Create an English introduction video for py-sql-cleaner

[enumura1]

Goal

Create a 2-3 minute English introduction video for py-sql-cleaner, publish it to YouTube, and embed it in the project website.

Positioning

The video should make clear that this project is not encouraging teams to bury large SQL queries inside Python files. It is a practical cleanup tool for real codebases where that already happened.

Suggested framing:

Ideally, long SQL queries should not be buried inside Python files. But in real codebases, they often are. Airflow DAGs, data jobs, legacy pipelines, and quick fixes can accumulate large embedded SQL strings that are hard to review, hard to format, and risky to clean up by hand. py-sql-cleaner helps find, format, and extract those queries safely.

Draft structure

• 0:00 Problem: long SQL embedded in Python is hard to review and maintain.
• 0:20 Reality: this happens in Airflow DAGs, data jobs, legacy scripts, and quick fixes.
• 0:40 py-sql-cleaner list: find embedded SQL blocks.
• 1:00 py-sql-cleaner format --dry-run: preview formatting.
• 1:25 py-sql-cleaner format: format safe SQL in place.
• 1:50 py-sql-cleaner extract --out-dir sql: move long SQL into .sql files.
• 2:15 Safety: skip f-strings and Jinja-like templates instead of rewriting risky code.
• 2:35 Install and links: pip, pipx, uvx, GitHub, PyPI, docs.

Deliverables

• English narration script.
• Screen recording shot list.
• Demo Python file and expected SQL output.
• Optional subtitles (.srt).
• YouTube title, description, and chapters.
• Website update to embed the YouTube video after publishing.

Notes

Keep the tone practical and concise. The target viewer is a developer who has seen messy embedded SQL in production code and wants a safe way to start cleaning it up.