boltz2-nim
Use Boltz2 NIM for biomolecular structure prediction and binding affinity.
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-boltz2-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/boltz2-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
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.
- License
- Apache-2.0 AND CC-BY-4.0
- Compatibility
- requests>=2.28
- Allowed tools
- Bash,, Read,, Write,, AskUserQuestion
- Instructions
- 152 lines in SKILL.md · view on GitHub
Before installing
- Pre-approves some shell commands
- Instructions call the network
Agent Skills spec issues
- name: "boltz2-nim" does not match folder "bionemo-boltz2-nim"
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)
# 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
### 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`.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)
- Oct 2, 2026 (26 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
boltz2-nim skill. Agent Mart. https://agentmart.dev/skills/nvidia-boltz2-nim (last updated 2026-10-24)