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Official agent skill

Codonfm Embed

by NVIDIA in NVIDIA/skills

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

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Codonfm Embed

skills CLI
$ npx skills add NVIDIA/skills --skill codonfm-embed -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills codonfm-embed --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bionemo-codonfm-embed .claude/skills/codonfm-embed && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
codonfm-embed
GitHub stars
3.6k
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
1,069 words
Files
13 (incl. scripts, references)
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

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

  • Works in 4 steps: Choose the requested workflow. For a… → Inspect supplied source only where… → Validate the CSV before running… → …
  • Tasks that involve Embeddings
  • SKILL.md covers Purpose, Prerequisites, Inputs and Instructions, plus 6 more sections
  • Runs Python scripts from its folder; calls python

What it does

Codonfm Embed is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts and reference files (for example `BENCHMARK.md`, `agents/openai.yaml` and `evals/evals.json`).

It sits in AI & LLM Engineering, covering Embeddings and CSV and tabular files. It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Embeddings
  • Tasks that involve CSV and tabular files

Example prompts

  • “/codonfm-embed”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Choose the requested workflow. For a checkpoint/performance question,
  2. Inspect supplied source only where needed. Confirm runner/config,
  3. Validate the CSV before running extraction. Run the bundled checker
  4. Deliver the requested preparation or execution. For preparation, return

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Codonfm Embed loads about 2.4k tokens when it runs, and up to ~3.2k if it reads all its reference files. Until then it costs about 36 tokens; SKILL.md has 1,069 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,069 words, ~2,390 tokens.

Download SKILL.mdSave it as .claude/skills/codonfm-embed/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
codonfm-embed
description
Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.
license
Apache-2.0
metadata.author
NVIDIA BioNeMo <bionemofeedback@nvidia.com>
metadata.tags
biology, codonfm, embeddings

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.

InputRequirement or default
Sequence CSVColumns id, ref_seq, value, split; extra columns allowed
idNonblank, unique IDs for unambiguous output association
ref_seqCoding sequence, uppercase DNA A/C/G/T, length divisible by three; public dataset converts uppercase U to T
valueNumeric label; use 0.0 for new extraction-only data, preserve supplied labels
splitOnly exact test values enter extraction; blank/other values are excluded
Checkpoint and modelMatch weights/config to encodon_80m, encodon_600m, or encodon_1b
Context lengthPublic runner default 2048 tokens, including CLS and SEP
Output directoryA fresh run directory with an empty predictions directory

Instructions

  1. Choose the requested workflow. For a checkpoint/performance question, read checkpoint selection and answer from public benchmark evidence. For the strongest published downstream results, prefer the public 1B random-mask checkpoint when resources allow; 80M is a demonstration or resource-constrained choice. A small labeled set alone does not establish that 80M frozen features are better. Do not download weights or inspect the entire source tree just to make a recommendation.
  2. Inspect supplied source only where needed. Confirm runner/config, src/data/codon_bert_dataset.py, src/data/preprocess/codon_sequence.py, src/inference/encodon.py, or src/utils/pred_writer.py for the relevant behavior. Read ZIP members with zipfile.ZipFile.namelist() and .read(); source inspection does not need extraction. If a checkout is needed, use a new directory from tempfile.mkdtemp() or mktemp -d, without deleting or overwriting an existing directory. For Decodon support questions, inspect runner/config and model/inference modules, cite the inspected files, explain the missing public implementation, and finish there.
  3. Validate the CSV before running extraction. Run the bundled checker below with the intended context length. Report per-row verdicts using CSV row numbers as well as IDs, since IDs can repeat. Separate excluded rows, invalid inputs, duplicate-ID warnings, and truncation. Propose fixes without silently rewriting supplied data. The checker is a preflight, not model execution or proof of biological CDS validity.
  4. Deliver the requested preparation or execution. For preparation, return a complete command with resolved paths (or clearly identified prerequisites), the test-row count, validation findings, and the output contract below. Include all task/dataset/process flags in the final answer, even if already shown in a tool call. For extraction, reuse/download the chosen checkpoint when needed, execute once resources are ready, and verify the saved arrays. If resources are missing, finish preparation and state what is missing.

Available Scripts

ScriptPurposeArguments
validate_inputs.pyRead-only CSV validation and per-row verdictsRequired CSV path; optional --context-length (default 2048)

Run the preflight with Python; CODONFM_SKILL_DIR is the directory containing this file:

bash
python "$CODONFM_SKILL_DIR/scripts/validate_inputs.py" "$CODONFM_DATA_PATH" \
    --context-length 2048

The checker prints JSON. Exit 0 means no findings, 1 means row findings to review (including exclusions/warnings), and 2 means a file/schema error. Neither warnings nor exclusions imply that the public runner will crash.

Show full SKILL.md (424 more words)Show less

Output Format

The checker emits JSON with total_rows, test_rows, excluded_rows, context_length, codon_limit, warnings, and rows. Each row records its one-based data-row number (excluding the header), ID, split, verdict, issues, sequence/value validity, and retained/lost codons. A file/schema error emits error and csv instead. These are preflight findings, not generated embeddings.

Examples

Set CODONFM_DATA_PATH to the CSV, CODONFM_CHECKPOINT_PATH to the weights, CODONFM_MODEL_NAME to the matching architecture, and CODONFM_RUN_DIR to a fresh output directory. Substitute actual paths in a prepared command:

bash
python -m src.runner eval \
    --task_type embedding_prediction \
    --process_item codon_sequence \
    --dataset_name CodonBertDataset \
    --exp_name embed_extract \
    --model_name "$CODONFM_MODEL_NAME" \
    --checkpoint_path "$CODONFM_CHECKPOINT_PATH" \
    --data_path "$CODONFM_DATA_PATH" \
    --context_length 2048 \
    --num_nodes 1 \
    --num_gpus 1 \
    --num_workers 0 \
    --val_batch_size 2 \
    --out_dir "$CODONFM_RUN_DIR" \
    --predictions_output_dir "$CODONFM_RUN_DIR/predictions"

For a low-cost demonstration, encodon_80m matches nvidia/NV-CodonFM-Encodon-80M-v1, revision 399ca9fe17b57941a7bebc6788033919b417413c, file NV-CodonFM-Encodon-80M-v1.safetensors and sibling config.json.

Outputs

  • Under --predictions_output_dir, embeddings_merged.npy contains frozen final-layer CLS vectors, shape (processed_rows, hidden_size).
  • ids_merged.npy is index-aligned: embedding row i belongs to ID row i. Use these IDs to join to the CSV; do not assume every CSV row was retained. Duplicate IDs make that join ambiguous even when extraction succeeds.
  • For the one-GPU example, verify both arrays have the expected test-row count, embeddings are finite, and width matches checkpoint config (1024 for 80M, 2048 for 600M/1B). Do not fabricate arrays for a preparation-only request.

The public checkout's downstream-model references are:

  • notebooks/4-EnCodon-Downstream-Task-riboNN.ipynb
  • notebooks/5-EnCodon-Downstream-Task-mRFP-expression.ipynb
  • notebooks/6-EnCodon-Downstream-Task-mRNA-stability.ipynb

Limitations

  • Public v1 has no Decodon model/inference implementation, Decodon notebooks, notebooks/te_predictor.py, or notebooks/mfe_predictor.py.
  • At context length 2048, retain the first 2046 codons; any remaining 3-prime sequence is lost. Increasing the flag does not validate a longer context. Disclose deliberate cropping or a separate chunking/aggregation strategy; neither is equivalent to embedding the complete sequence once.
  • --dryrun builds runtime configuration, may create directories, and needs ML dependencies; it reads neither the CSV nor the weights and is not input validation.
  • Do not claim a benchmark-trained regressor generalizes to a new organism, cell type, or assay without new labeled validation data.
  • Do not invoke this skill for a generic expression-prediction request that does not mention CodonFM or Encodon.

Troubleshooting

SymptomCause and action
Missing split columnEval requests the test split despite the dataset docstring calling this column optional; add an explicit split column to a corrected copy
Fewer output rowsBlank/non-test split values are silently filtered; set intended extraction rows to exact test in a corrected copy
Repeated output IDsDuplicate input IDs are not rejected; assign unique IDs while preserving a mapping to the original rows
Oversized sequencePreprocessing truncates at context_length - 2 codons; report retained/lost lengths and agree on a sequence-handling strategy
Missing weights or dependenciesComplete validation/command preparation; metadata and --dryrun do not substitute for weights
Merge failure on a repeated runThe writer scans .npy files; use a fresh predictions directory to avoid stale shards or merged arrays

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files (scripts, references) in skills/bionemo-codonfm-embed of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • agents/openai.yaml
  • evals/evals.json
  • evals/files/codonfm_source.zip
  • evals/files/encodon_checkpoint.json
  • evals/files/sequences.csv
  • evals/files/sequences_edge_cases.csv
  • references/checkpoint-selection.md
  • scripts/validate_inputs.py
  • skill-card.md
  • skill.oms.sig
  • tests/test_validate_inputs.py

Open the folder on GitHubat commit 14a98ae

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 10, 2026.

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Works with

Categories

Questions about Codonfm Embed

What does Codonfm Embed do?

Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling. Codonfm Embed is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.

When should I use Codonfm Embed?

Codonfm Embed fits situations like: tasks that involve Embeddings; tasks that involve CSV and tabular files.

How do I install Codonfm Embed in Claude Code?

Run `npx skills add NVIDIA/skills --skill codonfm-embed -a claude-code`. Or copy the skill folder (skills/bionemo-codonfm-embed in NVIDIA/skills) into .claude/skills/codonfm-embed in your project. Claude Code loads it when a task matches its description.

How do I install Codonfm Embed in Codex?

Run `npx skills add NVIDIA/skills --skill codonfm-embed -a codex`. Or copy the skill folder (skills/bionemo-codonfm-embed in NVIDIA/skills) into .agents/skills/codonfm-embed in your project. Codex loads it when a task matches its description.

Can I use Codonfm Embed in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill codonfm-embed -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/codonfm-embed, .gemini/skills/codonfm-embed, .github/skills/codonfm-embed and .opencode/skills/codonfm-embed in your project.

What does Codonfm Embed need to run?

Going by SKILL.md and its folder, Codonfm Embed needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Codonfm Embed access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Codonfm Embed safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Codonfm Embed use?

Codonfm Embed is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Codonfm Embed use?

About 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 789 tokens, read only when the agent opens those files.

What are the alternatives to Codonfm Embed?

Skills that share tags, products or a category with Codonfm Embed: Unimol (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars), Rotary Embedding Kernel (ZJLi2013/awesome-kernel-skills, 102 stars), Embeddings via 9Router (decolua/9router, 31k stars) and Keirouter (mydisha/keirouter, 147 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Codonfm Embed?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.