I build a lot of data pipelines, and I've had to deal with too many inconsistent "source to target mapping specs" (usually Excel files) in integration and data projects in my life. They're too opaque for AI coding tools to get consistent results for generating implementations, suggesting tests, making test data etc. So I made "Satsuma" (https://equalexperts.github.io/satsuma-lang/)

At first glance, it's just a nice version-controllable, parseable DSL. But I also made succinct prompts with the grammar that lets LLMs produce and reason about Satsuma, a language server, CLI tools for the AI tools to use to navigate specs token-efficiently, reason about lineage, pretty viz in vscode plugins/syntax highlighting, some agent skills etc. There are metadata conventions for succinctly representing a lot of quirky formats and capturing common analytics conventions (scd2/Kimball/datavault/etc c.)

Yes, yes, I may have gotten carried away.

But I'm finding it really useful as a specification tool in projects for reverse engineering mappings from code/workflows, generating new code (dbt, Spark etc.)

This definitely isn't something I would've had the bandwidth to push this far before AI!