folder of files and a platform flag and writes the header that platform demands, and nothing else. Three commands:

python make_metadata.py./my-assets --platform adobe --out metadata.csv python make_metadata.py./my-assets --platform vecteezy --out vecteezy.csv python make_metadata.py./my-assets --platform dreamstime --out dreamstime.csv Enter fullscreen mode Exit fullscreen mode It uses only the Python standard library. No pip install. You can pass a titles.json if you have real titles and keywords, and you should, because the fallback title-cases the filename, which is fine for a first pass and not fine for submission. Reviewers see lazy titles and they behave accordingly.

This is the one nobody warns you about. You upload 40 files, something fails at file 31, you fix it and re-run the batch. Now 30 files exist twice. Platforms treat that as spam behavior. I did it once and got lucky. I decided I would never do it again.

python upload_tracker.py pending items.txt python upload_tracker.py done "asset-001.jpg" python upload_tracker.py status Enter fullscreen mode Exit fullscreen mode Before any batch, I run pending and upload only what it shows. It also uses the standard library alone. It is the least interesting script in the set and the one that has saved me the most trouble.

I had real content sitting in Markdown files. Nobody buys a raw.md file. md2pdf.py turns one Markdown file into a styled PDF with headings, tables, lists, and working links, in one command:

python md2pdf.py content.md product.pdf "My Product Title" Enter fullscreen mode Exit fullscreen mode It uses reportlab, one of only two third-party packages in the whole kit. The other is Pillow. Everything else is standard library, because every dependency is something that breaks on a small server at 2 AM.

My first sale was $0.99 and it came from a product that had a real cover. Before that, bare zips sat unsold. The difference was not the content, it was the presentation.

pack_product.py bundles a product in one step:

python pack_product.py my-product "My Product" "A short subtitle" 9 Enter fullscreen mode Exit fullscreen mode It writes the zip, a 1280x720 cover, and a 600x600 thumbnail. Covers go in listings, thumbnails go in grids, and both sizes matter because marketplaces crop and scale aggressively. Output lands in a _system folder by default, and you can redirect it with --dir or a PACK_ROOT environment variable so you never edit the script.

I run this on a tiny server with no GPU, and it works because of habits, not hardware:

project/ assets/ raw output output/ ready-to-submit files + metadata.csv packs/ finished zips + covers research/ notes and sources scripts/ these tools Enter fullscreen mode Exit fullscreen mode One rule I learned the hard way: never delete research/. My scrape logs and notes are the raw material for future products. I cleaned that folder out once to "tidy up" and lost weeks of source material. It will not happen twice.

That pipeline has produced about 1,700 assets, 58 are live on Adobe Stock, and the first sale was $0.99, which I keep as a reminder that the first dollar matters more than the size of it. The scripts packaged 39 products, about 856 MB of output, sitting under roughly 19,000 lines of pipeline code and 300+ pages of research behind the rules they enforce.

I am not going to pretend the numbers are bigger than they are. What I can say is that every rule in the scripts came from a real rejection, a real double-post, or a real out-of-memory crash, and the pipeline runs unattended on hardware that costs almost nothing.

I put the four scripts, the folder layout, and the RAM notes together into one kit called digital asset factory. It is the packaging layer I wish I had on that Tuesday night, and it plugs into a free stack if you want to grow it into a full pipeline. If it saves you one rejected CSV, it has done its job.

Building digital products? Free guides & kits for digital product sellers.