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.
npx skills add dtunai/agent-skills-for-compute --skill alchemi-toolkit-opsgit clone --depth 1 https://github.com/dtunai/agent-skills-for-computeWrote 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.
[](https://agentmods.dev/skills/dtunai/agent-skills-for-compute/alchemi-toolkit-ops)<a href="https://agentmods.dev/skills/dtunai/agent-skills-for-compute/alchemi-toolkit-ops"><img src="https://agentmods.dev/badge/skills/dtunai/agent-skills-for-compute/alchemi-toolkit-ops/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dtunai/agent-skills-for-compute/alchemi-toolkit-ops"><img src="https://agentmods.dev/badge/skills/dtunai/agent-skills-for-compute/alchemi-toolkit-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.01898 |
| Opus 5 | $0.00039 | $0.00949 |
| Sonnet 5 | $0.00016 | $0.00380 |
| Haiku 4.5 | $0.00008 | $0.00190 |
Grade A, and why
alchemi-toolkit-ops 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ALCHEMI Toolkit-Ops
Overview
GPU-accelerated library of low-level, high-performance kernels for atomistic simulations, computational chemistry, and graph neural networks. Built on NVIDIA Warp, outputs native PyTorch tensors with full torch.compile compatibility. Covers neighbor list construction, long-range electrostatics (Ewald/PME/multipole), and DFT-D3 dispersion corrections — all with native batch processing for heterogeneous molecular systems.
Quick Pattern
Incorrect — CPU-based neighbor search without batching:
from scipy.spatial import KDTree
tree = KDTree(positions)
pairs = tree.query_pairs(cutoff)
Correct — GPU-accelerated neighbor list with auto-dispatch:
import torch
from nvalchemiops.neighborlist import neighbor_list
positions = torch.randn(10000, 3, device="cuda")
cell = torch.eye(3, device="cuda").unsqueeze(0) * 30.0
pbc = torch.tensor([[True, True, True]], device="cuda")
result = neighbor_list(
positions, cutoff=5.0, cell=cell, pbc=pbc,
)
# result.neighbor_matrix: [N, max_neighbors], int32
# result.num_neighbors: [N], int32
# result.neighbor_matrix_shifts: [N, max_neighbors, 3], int32
Quick Command
# Install from PyPI
pip install nvalchemi-toolkit-ops
# Install from source
pip install git+https://github.com/NVIDIA/nvalchemi-toolkit-ops.git
# Verify installation
python -c "import nvalchemiops; print(nvalchemiops.__version__)"
# Docker
docker run --gpus all nvidia/cuda:13.0.0-runtime-ubuntu24.04
# Then: pip install nvalchemi-toolkit-ops
Quick Reference
Core Modules
| Module | Import | Purpose |
|---|---|---|
| Neighbor List | nvalchemiops.neighborlist |
GPU-accelerated neighbor search |
| Ewald Summation | nvalchemiops.interactions.electrostatics |
Long-range electrostatics O(N^2) |
| PME | nvalchemiops.interactions.electrostatics |
Particle Mesh Ewald O(N log N) |
| Coulomb | nvalchemiops.interactions.electrostatics.coulomb |
Direct Coulomb interactions |
| Multipole | nvalchemiops.interactions.electrostatics |
Dipole/quadrupole Ewald/PME |
| DFT-D3 | nvalchemiops.interactions.dispersion.dftd3 |
Dispersion corrections (BJ damping) |
| Spherical Harmonics | nvalchemiops.math.spherical_harmonics |
Real spherical harmonics |
| GTO | nvalchemiops.math.gto |
Gaussian Type Orbital functions |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 169 lines · 78 tokens per session scan A 3bc8771d2f48
alchemi-toolkit-ops is a skill published in the GitHub repository dtunai/agent-skills-for-compute (2 stars, last pushed 6mo ago), licensed MIT. It adds 78 tokens to every session and 1,898 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 2026-08-31.
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