# Codonfm Embed

Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose...

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add NVIDIA/skills --skill codonfm-embed -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill codonfm-embed -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill codonfm-embed -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill codonfm-embed -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-codonfm-embed/SKILL.md