Molmim Nim

What it does

When your agent uses it

• NVIDIAs BioNeMo NIM microservice for small-molecule latent-space generation and optimization
• Tasks that involve Drug discovery and cheminformatics
• Tasks that involve Embeddings

Example prompts

• “/molmim-nim”

Requirements

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

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

  Shell commands in SKILL.md call:

  • docker

  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
  • NGC_CLI_API_KEY
  • NVIDIA_API_KEY

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

• Compatibility

  requests>=2.28; rdkit

  From compatibility in the SKILL.md frontmatter.

Context cost

Molmim Nim loads about 1.9k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 468 words of instructions outside code blocks.

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

~88

When it runs · the whole SKILL.md, loaded when a task matches

~1.9k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.1k

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

• NoteMentions a .env fileSKILL.md:40

  Use shell env first; source repo-root `.env` only if present. Do not print keys.

• 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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 468 words, ~1,898 tokens.

name

molmim-nim

description

Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.

allowed-tools

Bash, Read, Write, AskUserQuestion

compatibility

requests>=2.28; rdkit

license

Apache-2.0 AND CC-BY-4.0

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

import requests

seed = "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"
response = requests.post(
    "http://localhost:8000/embedding",
    headers={"Content-Type": "application/json"},
    json={"sequences": [seed]},
    timeout=60,
)
response.raise_for_status()
embedding_data = response.json()
embeddings = embedding_data["embeddings"]
print(f"received {len(embeddings)} embedding vector(s)")

import os
import requests

hosted = True
url = (
    "https://health.api.nvidia.com/v1/biology/nvidia/molmim/generate"
    if hosted else "http://localhost:8000/generate"
)
headers = {"Content-Type": "application/json"}
if hosted:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

payload = {
    "smi": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
    "algorithm": "CMA-ES",
    "num_molecules": 10,
    "property_name": "QED",
    "minimize": False,
    "min_similarity": 0.4,
    "particles": 8,
    "iterations": 3,
}

response = requests.post(url, headers=headers, json=payload, timeout=180)
response.raise_for_status()
result = response.json()

seed = "CC(Cc1ccc(cc1)C(C(=O)O)C)C"
base = "http://localhost:8000"
headers = {"Content-Type": "application/json"}

embedding = requests.post(
    f"{base}/embedding",
    headers=headers,
    json={"sequences": [seed]},
    timeout=60,
)
embedding.raise_for_status()
embedding_data = embedding.json()
embeddings = embedding_data["embeddings"]
print(f"received {len(embeddings)} embedding vector(s)")

hidden = requests.post(
    f"{base}/hidden",
    headers=headers,
    json={"sequences": [seed]},
    timeout=60,
)
hidden.raise_for_status()
hidden_data = hidden.json()
hiddens = hidden_data["hiddens"]
mask = hidden_data["mask"]

decoded = requests.post(
    f"{base}/decode",
    headers=headers,
    json={"hiddens": hiddens, "mask": mask},
    timeout=60,
)
decoded.raise_for_status()

sampled = requests.post(
    f"{base}/sampling",
    headers=headers,
    json={"sequences": [seed], "num_molecules": 10, "scaled_radius": 0.7},
    timeout=60,
)
sampled.raise_for_status()

from pathlib import Path
import json

def molmim_smiles(result):
    values = []
    if isinstance(result.get("generated"), list):
        for item in result["generated"]:
            if isinstance(item, str):
                values.append(item)
            elif isinstance(item, list):
                values.extend(x for x in item if isinstance(x, str))
    molecules = result.get("molecules")
    if isinstance(molecules, str):
        molecules = json.loads(molecules)
    if isinstance(molecules, list):
        for item in molecules:
            if isinstance(item, dict) and isinstance(item.get("sample"), str):
                values.append(item["sample"])
    return values

generated = molmim_smiles(result)
if not generated:
    raise RuntimeError(f"MolMIM returned no generated molecules: {result}")

Path("molmim_response.json").write_text(json.dumps(result, indent=2))
Path("molmim_generated.smi").write_text("\n".join(generated) + "\n")
for i, smiles in enumerate(generated, start=1):
    print(i, smiles)

© 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 (references) in skills/bionemo-molmim-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
• 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.

• NVIDIA-BioNeMo/bionemo-agent-toolkitnim-skills/molmim-nimskills/bionemo-agent-toolkit/skills/molmim-nim