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 dft-dataset-generationgit 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/dft-dataset-generation)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/dft-dataset-generation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-dataset-generation/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/dft-dataset-generation"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-dataset-generation.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 | $0.00005 | $0.14240 |
| Opus 5 | $0.00003 | $0.07120 |
| Sonnet 5 | $0.00001 | $0.02848 |
| Haiku 4.5 | $0.00001 | $0.01424 |
Grade A, and why
dft-dataset-generation 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 — 1,070 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DFT Dataset Generation
Description
This skill covers the systematic generation of DFT-labeled datasets for training machine-learned interatomic potentials: configuration-space coverage strategy, single-point labeling with VASP and Quantum ESPRESSO, label consistency enforcement (energies, forces, stresses, units, PBC), isolated atom reference energy protocols, dataset assembly in extxyz format, provenance tracking, outlier detection, and failed-calculation triage. Invoke this skill when designing the initial training corpus for an MLP, when extending an existing dataset to new thermodynamic conditions or structure families, or when auditing a dataset inherited from a prior project.
Domain Context
A machine-learned interatomic potential is only accurate within the region of configuration space spanned by its training data. The central challenge of dataset generation is maximizing that coverage per DFT calculation. DFT calculations of bulk crystals are cheap relative to surface or defect calculations; a naive dataset dominated by near-equilibrium bulk structures will produce a model that is excellent for lattice dynamics but fails immediately for grain boundaries, liquid phases, or adsorption. Conversely, randomly generated structures — even if chemically valid — sample high-energy regions of configuration space that the target MD trajectory will never visit, wasting the DFT budget without improving model quality.
Coverage vs. cost tradeoff. Each DFT single-point evaluation costs O(N³) in SCF iterations (where N is the number of electrons) and is entirely determined by the atomic positions, cell, and DFT settings. The dataset designer's task is to identify which structures are both (a) physically accessible in the target application and (b) maximally informative given the structures already in the dataset. Active learning is the principled solution to (b); systematic structure generation (rattling, straining, AIMD sampling, defect enumeration) is the practical solution to (a) at the start of a campaign before an MLP exists.
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 · 1,070 lines · 5 tokens per session scan A dd6709f36d1d
dft-dataset-generation is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 14,240 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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