BankerInterface/youtube-seo-optimizer

The repo offers a free YouTube SEO optimizer that audits video metadata and returns an SEO score, with 218 stars on GitHub. It checks titles, descriptions, tags, chapters, and keyword usage to suggest ranking improvements. The tool runs locally, so no API keys or external services are required. Its primary output is a numeric score and a list of actionable changes.

The optimizer parses a video's metadata using Python's standard libraries and regular expressions. It tokenizes the title and description, counts keyword density, and verifies that chapters follow the required timestamp format. Each element receives a weighted contribution to an overall SEO score, based on heuristics defined in the code. The script then prints a report highlighting missing tags, low‑frequency keywords, and chapter formatting errors.

The design keeps everything self‑contained: a single Python script, optional command‑line arguments for input files, and no external dependencies beyond the Python standard library. Users supply a JSON or CSV export of their video metadata, and the script returns a JSON report that can be fed into other tools. Because it runs offline, it respects privacy and avoids rate‑limit issues that cloud‑based analyzers face.

If you build a video publishing pipeline, you can drop this script into your CI step to catch SEO problems before upload. Researchers studying YouTube algorithmic factors can use the score as a quantitative label for large video datasets. The tool also serves as a baseline for custom optimizers that need a quick, reproducible audit.

TakeawayRun the script on your metadata to get a concrete SEO score and specific fixes before publishing.