A tested wrangling skill, and backlog moves to GitHub issues

DataPressr moves from three thin publishing commands to the start of an actual, opinionated skill suite for turning raw data into published datasets — and, unusually for an AI-authored playbook, the parts that can be tested now are.

/validate is no longer an LLM re-deriving a checklist from prose each run — it's a small, dependency-free script with its own test suite, checking a dataset's schema, license, and source metadata before anything gets published. A new structure skill walks through turning a messy raw source into a clean, typed dataset, written and verified against two datasets already on DataHub — including a genuine gotcha caught while parsing a 27MB multi-sheet spreadsheet (Excel formula cells silently breaking a naive parser, now documented with the fix). Its cleanup patterns are themselves a tested module, not just prose. The wrangling approach changed after testing against real data, too: an initial DuckDB-first decision got reversed for Node/TypeScript once it was clear no dataset in the project actually used DuckDB.

Every dataset now also needs a recorded license and source before it's more than a stub — see AGENTS.md for the full set of conventions and the structure skill for the playbook.

Separately: BACKLOG.md, INBOX.md, and DASHBOARDS.md are retired in favour of GitHub issues and a short NEXT.md — a queue that can actually be searched, instead of a growing markdown file.