Official agent skill

Evo2 Nim

by NVIDIA in NVIDIA/skills

Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice.

OfficialApache-2.0Auto-check: notesDevOps & Cloud

Install Evo2 Nim

skills CLI
$ npx skills add NVIDIA/skills --skill evo2-nim -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills evo2-nim --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-evo2-nim .claude/skills/evo2-nim && 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
evo2-nim
GitHub stars
3.5k
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
930 words
Files
14 (incl. scripts, references)
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice.

  • Works in 3 steps: Select the requested mode. For hosted… → When the user asks to run generation,… → Report the generated DNA (or its file…
  • Genomic sequence generation
  • SKILL.md covers Instructions, Choose Mode, Examples and Local Docker Requirements, plus 3 more sections
  • Runs Python scripts from its folder; calls docker and python; reaches health.api.nvidia.com; needs NGC_API_KEY and NVIDIA_API_KEY

What it does

Evo2 Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts and reference files (for example `BENCHMARK.md`, `config/skillspector-baseline.yml` and `evals/config.yml`). Compatibility notes: requests=2.28; numpy=1.24

It sits in DevOps & Cloud, covering Bioinformatics, Containers and Microservices. It works with NVIDIA AI Platform and Docker. 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

  • Genomic sequence generation
  • Hosted generation
  • Local Docker deployment
  • Local forward passes

Example prompts

  • “/evo2-nim”

Requirements

  • Python 3
  • Docker
  • A credential in NGC_API_KEY
  • A credential in NVIDIA_API_KEY
  • Compatibility (from SKILL.md): requests>=2.28; numpy>=1.24
  • Pre-approved tools (allowed-tools): Bash, Read, Write, AskUserQuestion

Workflow steps

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

  1. Select the requested mode. For hosted generation, go directly to the
  2. When the user asks to run generation, execute the client and inspect its
  3. Report the generated DNA (or its file for long sequences), actual

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • AskUserQuestion

    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:

    • docker
    • python

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • health.api.nvidia.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NGC_API_KEY
    • NVIDIA_API_KEY

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

  • Compatibility

    requests>=2.28; numpy>=1.24

    From compatibility in the SKILL.md frontmatter.

Context cost

Evo2 Nim loads about 2.4k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 74 tokens; SKILL.md has 930 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
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
~6.1k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:120
    Use shell env first; source repo-root `.env` only if present. Do not invent a
  • NoteMentions a .env fileSKILL.md:125
    [ -f .env ] && . ./.env
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, AskUserQuestion

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 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 930 words, ~2,424 tokens.

Download SKILL.mdSave it as .claude/skills/evo2-nim/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
evo2-nim
description
Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.
allowed-tools
Bash, Read, Write, AskUserQuestion
compatibility
requests>=2.28; numpy>=1.24
license
Apache-2.0 AND CC-BY-4.0

Evo 2 NIM

Use Evo 2 for DNA generation and, locally, layer-output extraction. Load supplemental files only when needed:

  • references/api.md: exact schemas, layer names, Docker flags, hardware notes.
  • references/science.md: genomic use cases, limits, and interpretation.
  • references/parameters.md: generation/forward parameter effects.
  • references/validation.md: DNA, probability, timing, and tensor checks.
  • references/examples.md: compact hosted/local request patterns.

Instructions

For generation, use scripts/generate.py to execute the request, validate the response, and save its artifacts. Resolve the script path relative to this skill's directory and choose an output directory in the user's workspace. Use the user's sequence and requested parameters; the example below is only a smoke test.

  1. Select the requested mode. For hosted generation, go directly to the generation example; Docker setup and local forward passes are separate tasks.
  2. When the user asks to run generation, execute the client and inspect its exit status and result. Writing a script alone does not complete that request.
  3. Report the generated DNA (or its file for long sequences), actual elapsed_ms, sampled-probability summary, seed, and artifact paths from the successful run. Read the saved response or metrics if any result is unclear.

If the request or validation fails, report the actual failure and any diagnostic files. Do not replace an unavailable API response with example values. For a code-only request, provide the command without making an inference call.

Choose Mode

Honor NIM_API_MODE when it is set. Accepted values are hosted and local. If it is unset, treat an explicit EVO2_NIM_URL as local; otherwise ask when the requested mode is unclear:

Hosted NVIDIA API or local Docker Evo 2 NIM?

  • Hosted generation: https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate
  • Local base URL: $EVO2_NIM_URL, falling back to http://localhost:8000
  • Local generation: $EVO2_NIM_URL/biology/arc/evo2/generate
  • Local forward/layer outputs: $EVO2_NIM_URL/biology/arc/evo2/forward

Always resolve local health and inference routes from EVO2_NIM_URL when it is present. localhost works only when the caller and NIM share a network namespace; a caller in a separate container usually needs a service URL such as http://evo2-nim:8000. Do not silently switch modes when the selected endpoint is unavailable. Report the failed endpoint and fix its configuration.

The hosted docs expose generation. /forward is documented for local Docker; do not invent a hosted /forward endpoint. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed.

Examples

Normalize prompts before sending. Use A/C/G/T unless ambiguous bases are a deliberate modeling choice and clearly reported.

For a hosted generation request, run the bundled client with the user's inputs (the script path below is relative to the skill directory):

bash
python scripts/generate.py \
  --mode hosted \
  --sequence ACTGACTGACTGACTG \
  --num-tokens 64 --seed 1 \
  --temperature 0.7 --top-k 3 --top-p 0.0 \
  --output-dir /path/to/workspace/evo2-output

For an already-ready local NIM, use --mode local; the client resolves EVO2_NIM_URL and sends no Authorization header. It never switches endpoints after a failed request. Set --timeout for a longer read if the user requests a larger generation; failed requests are not automatically resubmitted.

The client saves request.json, the actual response.json, generated.fasta, and metrics.json in the chosen output directory. It also saves the exact response body in response.raw before checking HTTP status or parsing JSON, so diagnostics survive malformed JSON and non-finite probability/timing values. It validates the requested number of generated bases, A/C/G/T alphabet, finite sampled probabilities in [0, 1], and nonnegative timing before printing a successful summary. Existing directories are never reused, even if empty. Choose an output directory that does not exist; the client creates it atomically so concurrent runs cannot overwrite each other's artifacts. The FASTA contains generated bases only, not the input prompt prepended again.

sampled_probs is requested by the client and summarized with count/min/max/mean; the full values stay in the saved response. A missing or malformed probability array is a validation failure, not permission to invent confidence values. Only request enable_logits in a custom request when needed; logits can make responses large. See references/api.md for custom payloads. random_seed supports development reproducibility, not biological certainty.

Show full SKILL.md (271 more words)Show less

Local Docker Requirements

Evo 2 local deployment requires FP8-capable GPUs. Do not present A100 as compatible; A100 can pull the image but fails warmup because FP8 requires compute capability 8.9 or higher.

  • Default 40B: 2x H100 80 GB or 1x H200 141 GB. Use NIM_TEST_GPUS=0,1 for 2x H100, or NIM_TEST_GPUS=0 for one H200.
  • 7B fallback: set NIM_VARIANT=7b; supported GPUs include H100, H200, RTX 6000 Ada, and L40S.
  • Approximate disk: 110 GB for 40B, 50 GB for 7B.

Use shell env first; source repo-root .env only if present. Do not invent a cache default or drop the NVIDIA_API_KEY fallback.

bash
set -a
[ -f .env ] && . ./.env
set +a

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

# 40B default: 0,1 for 2x H100; set 0 for a single H200.
export NIM_TEST_GPUS="${NIM_TEST_GPUS:-0,1}"
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 700 "${LOCAL_NIM_CACHE}"   # owner-only; if the NIM runs as a different UID, add -u "$(id -u)" to docker run

# For 7B: export NIM_VARIANT=7b; export NIM_TEST_GPUS="${NIM_TEST_GPUS:-0}"
docker run --rm -it --name evo2-nim \
  --runtime=nvidia \
  --gpus "\"device=${NIM_TEST_GPUS}\"" \
  -e NGC_API_KEY \
  -e NIM_VARIANT \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/arc/evo2:2

Readiness:

bash
evo2_nim_url="${EVO2_NIM_URL:-http://localhost:8000}"
until curl -sf "${evo2_nim_url%/}/v1/health/ready"; do sleep 10; done

If RTX PRO 6000 Blackwell Workstation fails with no Transformer Engine attention backend, treat it as outside the current validated matrix and rerun on a documented GPU/runtime.

Local Forward Pass

Forward returns base64-encoded NPZ tensors.

python
import base64
import io
import os
import numpy as np
import requests

mode = os.getenv("NIM_API_MODE", "local")
if mode != "local":
    raise RuntimeError("Evo 2 /forward is available only in local mode")
nim_url = os.getenv("EVO2_NIM_URL", "http://localhost:8000").rstrip("/")
sequence = "ACTGACTGACTG"  # Replace with the user's DNA sequence.
sequence = "".join(sequence.upper().split())
if not sequence or set(sequence) - set("ACGT"):
    raise ValueError("Expected nonempty A/C/G/T DNA")
payload = {
    "sequence": sequence,
    "output_layers": ["output_layer", "decoder.layers.3.self_attention"],
}
response = requests.post(
    f"{nim_url}/biology/arc/evo2/forward",
    headers={"Content-Type": "application/json"},
    json=payload,
    timeout=300,
)
response.raise_for_status()
npz_bytes = base64.b64decode(response.json()["data"])
with open("evo2_forward_outputs.npz", "wb") as handle:
    handle.write(npz_bytes)
arrays = np.load(io.BytesIO(npz_bytes), allow_pickle=False)
for name in arrays.files:
    arr = arrays[name]
    print(name, arr.shape, arr.dtype, bool(np.isfinite(arr).all()), float(arr.mean()))

Validate And Report

Save request/response JSON, generated FASTA, and a metrics JSON with sequence length, GC fraction, ambiguous-base fraction, homopolymer length, sampled-prob checks, and elapsed timing. Treat invalid schema or alphabet as hard failures; treat extreme GC, low complexity, duplicates, and missing motifs as warnings. For deeper checks, read references/validation.md.

Key fields: sequence, num_tokens, temperature, top_k (0-6), top_p (0-1), random_seed, enable_sampled_probs, enable_elapsed_ms_per_token, and optional enable_logits.

Troubleshooting

  • 401/403: hosted key missing/expired or not sent as Bearer token.
  • 422: wrong field names such as max_tokens instead of num_tokens.
  • Local endpoint confusion: print NIM_API_MODE and EVO2_NIM_URL; do not replace a configured service URL with localhost.
  • Local auth confusion: do not send Authorization to local inference.
  • Local startup: first run downloads model assets; poll $EVO2_NIM_URL/v1/health/ready before inference.
  • FP8 failure: use hosted, 7B on a supported FP8 GPU, or documented 40B GPUs.

© 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 13 other files (scripts, references) in skills/bionemo-evo2-nim of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • config/skillspector-baseline.yml
  • evals/config.yml
  • evals/evals.json
  • evals/trigger_evals.json
  • references/api.md
  • references/examples.md
  • references/parameters.md
  • references/science.md
  • references/validation.md
  • scripts/generate.py
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 67a13c0

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 7, 2026.

Compare with similar skills

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Questions about Evo2 Nim

What does Evo2 Nim do?

Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Evo2 Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice.

When should I use Evo2 Nim?

Evo2 Nim fits situations like: genomic sequence generation; hosted generation; local Docker deployment; local forward passes.

How do I install Evo2 Nim in Claude Code?

Run `npx skills add NVIDIA/skills --skill evo2-nim -a claude-code`. Or copy the skill folder (skills/bionemo-evo2-nim in NVIDIA/skills) into .claude/skills/evo2-nim in your project. Claude Code loads it when a task matches its description.

How do I install Evo2 Nim in Codex?

Run `npx skills add NVIDIA/skills --skill evo2-nim -a codex`. Or copy the skill folder (skills/bionemo-evo2-nim in NVIDIA/skills) into .agents/skills/evo2-nim in your project. Codex loads it when a task matches its description.

Can I use Evo2 Nim 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 evo2-nim -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/evo2-nim, .gemini/skills/evo2-nim, .github/skills/evo2-nim and .opencode/skills/evo2-nim in your project.

What does Evo2 Nim need to run?

Going by SKILL.md and its folder, Evo2 Nim needs Python for the scripts in its folder, the command-line tools its instructions call (docker and python) and credentials named NGC_API_KEY and NVIDIA_API_KEY. Our summary lists: Python 3; Docker; A credential in NGC_API_KEY; A credential in NVIDIA_API_KEY. Its frontmatter pre-approves these tools: Bash, Read, Write, AskUserQuestion. Compatibility (from SKILL.md): requests>=2.28; numpy>=1.24.

Does Evo2 Nim access the network?

SKILL.md names 1 domain. In commands or code: health.api.nvidia.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Evo2 Nim safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. 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 Evo2 Nim use?

Evo2 Nim 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 Evo2 Nim use?

About 2.4k tokens (SKILL.md is roughly 9.7k 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 3.7k tokens, read only when the agent opens those files.

What are the alternatives to Evo2 Nim?

Skills that share tags, products or a category with Evo2 Nim: Frontmcp Deployment (agentfront/frontmcp, 146 stars), Model Download Dev (open-edge-platform/edge-ai-libraries, 169 stars), Time Series Analytics Dev (open-edge-platform/edge-ai-libraries, 169 stars) and Spine Service (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Evo2 Nim?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 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.