When language learners learn vocab, there are two main processes:
1. Use a premade list of the top x words. Pro: they are guaranteed to be common words. Con: Once you make it past 2000 or so, they might not show up depending on what sort of niche things you read/watch/talk about
2. Look up random words as you come across them, and learn those. Pro: These are words you really saw. Con: You don't know how common that word is, maybe this is the only time in your life that you'll see it
I made a web app that lets you note down words that you come across and might want to learn, and then generates a learning order of those words based on a variety of frequency lists, as well as linking offsite for sample sentences etc. It allowed me to pass the JLPT N1 with just 6k known words (people usually need 8-10k)
Shouldn't be too difficult. The main thing you'd need is a dictionary that's as close as possible to covering the entire language (ie a digital-first one rather than a digital representation of a paper dictionary), and at least one but ideally several frequency lists. Ideally in the JSON format that Yomichan uses, as that's what the script for generating the data currently expects, but if not then the data can always be massaged. Then you just need to replace the specifics in the app (hardcoded frequency list names etc) with the stuff for your language.
I made a web app that lets you note down words that you come across and might want to learn, and then generates a learning order of those words based on a variety of frequency lists, as well as linking offsite for sample sentences etc. It allowed me to pass the JLPT N1 with just 6k known words (people usually need 8-10k)
vocab.c-ehrlich.dev