Loading...
Loading...
Found 3 Skills
Prepare a research artifact package for conference artifact evaluation, reproducibility review, badges, supplementary material, or post-acceptance artifact release. Use this skill whenever the user needs install instructions, reviewer-facing reproduction commands, Docker or environment checks, data/checkpoint packaging, hardware/runtime estimates, anonymized or public artifact metadata, artifact evaluation forms, or a claim-to-artifact reproducibility audit for ML/AI venues.
Make research code runnable and repeatable for artifact review by auditing README, dependencies, seeds, entrypoints, data instructions, result commands, and smoke tests with repro_check.py. Use for smoke tests, seed pinning, environment capture, reproducibility checks, or works-on-my-machine cleanup.
Run N independent attempts at the same task in parallel, pick the strongest as the base, and graft the best parts of the others into it. Use when one attempt at a non-trivial artifact could land on the wrong shape, or for /ds-best-of-n, 'try a few versions', 'best of 3'.