MarinFold

Evaluate a MarinFold contacts-v1 checkpoint with the fixed exp89 contact benchmark. Use for checkpoint scoring, R-precision/AUC requests, comparisons with structure baselines, or reproducing contact metrics on local, CUDA, or Iris TPU execution.

Write a Zephyr/Iris data-generation pipeline that finishes in minutes (not hours) and ships a clean corpus (not a silently-corrupted one). Use when authoring or reviewing an `exp<N>_data_*/cli.py` (or any `map_shard`-based job that fetches per-row inputs and emits parquet). Covers the per-row / per-shard / per-worker / per-job decisions for wall-clock plus the fail-loud default for data quality — including **neural / accelerator (TPU) tokenizers** when the model is the cost (batched bucketed inference, `ResourceConfig.with_tpu`, and parallel scans for sequential-recurrence models like SSMs/RNNs).

Monitor/babysit a job continuously and recover on failure. Use when asked to babysit, monitor, or watch a job, pipeline, workflow, or training run.

Start, monitor, and babysit Zephyr pipeline jobs on Iris. Use when launching a zephyr job, watching it run, or restarting after failures.