Generate Nemo Gym Env
adithya-s-k/FineEnvs
Builds a NeMo Gym (NVIDIA) variant of an RL environment. An agent skill from adithya-s-k/FineEnvs.
Use Boltz2 NIM for biomolecular structure prediction and binding affinity.
$ npx skills add NVIDIA/skills --skill boltz2-nim -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills boltz2-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-boltz2-nim .claude/skills/boltz2-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 "boltz2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-boltz2-nim into .claude/skills/boltz2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-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-boltz2-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 boltz2-nim -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills boltz2-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-boltz2-nim .agents/skills/boltz2-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 "boltz2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-boltz2-nim into .agents/skills/boltz2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-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 boltz2-nim -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills boltz2-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-boltz2-nim .cursor/skills/boltz2-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 "boltz2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-boltz2-nim into .cursor/skills/boltz2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-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-boltz2-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 boltz2-nim -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills boltz2-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-boltz2-nim .gemini/skills/boltz2-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 "boltz2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-boltz2-nim into .gemini/skills/boltz2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-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 boltz2-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 boltz2-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-boltz2-nim .github/skills/boltz2-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 "boltz2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-boltz2-nim into .github/skills/boltz2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-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 boltz2-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 boltz2-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-boltz2-nim .opencode/skills/boltz2-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 "boltz2-nim" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-boltz2-nim into .opencode/skills/boltz2-nim/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "boltz2-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.
boltz2-nimUse Boltz2 NIM for biomolecular structure prediction and binding affinity.
Boltz2 Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.
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: requests=2.28
It sits in DevOps & Cloud, covering Protein structure and design, Drug discovery and cheminformatics and Containers. 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 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.
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.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
From compatibility in the SKILL.md frontmatter.
Boltz2 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 69 tokens; SKILL.md has 383 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.
default or drop the `.env` load or `NVIDIA_API_KEY` fallback.[ -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); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 383 words, ~1,379 tokens.
.claude/skills/boltz2-nim/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.Predict biomolecular structures and optional ligand affinity. Use this guide for first-pass hosted/local usage; load supplemental files only when needed:
references/api.md: exact endpoints, schemas, Docker flags, response fields.references/science.md: purpose, strengths, limitations, and handoffs.references/parameters.md: prediction, sampling, MSA, template, affinity tuning.references/validation.md: mmCIF, confidence, affinity, and chemistry checks.references/examples.md: compact hosted/local payload patterns.Read credentials from the environment only when needed. Check presence with
bool(os.getenv("NGC_API_KEY")); keep key values and Authorization headers out of
terminal output, logs, saved artifacts, and the final response. Avoid environment
dumps when diagnosing authentication. If the hosted key is absent, report the
missing variable before submitting a request.
Ask only when context is unclear:
Hosted NVIDIA API or local Docker NIM?
https://health.api.nvidia.com/v1/biology/mit/boltz2/predicthttp://localhost:8000/biology/mit/boltz2/predictHosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker
startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for
registry login, entitlement checks, and first-run model downloads; pass it
into the container with -e NGC_API_KEY. Local inference requests use no
auth header after readiness. Warm-cache key-free startup varies by
image/version and should not be assumed.
For local setup answers, copy the preflight below before docker login,
docker run, readiness, and the no-auth local request. Do not invent a cache
default or drop the .env load or NVIDIA_API_KEY fallback.
set -a
[ -f .env ] && . ./.env
set +a
if [ -z "${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
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 755 "${LOCAL_NIM_CACHE}"
docker run --rm --name boltz2 --gpus all \
--shm-size=16G \
-e NGC_API_KEY \
-v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
-p 8000:8000 \
nvcr.io/nim/mit/boltz2:1.6.0Readiness:
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; doneFirst startup downloads about 30 GB of model weights.
import os
import requests
HOSTED = True
url = (
"https://health.api.nvidia.com/v1/biology/mit/boltz2/predict"
if HOSTED else "http://localhost:8000/biology/mit/boltz2/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
api_key = os.getenv("NGC_API_KEY")
if not api_key:
raise SystemExit("NGC_API_KEY is required for the hosted API")
headers["Authorization"] = f"Bearer {api_key}"
payload = {
"polymers": [{
"id": "A",
"molecule_type": "protein",
"sequence": "MTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPT",
}],
"recycling_steps": 3,
"sampling_steps": 50,
"diffusion_samples": 1,
"step_scale": 1.638,
"output_format": "mmcif",
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()Payload essentials:
{"molecule_type": "protein", "sequence": "..."}.molecule_type "dna" or "rna".{"id": "L1", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"}.{"id": "L1", "ccd": "ATP"}."predict_affinity": True on exactly one ligand; report
affinity_pic50, affinity_pred_value, and affinity_probability_binary.alignment, format, and rank; do not use a stale data field.protein_with_msa = {
"id": "A",
"molecule_type": "protein",
"sequence": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
"msa": {"msa_search": {"a3m": {
"alignment": ">query\nMTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
"format": "a3m",
"rank": 0,
}}},
}Save every .cif artifact and read the confidence/affinity fields using the
snippet in references/examples.md under Save
Structures And Affinity. Visualize in PyMOL, ChimeraX, or UCSF Chimera. For
confidence/affinity sanity checks, read references/validation.md.
422: invalid sequence, invalid CCD/SMILES, malformed MSA, or multiple
affinity ligands./v1/health/ready.--gpus all, --shm-size=16G, and the /opt/nim/.cache mount.© 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-boltz2-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.
Boltz2 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 |
|---|---|---|---|---|---|---|
| Boltz2 Nim this skillNVIDIA/skills | 3.5k | 1 repos | ~1.4k | Automated safety check: Notes | Apache-2.0 | |
| Generate Nemo Gym Envadithya-s-k/FineEnvs | 443 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Setup Workshopbrevdev/workshop-build-an-agent | 144 | — | ~2.3k | Automated safety check: Notes | Apache-2.0 | |
| Docker Ros2 Developmentarpitg1304/robotics-agent-skills | 368 | — | ~9.1k | Automated safety check: Notes | Apache-2.0 | |
| Init GPU Serverdrawthingsai/draw-things-community | 580 | — | ~2.2k | Automated safety check: Pass | GPL-3.0 | |
| Upgrade Depsareal-project/AReaL | 5.8k | — | ~6k | Automated safety check: Pass | Apache-2.0 |
adithya-s-k/FineEnvs
Builds a NeMo Gym (NVIDIA) variant of an RL environment. An agent skill from adithya-s-k/FineEnvs.
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.
arpitg1304/robotics-agent-skills
Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for…
drawthingsai/draw-things-community
Initialize a Draw Things GPU server with GPUScript, including script sync, Docker/CUDA/NVIDIA runtime setup, 7T data disk mounting, mergerfs, and end-to-end GPU verification.
areal-project/AReaL
Upgrade focused runtime dependencies in AReaL. An agent skill from areal-project/AReaL.
NVIDIA-BioNeMo/bionemo-agent-toolkit
First-time setup, environment configuration, and model-weight installation for Proteina-Complexa.
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
Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Boltz2 Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use Boltz2 NIM for biomolecular structure prediction and binding affinity.
Boltz2 Nim fits situations like: tasks that involve Protein structure and design; tasks that involve Drug discovery and cheminformatics; tasks that involve Containers.
Run `npx skills add NVIDIA/skills --skill boltz2-nim -a claude-code`. Or copy the skill folder (skills/bionemo-boltz2-nim in NVIDIA/skills) into .claude/skills/boltz2-nim in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill boltz2-nim -a codex`. Or copy the skill folder (skills/bionemo-boltz2-nim in NVIDIA/skills) into .agents/skills/boltz2-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 boltz2-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/boltz2-nim, .gemini/skills/boltz2-nim, .github/skills/boltz2-nim and .opencode/skills/boltz2-nim in your project.
Going by SKILL.md and its folder, Boltz2 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): requests>=2.28.
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. Review the folder before installing.
Boltz2 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 5.5k 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 3.2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Boltz2 Nim: Generate Nemo Gym Env (adithya-s-k/FineEnvs, 443 stars), Setup Workshop (brevdev/workshop-build-an-agent, 144 stars), Docker Ros2 Development (arpitg1304/robotics-agent-skills, 368 stars) and Init GPU Server (drawthingsai/draw-things-community, 580 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.