What problem does it solve? Publishing new benchmark results to the TabArena leaderboard requires a multi-step, error-prone workflow: regenerating website artifacts, verifying model type classification, safely swapping the Space repo's data directory, and bumping version history. This Skill operationalizes that entire maintainer workflow so nothing is missed. ## Core Features & Use Cases - Model-type pre-check: Verifies newly added models classify into the correct family (Foundation Model, Tree-based, etc.) in website_format.py before regeneration, preventing models from shipping as "Other". - Artifact regeneration: Runs run_generate_website_artifacts.py in the background with monitoring, distinguishing harmless ray shutdown noise from real failures and validating output structure (480 leaderboard CSVs, 8 entrant pools, 2400 explorer HTML files). - Safe data refresh: Deletes the old data/ subtree in the Space repo before copying new artifacts, then verifies the git diff contains only modifications. - Version history bump: Adds a dated entry to website_texts.py with correct verified/unverified model wording. - LFS storage recovery: Includes a HEAD-aware purge script to resolve HuggingFace Space 1 GB storage-limit rejections without breaking the live revision. - Use Case: After uploading a new model's benchmark results, a maintainer asks to refresh leaderboard-testing; the Skill pre-checks the model's type classification, regenerates artifacts, swaps the Space data, bumps the version, and hands off for commit and push. ## Quick Start Update the leaderboard-testing Space with the latest results for the newly added TabSwift model.