molmim-nim
Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization.
Skill by NVIDIA
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/nvidia-molmim-nim
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About
Use cases: backend, devops
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/molmim-nim
Tools it pre-approves 4
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
Bash,Read,Write,AskUserQuestion
What's inside
- Skill 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.
- License
- Apache-2.0 AND CC-BY-4.0
- Compatibility
- requests>=2.28; rdkit
- Allowed tools
- Bash,, Read,, Write,, AskUserQuestion
- Instructions
- 219 lines in SKILL.md · view on GitHub
Before installing
- Pre-approves some shell commands
- Instructions call the network
Agent Skills spec issues
- name: "molmim-nim" does not match folder "bionemo-molmim-nim"
- body: contains an absolute path from the author's machine
Files (13)
- BENCHMARK.md
- SKILL.md
- skillspector-baseline.yml · config/
- config.yml · evals/
- evals.json · evals/
- trigger_evals.json · evals/
- api.md · references/
- examples.md · references/
- parameters.md · references/
- science.md · references/
- validation.md · references/
- skill-card.md
- skill.oms.sig
SKILL.md (first lines)
# MolMIM NIM
Generate, sample, embed, and decode small molecules with MolMIM. Use this
guide for first-pass hosted/local usage; load supplemental files only when
needed:
- `references/api.md`: endpoints, schema, Docker flags, response fields.
- `references/science.md`: use cases, strengths, limits, and handoffs.
- `references/parameters.md`: generation, sampling, and optimization effects.
- `references/validation.md`: SMILES/property/artifact checks.
- `references/examples.md`: compact hosted/local request patterns.
## Choose Mode
Ask only when context is unclear:
> Hosted NVIDIA API or local Docker NIM?
See [`references/api.md`](references/api.md) under **Endpoints** for the full
hosted/local endpoint list.
Mode difference: the hosted API reference exposes `/generate`; the local
container exposes the broader latent-space workflow (`/embedding`, `/hidden`,
`/decode`, `/sampling`, `/generate`). Do not invent hosted latent endpoints.
Hosted requests use `Authorization: Bearer $NGC_API_KEY`. Local inference uses
no auth header after readiness.
## Local Docker
Use shell env first; source repo-root `.env` only if present. Do not print keys.
MolMIM docs use `NGC_CLI_API_KEY` for the local container; this repo accepts
`NGC_API_KEY` or `NVIDIA_API_KEY` and maps to `NGC_CLI_API_KEY` for startup.
Mount `LOCAL_NIM_CACHE` at `/home/nvs/.cache/nim`.
For the exact startup preflight (the `NGC_API_KEY`/`NVIDIA_API_KEY` →
`NGC_CLI_API_KEY` mapping, `docker login`, and the `docker run` for
`nvcr.io/nim/nvidia/molmim:1.0.0`), copy the command block in
[`references/api.md`](references/api.md) under **Local Docker** verbatim.
Readiness check:
```bash
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done
```
Local embedding smoke test after readiness. Local inference uses no
`Authorization` header:
```python
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)")
```
## Hosted Generation Pattern
Use hosted `/generate` for seed-SMILES generation or optimization. Use
`algorithm: "CMA-ES"` for guided property optimization and `algorithm: "none"`
for unguided sampling around the seed.
```python
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()
```
Generation gotchas:
- Field name is `smi`, not `smiles`.
- `algorithm` is `"CMA-ES"` or `"none"`.
- `property_name` is `"QED"` or `"plogP"`.
- `num_molecules` is 1-100. `iterations` is 1-1000. `particles` is 2-1000.
- `min_similarity` is 0-1 in the hosted API reference; local docs emphasize
common values up to 0.7 for constrained optimization.
- `scaled_radius` is 0-2 and is mainly used with `algorithm: "none"` or local
`/sampling`.
## Local Latent Workflow
Use local-only endpoints for embedding, hidden-state manipulation, and decode.
This is also the surface used by the guided optimization example package.
For local latent workflows, state explicitly that the hosted API reference
exposes `/generate`; `/embedding`, `/hidden`, `/decode`, and `/sampling` are
local-only in the current docs.From NVIDIA/skills, Apache-2.0 AND CC-BY-4.0.
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Signals
- Stars
- 3,718
- Forks
- 457
- Open issues
- 8
- Last commit (this folder)
- Sep 29, 2026 (29 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0 AND CC-BY-4.0
- Repository
- github.com/NVIDIA/skills
- Website
- docs.nvidia.com/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
molmim-nim skill. Agent Mart. https://agentmart.dev/skills/nvidia-molmim-nim (last updated 2026-10-24)