Frontmcp Deployment
agentfront/frontmcp
A skill your agent uses when deploying, building for production, packaging, or shipping a FrontMCP server.
Generate novel drug-like molecules using the GenMol NIM microservice.
$ npx skills add NVIDIA/skills --skill genmol-nim -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills genmol-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-genmol-nim .claude/skills/genmol-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 "genmol-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-genmol-nim into .claude/skills/genmol-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genmol-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-genmol-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 genmol-nim -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills genmol-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-genmol-nim .agents/skills/genmol-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 "genmol-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-genmol-nim into .agents/skills/genmol-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genmol-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 genmol-nim -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills genmol-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-genmol-nim .cursor/skills/genmol-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 "genmol-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-genmol-nim into .cursor/skills/genmol-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genmol-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-genmol-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 genmol-nim -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills genmol-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-genmol-nim .gemini/skills/genmol-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 "genmol-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-genmol-nim into .gemini/skills/genmol-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genmol-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 genmol-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 genmol-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-genmol-nim .github/skills/genmol-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 "genmol-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-genmol-nim into .github/skills/genmol-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genmol-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 genmol-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 genmol-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-genmol-nim .opencode/skills/genmol-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 "genmol-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-genmol-nim into .opencode/skills/genmol-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "genmol-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.
genmol-nimGenerate novel drug-like molecules using the GenMol NIM microservice.
Genmol Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Generate novel drug-like molecules using the GenMol NIM microservice. Use for de novo generation, scaffold decoration, motif extension, lead optimization, SAFE notation, QED or LogP ranking, hosted NVIDIA API calls, or local Docker deployment. GenMol takes SAFE notation in the smiles field, not ordinary SMILES.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including reference files (for example `BENCHMARK.md`, `config/skillspector-baseline.yml` and `evals/config.yml`). Compatibility notes: safe-mol=0.1.14; requests=2.28
It sits in DevOps & Cloud, covering Drug discovery and cheminformatics, 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.
Read from SKILL.md and the folder at commit 0e0d506. 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.
Shell commands in SKILL.md call:
dockerFrom 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.comAlso links to:
nvcr.ioFrom 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.
safe-mol>=0.1.14; requests>=2.28
From compatibility in the SKILL.md frontmatter.
Genmol Nim loads about 1.4k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 504 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.
allowed-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); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 504 words, ~1,358 tokens.
.claude/skills/genmol-nim/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.Generate drug-like molecules with GenMol. Use this guide for first-pass hosted and 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: SAFE patterns and tuning effects.references/validation.md: chemical and artifact checks.references/examples.md: compact request patterns.Ask only when context is unclear:
Hosted NVIDIA API or local Docker NIM?
https://health.api.nvidia.com/v1/biology/nvidia/genmol/generatehttp://localhost:8000/generateHosted requests use Authorization: Bearer $NGC_API_KEY. For local Docker,
authenticate image pulls with docker login nvcr.io using NGC_API_KEY
(or NVIDIA_API_KEY via the preflight). Pass -e NGC_API_KEY into the
container for entitlement checks and first-run model downloads. Local inference
requests use no auth header after readiness, so bind the published port to
loopback with -p 127.0.0.1:8000:8000. Warm-cache key-free startup varies by
image version and should not be assumed.
Use credentials already supplied in the shell environment or injected by a
secret manager. Do not load credential files, print keys, or enable shell tracing.
For local setup answers, include this sequence: env preflight, docker login
with --password-stdin, docker run, readiness loop, then a no-auth localhost
request. Do not invent a cache default or drop the NVIDIA_API_KEY fallback.
Before executing local setup, explain that registry authentication sends the key
to the NVIDIA registry at https://nvcr.io and first-run model downloads use
about 20 GB in LOCAL_NIM_CACHE.
Execute deployment only when the user requests it; for a setup guide, provide
the commands without running them.
For the exact startup preflight (environment checks, NVIDIA_API_KEY fallback,
--shm-size=2G, both --ulimit flags, docker login, and the docker run
for nvcr.io/nim/nvidia/genmol:1.0.1), copy the command block in
references/api.md under Local container startup verbatim.
GenMol is single-GPU; NIM_TEST_GPU defaults to 0. Wait for readiness:
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; doneThe API field is named smiles, but GenMol expects SAFE notation. Masked
positions use [*{min-max}].
safe_input = "[*{20-30}]"safe_input = scaffold_to_safe("C1CC(=O)NC1", 10, 15)safe_input = f"[*{{5-10}}].{motif_safe}.[*{{5-10}}]".[*{5-12}]Use safe-mol for conditioned generation. Simple ring scaffolds may raise
SAFEFragmentationError; fall back to the original SMILES plus a SAFE mask.
See the scaffold_to_safe helper in
references/examples.md under Scaffold Decoration.
Wider masks increase diversity; tight masks keep analog size more predictable.
import os
import requests
HOSTED = True
url = (
"https://health.api.nvidia.com/v1/biology/nvidia/genmol/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 = {
"smiles": "[*{20-30}]", # SAFE notation
"num_molecules": 30,
"temperature": "1", # string, not float
"noise": "1", # string, not float
"step_size": 1,
"scoring": "QED", # or "LogP"
"unique": False,
}
response = requests.post(url, headers=headers, json=payload, timeout=180)
response.raise_for_status()
result = response.json()Gotchas:
temperature and noise are strings.num_molecules is 1-1000; invalid/duplicate molecules may be filtered, so
request extra when the user needs a minimum count.scoring is "QED" for drug-likeness or "LogP" for lipophilicity.unique=True for deduplicated analog lists.Sort molecules by score, print the top ranks, and write a .smi file as shown
in references/examples.md under Save Ranked
Results. For chemical validity, uniqueness, PAINS/alerts, and visualization
with RDKit, read references/validation.md.
status: "failed" or validation errors.safe-mol only for scaffold, motif, or lead-optimization workflows;
de novo masks work without conversion.LOCAL_NIM_CACHE.nvidia-smi, NVIDIA Container Toolkit, and
--runtime=nvidia; use NIM_TEST_GPU to choose the single visible GPU.© 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 12 other files (references) in skills/bionemo-genmol-nim of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
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.
Genmol 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 |
|---|---|---|---|---|---|---|
| Genmol Nim this skillNVIDIA/skills | 3.5k | 1 repos | ~1.4k | 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 | 168 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Time Series Analytics Devopen-edge-platform/edge-ai-libraries | 168 | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | |
| Supabasemagnus919/agent-skills | 111 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Setup Workshopbrevdev/workshop-build-an-agent | 143 | — | ~2.3k | Automated safety check: Notes | Apache-2.0 |
agentfront/frontmcp
A skill your agent uses when deploying, building for production, packaging, or shipping a FrontMCP server.
open-edge-platform/edge-ai-libraries
Extend, test, debug, or integrate the Model Download microservice codebase.
open-edge-platform/edge-ai-libraries
Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/runtests.sh) and the…
magnus919/agent-skills
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brevdev/workshop-build-an-agent
This skill should be used when the user wants to set up, install, deploy, bootstrap, or "spin up" the Build-an-Agent workshop (a.k.a.
adithya-s-k/FineEnvs
Builds a NeMo Gym (NVIDIA) variant of an RL environment. An agent skill from adithya-s-k/FineEnvs.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Works with
Categories
Generate novel drug-like molecules using the GenMol NIM microservice. Genmol Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Generate novel drug-like molecules using the GenMol NIM microservice.
Genmol Nim fits situations like: de novo generation; scaffold decoration; motif extension; lead optimization.
Run `npx skills add NVIDIA/skills --skill genmol-nim -a claude-code`. Or copy the skill folder (skills/bionemo-genmol-nim in NVIDIA/skills) into .claude/skills/genmol-nim in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill genmol-nim -a codex`. Or copy the skill folder (skills/bionemo-genmol-nim in NVIDIA/skills) into .agents/skills/genmol-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 genmol-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/genmol-nim, .gemini/skills/genmol-nim, .github/skills/genmol-nim and .opencode/skills/genmol-nim in your project.
Going by SKILL.md and its folder, Genmol Nim needs the command-line tools its instructions call (docker) 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): safe-mol>=0.1.14; requests>=2.28.
SKILL.md names 2 domains. In commands or code: health.api.nvidia.com; the agent is likely to contact it when it follows the instructions. As links in the text: nvcr.io. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Genmol 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 1.4k tokens (SKILL.md is roughly 5.4k 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 3.1k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Genmol Nim: Frontmcp Deployment (agentfront/frontmcp, 146 stars), Model Download Dev (open-edge-platform/edge-ai-libraries, 168 stars), Time Series Analytics Dev (open-edge-platform/edge-ai-libraries, 168 stars) and Supabase (magnus919/agent-skills, 111 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 3,534 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.