add-lab

What problem does it solve?

Onboarding a new AI lab into the Lab Tracker is often manual, error-prone work that requires creating and updating multiple files, assets, and references. This Skill automates the generation of a consistent lab entry, logo scaffolding, and initial outputs, reducing setup time and keeping data synchronized.

Core Features & Use Cases

• Automated lab YAML creation: generates the lab's primary data entry with fields like name, slug, url, region, founded, type, and description.
• Logo and assets scaffolding: provisions placeholders or real logo assets and references for the lab.
• Initial outputs scaffolding: creates initial outputs entries for flagship models, papers, or datasets based on the lab's focus.
• Validation and consistency checks: ensures required fields are present and formats are correct, with notes on legacy orgs and regional classification.
• Use Case: onboard a new startup with a profile, founders, and first set of outputs, then push updates to the README and region index.

Quick Start

Run the add-lab workflow with the lab’s basic details to generate the YAML, logo, and output scaffolds.