Automation of STAT table conversion for font design workflows

Build the CLI with Python 3.11 using Click for argument parsing. Use the `fontTools` library (specifically `fontTools.ttLib`) to open .ttf/.otf files and read the `STAT` table via `ttFont['STAT']`. Iterate `statTable.AxisRecordArray.AxisRecords` to collect axisTag, axisNameID, and `AxisValueArray.AxisValueRecords` for each axis. Resolve name IDs through `ttFont['name'].getName(id, platformID, platEncID, langID).toUnicode()`. Serialize the collected structure to a .stylespace JSON file matching the MutatorMath schema (keys: axes, locations, defaults). For the stub .designspace, generate an XML document using `lxml.etree`; include `<axes>` with `<axis tag="..." name="..." minimum="..." maximum="..." default="..."/>` and a `<sources>` placeholder pointing to the original font file. Implement validation by checking required fields (axisCount, valueCount) and raising Click exceptions with clear messages. Package the tool as a pip‑installable wheel, expose an entry point `stat2space`. Add a CI pipeline (GitHub Actions) that runs unit tests with `pytest` and validates output against known fixtures using `jsonschema` for .stylespace and an XSD for .designspace. Provide a `--verbose` flag that prints a human‑readable summary of extracted axes.

Automating STAT extraction eliminates repetitive parsing and transcription, ensuring that every variable font starts with a faithful axis definition. This reduces setup time, cuts the chance of human error in axis ranges, and standardizes the initial designspace, allowing downstream tooling (e.g., MutatorMath, RoboFont) to operate on a correct baseline. Consistent, machine‑generated files also lower CI build failures and streamline collaboration across foundries.