Frontmcp Deployment
agentfront/frontmcp
A skill your agent uses when deploying, building for production, packaging, or shipping a FrontMCP server.
Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice.
$ npx skills add NVIDIA/skills --skill evo2-nim -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills evo2-nim --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "evo2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nim into .claude/skills/evo2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evo2-nim", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nimType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill evo2-nim -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills evo2-nim --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/bionemo-evo2-nim .agents/skills/evo2-nim && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "evo2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nim into .agents/skills/evo2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evo2-nim", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill evo2-nim -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills evo2-nim --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/bionemo-evo2-nim .cursor/skills/evo2-nim && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "evo2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nim into .cursor/skills/evo2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evo2-nim", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/bionemo-evo2-nim--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill evo2-nim -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills evo2-nim --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/bionemo-evo2-nim .gemini/skills/evo2-nim && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "evo2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nim into .gemini/skills/evo2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evo2-nim", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills evo2-nimInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill evo2-nim -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/bionemo-evo2-nim .github/skills/evo2-nim && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "evo2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nim into .github/skills/evo2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evo2-nim", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill evo2-nim -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills evo2-nim --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/bionemo-evo2-nim .opencode/skills/evo2-nim && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "evo2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-evo2-nim into .opencode/skills/evo2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "evo2-nim", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
evo2-nimGenerate 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. 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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
BashReadWriteAskUserQuestionFrom allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
dockerpythonFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
health.api.nvidia.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
NGC_API_KEYNVIDIA_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
requests>=2.28; numpy>=1.24
From compatibility in the SKILL.md frontmatter.
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.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
Use shell env first; source repo-root `.env` only if present. Do not invent a[ -f .env ] && . ./.envallowed-tools: Bash, Read, Write, AskUserQuestionAutomated 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.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 930 words, ~2,424 tokens.
.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.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.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.
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.
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?
https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate$EVO2_NIM_URL, falling back to http://localhost:8000$EVO2_NIM_URL/biology/arc/evo2/generate$EVO2_NIM_URL/biology/arc/evo2/forwardAlways 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.
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):
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-outputFor 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.
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.
NIM_TEST_GPUS=0,1 for
2x H100, or NIM_TEST_GPUS=0 for one H200.NIM_VARIANT=7b; supported GPUs include H100, H200,
RTX 6000 Ada, and L40S.Use shell env first; source repo-root .env only if present. Do not invent a
cache default or drop the NVIDIA_API_KEY fallback.
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 "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
-p 8000:8000 \
nvcr.io/nim/arc/evo2:2Readiness:
evo2_nim_url="${EVO2_NIM_URL:-http://localhost:8000}"
until curl -sf "${evo2_nim_url%/}/v1/health/ready"; do sleep 10; doneIf 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.
Forward returns base64-encoded NPZ tensors.
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()))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.
401/403: hosted key missing/expired or not sent as Bearer token.422: wrong field names such as max_tokens instead of num_tokens.NIM_API_MODE and EVO2_NIM_URL; do not
replace a configured service URL with localhost.Authorization to local inference.$EVO2_NIM_URL/v1/health/ready before inference.© 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
SKILL.md and 13 other files (scripts, references) in skills/bionemo-evo2-nim of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
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.
Evo2 Nim next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Evo2 Nim this skillNVIDIA/skills | 3.5k | 1 repos | ~2.4k | Automated safety check: Notes | Apache-2.0 | |
| Frontmcp Deploymentagentfront/frontmcp | 146 | — | ~9.2k | Automated safety check: Notes | Apache-2.0 | |
| Model Download Devopen-edge-platform/edge-ai-libraries | 169 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Time Series Analytics Devopen-edge-platform/edge-ai-libraries | 169 | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | |
| Spine Servicejeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~804 | Automated safety check: Notes | MIT | |
| Supabasemagnus919/agent-skills | 113 | — | ~2.2k | Automated safety check: Pass | MIT |
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Works with
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.
Evo2 Nim fits situations like: genomic sequence generation; hosted generation; local Docker deployment; local forward passes.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.