Official agent skill

Boltz2 Nim

by NVIDIA in NVIDIA/skills

Use Boltz2 NIM for biomolecular structure prediction and binding affinity.

OfficialApache-2.0Auto-check: notesDevOps & Cloud

Install Boltz2 Nim

skills CLI
$ npx skills add NVIDIA/skills --skill boltz2-nim -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills boltz2-nim --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
boltz2-nim
GitHub stars
3.5k
Used in
1 other repo
Token cost
~1.4k tokens
SKILL.md length
383 words
Files
13 (incl. references)
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Use Boltz2 NIM for biomolecular structure prediction and binding affinity.

  • Tasks that involve Protein structure and design
  • SKILL.md covers Instructions, Local Docker, Examples and Save And Report Output, plus 1 more section
  • Calls docker; reaches health.api.nvidia.com; needs NGC_API_KEY and NVIDIA_API_KEY
  • Tasks that involve Drug discovery and cheminformatics

What it does

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.

When your agent uses it

  • Tasks that involve Protein structure and design
  • Tasks that involve Drug discovery and cheminformatics
  • Tasks that involve Containers

Example prompts

  • “/boltz2-nim”

Requirements

  • Python 3
  • Docker
  • A credential in NGC_API_KEY
  • A credential in NVIDIA_API_KEY
  • Compatibility (from SKILL.md): requests>=2.28
  • 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
    • NVIDIA_API_KEY

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

  • Compatibility

    requests>=2.28

    From compatibility in the SKILL.md frontmatter.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.5k

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

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:50
    default or drop the `.env` load or `NVIDIA_API_KEY` fallback.
  • NoteMentions a .env fileSKILL.md:54
    [ -f .env ] && . ./.env
  • 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). 383 words, ~1,379 tokens.

Download SKILL.mdSave it as .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.
name
boltz2-nim
description
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.
allowed-tools
Bash, Read, Write, AskUserQuestion
compatibility
requests>=2.28
license
Apache-2.0 AND CC-BY-4.0

Boltz2 NIM

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.

Instructions

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.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

  • Hosted: https://health.api.nvidia.com/v1/biology/mit/boltz2/predict
  • Local: http://localhost:8000/biology/mit/boltz2/predict

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.

Local Docker

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.

bash
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/boltz2:1.6.0

Readiness:

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

First startup downloads about 30 GB of model weights.

Examples

Show full SKILL.md (159 more words)Show less
Prediction Request
python
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:

  • Protein polymer: {"molecule_type": "protein", "sequence": "..."}.
  • DNA/RNA polymer: add another polymer with molecule_type "dna" or "rna".
  • Ligand by SMILES: {"id": "L1", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"}.
  • Ligand by CCD: {"id": "L1", "ccd": "ATP"}.
  • Affinity: set "predict_affinity": True on exactly one ligand; report affinity_pic50, affinity_pred_value, and affinity_probability_binary.
  • Precomputed A3M MSA goes under the protein polymer. The A3M record uses alignment, format, and rank; do not use a stale data field.
python
protein_with_msa = {
    "id": "A",
    "molecule_type": "protein",
    "sequence": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
    "msa": {"msa_search": {"a3m": {
        "alignment": ">query\nMTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
        "format": "a3m",
        "rank": 0,
    }}},
}

Save And Report Output

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.

Limits And Troubleshooting

  • Polymers/request: 12. Ligands/request: 20. Chain length: 4096 residues.
  • Affinity prediction supports one ligand per request and adds runtime.
  • 422: invalid sequence, invalid CCD/SMILES, malformed MSA, or multiple affinity ligands.
  • Local URL/auth: local path has no hosted auth header; wait on /v1/health/ready.
  • Local startup: use --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

Files

SKILL.md and 12 other files (references) in skills/bionemo-boltz2-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.

Compare with similar skills

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Questions about Boltz2 Nim

What does Boltz2 Nim do?

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.

When should I use Boltz2 Nim?

Boltz2 Nim fits situations like: tasks that involve Protein structure and design; tasks that involve Drug discovery and cheminformatics; tasks that involve Containers.

How do I install Boltz2 Nim in Claude Code?

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.

How do I install Boltz2 Nim in Codex?

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.

Can I use Boltz2 Nim in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Boltz2 Nim need to run?

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.

Does Boltz2 Nim access the network?

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.

Is Boltz2 Nim safe to install?

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.

What licence does Boltz2 Nim use?

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.

How many tokens does Boltz2 Nim use?

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.

What are the alternatives to Boltz2 Nim?

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.

Who maintains Boltz2 Nim?

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.