What I Refused To Automate Last In My 39-Product Asset Pipeline

Description and use

The author chose to keep manual control over titles, keywords, duplicate checks, image quality, and pricing to prevent costly automation errors.

Problem

Automating duplicate uploads and poor metadata caused a costly cleanup of a 39-product asset pipeline, revealing the need for manual checks.

Outcome

The author now avoids costly duplicate uploads and poor metadata, maintaining a 39‑product pipeline with 856 MB output on a 1 vCPU/2 GB machine.

Workflow

• Generate vector art from prompts
• Create metadata CSVs for each platform
• Run upload_tracker to verify no pending uploads
• Manually review titles and keywords
• Manually inspect each image for quality
• Manually decide product bundling and pricing
• Package assets and generate PDFs
• Upload to platforms

Evidence

Last March I lost about six hours of upload work in one afternoon. I had a batch of 40 vectors ready for Adobe Stock, the CSV was built by my script, everything looked fine. I ran the batch, went to make coffee, came back, and ran it again because I thought the first run had failed.

Source

I refuse to automate the titles and keywords

Source

After the March incident I wrote upload_tracker.py, a plain-text ledger. Before any batch I run pending and see exactly what is not done. Nothing uploads unless the ledger says it is clean.

Source

I look at every image before it goes into the output folder. Bad hands, broken paths, stray artifacts, all of it gets caught by eyes, not code.

Source

The scripts bundle, zip, and render covers at 1280×720 with a 600×600 thumbnail, because a zip with a real cover sells and a bare file does not.

Source