Agent skill · nvidia
codonfm-embed
Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.
What it needs
About 5k tokens when loaded.
What this skill does
Extract public Encodon embeddings Purpose Extract one frozen CLS vector per coding sequence with public Encodon v1. Support input validation, command preparation, extraction, and checkpoint selection for translation efficiency, expression, or mRNA stability modeling. Extraction does not automatically train a downstream regressor. Prerequisites Validation needs Python 3 standard library only; no GPU, weights, or API key. Execution needs the public CodonFM checkout, its requirements.txt environment, a compatible NVIDIA GPU, and local checkpoint weights. A metadata JSON is not a checkpoint. A .safetensors file needs its sibling config.json; .ckpt checkpoints are also supported by the public loader. Run python -m src.runner from the CodonFM repository root. In an isolated workspace, use supplied source artifacts; source paths below are relative to that checkout or source archive, not this skill directory. Inputs Input source precedence: explicit user prompt arguments, then supplied files/checkpoint metadata, then inspected public runner defaults. Resolve conflicting model names and checkpoint metadata before execution. Supplied 80M metadata is useful for preparing an 80M command; it does not restrict an open-ended recommendation to that size. Required for validation: a CSV. Required for extraction: the CSV, checkpoint, matching model name, and output directory. Optional: context length and batch size overrides. Checkpoint-selection questions can be answered without a CSV. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-codonfm-embed