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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
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add NVIDIA/skills --skill bionemo-codonfm-score -a cursor -y
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.
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.
Run general masked-codon mutation_prediction only. This produces a research
signal, not a clinical diagnosis or an expression-direction prediction.
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. If variants are missing, report the required schema; do not invent variants or silently switch to a public dataset.
Default to the public 80M checkpoint for demonstrations:
nvidia/NV-CodonFM-Encodon-80M-v1, revision
399ca9fe17b57941a7bebc6788033919b417413c, file
NV-CodonFM-Encodon-80M-v1.safetensors and sibling config.json.
Reuse an existing checkpoint or download it when needed for the requested work.
Preserve an explicitly requested model size.
src/runner.py, src/data/mutation_dataset.py, and
src/inference/encodon.py exist.encodon_80m, encodon_600m, or encodon_1b as
--model_name. The public parser lists larger names, but its model
configuration does not implement them..ckpt file, or a .safetensors file with
sibling config.json. Input preparation can use a planned path.Require these CSV columns:
id: unique row identifier.ref_seq: reference coding sequence, not genomic DNA with introns, UTR-only
sequence, or protein sequence.ref_codon and alt_codon: three-nucleotide codons.codon_position: zero-based codon position relative to the CDS.With --extract-seq, MutationDataset extracts an appropriate sequence window
from ref_seq; it does not derive or require alt_seq.
Before running, normalize sequences and codons to uppercase DNA (A/C/G/T),
require CDS lengths divisible by three, and check every row satisfies:
0 <= codon_position < len(ref_seq) / 3
ref_seq[3 * codon_position : 3 * codon_position + 3] == ref_codon
The public extractor asserts the second condition and otherwise stops the job.
Set CODONFM_DATA_PATH to the variant CSV, CODONFM_CHECKPOINT_PATH to the
checkpoint, and CODONFM_RUN_DIR to your chosen output directory:
Use the interpreter from the configured ML environment for inference. The
example uses python; substitute that environment's interpreter path if the
alias is unavailable.
python -m src.runner eval \
--exp_name variant_scoring \
--model_name encodon_80m \
--checkpoint_path "$CODONFM_CHECKPOINT_PATH" \
--data_path "$CODONFM_DATA_PATH" \
--process_item mutation_pred_mlm \
--dataset_name MutationDataset \
--task_type mutation_prediction \
--extract-seq \
--mask_mutation \
--num_nodes 1 \
--num_gpus 1 \
--num_workers 0 \
--val_batch_size 2 \
--out_dir "$CODONFM_RUN_DIR" \
--predictions_output_dir "$CODONFM_RUN_DIR/predictions"
Do not remove --mask_mutation: without it, the reference codon remains
visible at the scored position and invalidates masked-codon LLR scoring.
For preparation requests, inspect the CSV directly against the input schema
and reference-position checks above, then report the rows checked and provide
the scoring command. Extra columns are allowed; use --ref_seq_col if the
reference sequence has a different column name. These checks do not require
the ML runtime. The command above performs inference when resources are ready.
The existing --dryrun optionally builds runtime configuration and skips
execution. It requires the ML dependencies, can create the prediction directory,
and does not read the CSV or load weights. Do not use it as evidence that inputs,
checkpoint compatibility, or prediction quality have been validated.
--predictions_output_dir receives:
ref_likelihoods_merged.npyalt_likelihoods_merged.npylikelihood_ratios_merged.npyids_merged.npyLoad the arrays with NumPy and align scores by ids_merged.npy. The reported
LLR is log p(ref_codon) - log p(alt_codon); a larger positive value means the
alternate codon is less probable in context. It does not say whether
expression goes up or down.
Keep the final answer concise and self-contained, with the requested command or compatibility conclusion near the start. For command preparation, include each row's validation result, the complete command, all four output filenames, and the LLR definition and sign interpretation. Cite the inspected source locations for the command, outputs, and scoring semantics. State whether inference ran; report numerical scores only when execution produced them.
mutation_prediction handles both synonymous and missense changes.missense_prediction, missense_inference, MissenseDataset,
mutation_pred_clm, --organism_token, or --causal; those are newer
unavailable public-release features.