Agent skill · nvidia
codonfm-score
Validate, prepare, or run public CodonFM Encodon masked-codon variant scoring and review compatibility of its scoring workflows. Use only when the user explicitly requests CodonFM or Encodon, or that context is already established in the conversation. Do not select this skill for a generic variant-scoring request without that context; ask for the variant and intended analysis first.
What it needs
About 4k tokens when loaded.
What this skill does
Score variants with public Encodon Run general masked-codon mutationprediction only. This produces a research signal, not a clinical diagnosis or an expression-direction prediction. Instructions First confirm CodonFM or Encodon context in the user's request or established conversation. If that context is missing, ask for any missing variant details and the intended analysis before choosing a model or inspecting model-specific files. The presence of this skill or source files alone does not establish the user's intent. For source reviews and command preparation, inspect the supplied source and metadata without installing the ML runtime. Use an available Python 3 interpreter with standard-library zipfile, json, and csv; do not assume the python alias or unzip exists. Read archive members directly with ZipFile.namelist() and ZipFile.read() where possible. If extraction is needed, use a fresh directory from tempfile.mkdtemp() or mktemp -d and preserve existing checkouts and scratch directories. Check whether rg is available; use grep or Python if it is absent. Read the source sections needed for the requested command or compatibility question. Check whether the request is executable in public v1 before installing or downloading anything. For synonymous-codon aggregation or Decodon, inspect the parser and model configuration, explain the missing feature, and finish. Do not implement the missing workflow, search private code, or keep retrying unsupported commands. Resolve the variant CSV, checkpoint, and output directory from the request and available files. Validate inputs before inference. Execution requires the project's ML dependencies and a compatible NVIDIA GPU. If a required resource is unavailable, return the validated inputs where possible and a command with the missing prerequisite identified. When scoring is requested and resources are ready, execute and verify the score arrays. A request for preparation ends with the inputs and command. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-codonfm-score