Let ChatGPT build your Tana Supertags!

A fast way to jump-start Tana “super tag” systems is to feed ChatGPT a domain (like sales or reading) and then paste ChatGPT’s pre-formatted output into Tana so tags and fields appear with minimal manual setup. The key trick is that Tana can auto-generate tags and populate fields when pasted text follows a specific formatting pattern—using a concept line preceded by “%Tana%” and then indented field lines with “double colon” values. In the example, a block describing “The Hunger Games” with a hashtag plus indented bullets for year, author, and genre becomes a fully populated node: the book tag is created if it doesn’t already exist, and the corresponding fields fill in automatically.

That auto-tagging capability becomes more powerful when ChatGPT is used for schema brainstorming. Super tag systems are described as collections of super tags in Tana that organize a life domain—such as books and reading status, or a sales CRM—by linking related concepts (e.g., books → authors → genres → reading status; or products → customers → leads → sales). Instead of manually enumerating every concept, the workflow asks ChatGPT to propose the concepts needed for a given system. For a sales database, ChatGPT suggests core entities such as products, sales, leads, customers, and employees.

The next step turns those concepts into something Tana can ingest. ChatGPT is prompted to output a schema where each concept is written in singular form (e.g., “customer,” “product,” “lead”) and each concept’s field definitions are written as indented lines. Crucially, every concept block is preceded by a “%Tana%” marker so Tana recognizes what should become a tag template. When that formatted text is copied into Tana, the fields appear, but the fields may not yet be attached to the corresponding super tag template.

To connect fields to the right super tag, the workflow uses an intermediate setup: paste the schema into a simple text editor (avoiding Microsoft Word), create an example instance (like “Jim Smith” for a customer), indent the relevant fields under that instance, and then use Tana’s “sparkly star” control to add each field to the super tag’s template. Once the template is populated, the system can auto-collect values and be reused across future entries.

The practical takeaway is that ChatGPT removes much of the grunt work—coming up with candidate concepts and generating a Tana-ready formatting skeleton—while Tana handles the final configuration and relationships. The approach is presented as flexible: users can request more tags, remove unnecessary ones, or adapt the schema to other domains (productivity, networking, note-taking) as long as the formatting rules for Tana paste are respected.