nvalchemi-data-structures

nvalchemi-data-structures is a skill for Claude Code, Codex from NVIDIA/nvalchemi-toolkit. It costs 82 tokens per session (2,837 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 CodeCodex

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.

agentmods
npx agentmods add skills/nvidia/nvalchemi-toolkit/nvalchemi-data-structures
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, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for nvalchemi-data-structures

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia/nvalchemi-toolkit/nvalchemi-data-structures.svg)](https://agentmods.dev/skills/nvidia/nvalchemi-toolkit/nvalchemi-data-structures)
Your own site
<a href="https://agentmods.dev/skills/nvidia/nvalchemi-toolkit/nvalchemi-data-structures"><img src="https://agentmods.dev/badge/skills/nvidia/nvalchemi-toolkit/nvalchemi-data-structures.svg" alt="Measured on agentmods" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,837 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00082 $0.02837
Opus 5 $0.00041 $0.01418
Sonnet 5 $0.00016 $0.00567
Haiku 4.5 $0.00008 $0.00284

Measured 4d ago against content hash bc950235e050, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 4d 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 · 336 lines

How it starts

The opening of the file, as written. The whole thing — 336 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 · 336 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. 4d 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 (150 stars, last pushed 9d ago), licensed Apache-2.0. It adds 82 tokens to every session and 2,837 once invoked, about $0.0004 per session on Opus 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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