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 SFETNI/Deep-Matter-Chem-Skills --skill crystal-property-gnngit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-SkillsWrote 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/sfetni/deep-matter-chem-skills/crystal-property-gnn)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/crystal-property-gnn"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/crystal-property-gnn/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/sfetni/deep-matter-chem-skills/crystal-property-gnn"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/crystal-property-gnn.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 | $0.00006 | $0.07486 |
| Opus 5 | $0.00003 | $0.03743 |
| Sonnet 5 | $0.00001 | $0.01497 |
| Haiku 4.5 | $0.00001 | $0.00749 |
Grade A, and why
crystal-property-gnn 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 12d 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 — 592 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crystal Property GNN
Description
This skill covers graph neural networks for crystal and periodic-material property prediction: crystal graph construction from periodic structures, node/edge/lattice/distance/angle features, periodic neighbor finding, cutoff selection, invariant and equivariant representations, leakage-aware splitting, uncertainty estimation, applicability-domain checks, and validation for properties such as formation energy, band gap, elastic constants, phonon-derived quantities, phase stability, conductivity proxies, and related materials targets. Invoke this skill when a model must learn directly from crystal structures rather than fixed composition or local-environment descriptors.
Domain Context
Crystal GNNs represent a periodic structure as a graph whose nodes are atomic sites and whose edges connect sites through periodic images. Unlike molecular graphs, crystal graphs are not defined by chemical bonds alone. A single atom can have several periodic neighbors of the same site, and the edge identity depends on the lattice vector used to connect the pair. The graph therefore encodes both chemistry and geometry: species, fractional and Cartesian positions, lattice, neighbor distances, angles, and periodic image offsets.
The graph construction defines the physical approximation. A cutoff graph assumes that the target property is controlled by interactions within a finite radius. This is often reasonable for local structural contributions to formation energy or many learned representations, but it is incomplete for long-range electrostatics, polar response, magnetism, excitons, or transport properties unless the model includes appropriate global features or long-range corrections. [EXPERT REVIEW NEEDED]
Crystal structures also have representational ambiguity. The same material may appear as a primitive cell, conventional cell, supercell, symmetrized structure, relaxed structure, or database-derived standardized structure. A good crystal GNN should be invariant to cell choice and site ordering, but finite cutoffs, pooling, and preprocessing can make predictions sensitive to representation. Always test primitive/conventional and supercell consistency before trusting a model on mixed-source datasets.
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.
- 12d ago First seen · 592 lines · 6 tokens per session scan A b8409439454c
crystal-property-gnn is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 6 tokens to every session and 7,486 once invoked, about $0.0000 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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