The 4 silent rejection bugs we hit selling across 5 digital platforms — and the pure-Python linter we built to catch them

Description and use

An automated CLI linter that checks cover geometry, disclosures, regulatory compliance, and pricing for digital products sold on multiple platforms.

Problem

Digital sellers face silent failures on each platform (image geometry, disclosure, regulatory, pricing) that cause rejection or compliance issues.

Outcome

Reduced human effort and risk of rejection; Day 7 experiment saved ¥70,000 in human labor.

Workflow

• Collect product assets in a directory
• Run python listing_lint.py --dir ./my_products
• Tool parses image headers with struct to verify dimensions
• Checks disclosure tags per platform rules
• Scans for affiliate links, trademarks, forbidden keywords
• Validates pricing metadata for currency, free or negative values
• Reports any violations for correction

Evidence

After catching these by hand on Day 7 across dozens of SKUs, we wrote a single-file, zero-dependency Python CLI tool called Listing Lint.

Source

Current experiment score on Day 7: Human ¥70,000 — AI ¥0.

Source