What problem does it solve? Turning a finished TabArena benchmark run's raw results.pkl files into cached, hosted (R2), and leaderboard-registered artifacts is a multi-step maintainer workflow involving metadata edits, validation, uploads, and registration that is easy to get wrong. ## Core Features & Use Cases - Inspect and validate raw results: Infers method metadata fields from raw run data and diffs them against the method's info.py MethodMetadata before processing. - Process and upload to R2: Builds and caches metadata.yaml, processed/, and results/ artifacts locally, then pushes them to the R2 bucket after a dry-run verification. - Register and log the method: Edits info.py upload fields, registers the method in the arena's methods.py collection, and records the run in BENCHMARK_LOG.md. - Use Case: A maintainer finishes benchmarking a new model like ChimeraBoost and asks to upload it; the skill inspects the run data, fixes metadata mismatches, processes and uploads the artifacts to R2, and registers the method so it appears in the benchmark. ## Quick Start Process and upload the benchmark results in output/benchmark_chimeraboost_16062026/data for the chimeraboost model and register it in the tabarena arena.
