nvalchemi-data-structures

nvalchemi-data-structures is a skill for Claude Code from NVIDIA/nvalchemi-toolkit. It costs 106 tokens per session (3,094 once invoked), scanned A, original, Apache-2.0.

A guide to AtomicData and Batch, data structures for representing molecules and crystals as graphs and grouping them into batches for GPU processing.

In plain words
What is it for?
Use it when creating atomic systems, converting ASE Atoms objects, batching or unbatching structures, or reading values attached to atoms versus whole systems.
Why use it?
It explains how to store atomic positions, element numbers, bonds, and system-level values in the expected format.

Skill for Claude Code ✓ vendor

Written for Claude Code: installed under .claude/.

Good fit Use it when creating atomic systems, converting ASE Atoms objects, batching or unbatching structures, or reading values attached to atoms versus whole systems.

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Install with agentmods
npx agentmods add skills/nvidia/nvalchemi-toolkit/nvalchemi-data-structures
About the project

ALCHEMI Toolkit is a Python framework that uses GPUs to run atomic simulations and train machine-learned models for chemistry and materials science. Researchers and developers use it for molecular dynamics, geometry relaxation, and model training across one or multiple GPUs. Its catalogue add-ons guide coding agents in using the toolkit’s APIs and repository conventions.

NVIDIA/nvalchemi-toolkit · 192 stars · on GitHub · nvidia.github.io

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add NVIDIA/nvalchemi-toolkit --skill nvalchemi-data-structures
Clone the repo
git clone --depth 1 https://github.com/NVIDIA/nvalchemi-toolkit

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,094 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00106 $0.03094
Opus 5.5 $0.00042 $0.01238
Sonnet 5.5 $0.00021 $0.00619
Haiku 4.5 $0.00011 $0.00309

Measured 17d ago against content hash c7d7de058c95, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

nvalchemi-data-structures scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/nvalchemi-data-structures/SKILL.md · 354 lines

How it starts

The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.

nvalchemi Data Structures

Overview

nvalchemi represents atomic systems as graphs using two core classes:

  • AtomicData — a single atomic system (molecule, crystal, etc.)
  • Batch — an efficient container of multiple AtomicData objects stored as concatenated tensors

Both are Pydantic BaseModel subclasses with DataMixin for device/dtype operations.

from nvalchemi.data import AtomicData, Batch

AtomicData

Construction

Required fields: positions [n_nodes, 3] and atomic_numbers [n_nodes].

import torch

# Minimal
data = AtomicData(
    positions=torch.randn(4, 3),
    atomic_numbers=torch.tensor([1, 6, 6, 1], dtype=torch.long),
)

# With edges (bonds or neighbor list)
data = AtomicData(
    positions=torch.randn(4, 3),
    atomic_numbers=torch.tensor([1, 6, 6, 1], dtype=torch.long),
    neighbor_list=torch.tensor([[0, 1], [1, 0], [1, 2], [2, 1]], dtype=torch.long),
)

# With system-level fields (energy, cell, pbc)
data = AtomicData(
    positions=torch.randn(4, 3),
    atomic_numbers=torch.tensor([1, 6, 6, 1], dtype=torch.long),
    energy=torch.tensor([[0.5]]),
    cell=torch.eye(3).unsqueeze(0),       # [1, 3, 3]
    pbc=torch.tensor([[True, True, False]]),  # [1, 3]
)

From ASE Atoms:

data = AtomicData.from_atoms(
    atoms,                    # ase.Atoms object
    energy_key="energy",      # key in atoms.info / atoms.calc
    forces_key="forces",
    device="cpu",
    dtype=torch.float32,
)

Field reference

Fields are organized by level. All are optional except positions and atomic_numbers.

Level Field Shape Notes
Node atomic_numbers [V] Required, int64
Node positions [V, 3] Required, float
Node atomic_masses [V] Auto-populated from periodic table
Node atom_categories [V] Defaults to zeros
Node forces [V, 3] eV/Angstrom
Node velocities [V, 3] Auto-initialized to zeros
Node momenta [V, 3]
Node charges [V, 1]
Node node_embeddings [V, H]
Node kinetic_energies [V, 1]
Edge neighbor_list [E, 2] COO format, int64
Edge shifts [E, 3] Cartesian displacements (neighbor_list_shifts @ cell)
Edge neighbor_list_shifts [E, 3] Integer lattice image indices
Edge edge_embeddings [E, H]
Dense neighbor_matrix [V, K] Dense neighbor matrix (int64)
Dense neighbor_matrix_shifts [V, K, 3] Periodic shifts for dense neighbors
Dense num_neighbors [V] Valid neighbor count per atom
System cell [1, 3, 3] Lattice vectors
System pbc [1, 3] Periodic boundary conditions (bool)
System energy [1] eV
System stress [1, 3, 3] eV/Angstrom^3
System virial [1, 3, 3]
System dipole [1, 3]
System charge [1]
System graph_embeddings [1, H]

Read the full file on GitHub · 354 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 17d ago Changed · +18 lines · +24 tokens per session c7d7de058c95
  2. 1mo ago First seen · 336 lines · 82 tokens per session scan A bc950235e050

Subscribe to this mod's changes

nvalchemi-data-structures is a skill published in the GitHub repository NVIDIA/nvalchemi-toolkit (192 stars, last pushed yesterday), licensed Apache-2.0. It adds 106 tokens to every session and 3,094 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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