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 vasp-workflowgit 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/vasp-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/vasp-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/vasp-workflow/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/vasp-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/vasp-workflow.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 | $0.00004 | $0.05953 |
| Opus 5 | $0.00002 | $0.02976 |
| Sonnet 5 | $0.00001 | $0.01191 |
| Haiku 4.5 | $0.00000 | $0.00595 |
Grade A, and why
vasp-workflow 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 — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VASP Workflow
Description
This skill covers end-to-end DFT calculations using VASP 6, from input file construction through convergence testing to production runs. It is the entry point for electronic structure work and the primary source of training data for ML interatomic potentials. Invoke this skill when an agent needs to set up, run, analyze, or debug a VASP calculation, or when generating DFT reference data for downstream tasks.
Domain Context
VASP solves the Kohn-Sham equations of density functional theory on a plane-wave basis set with PAW (Projector Augmented Wave) pseudopotentials. The key approximation is the exchange-correlation functional: it captures electron-electron interactions beyond the Hartree level, but no functional is exact for all systems.
Core assumptions that must be understood before using this workflow:
- Plane-wave basis convergence: The basis set size is controlled by ENCUT (cutoff energy in eV). Increasing ENCUT systematically improves completeness until results converge. The PAW potential files specify a recommended ENCUT; production runs should test convergence above this value.
- Brillouin zone sampling: The k-point mesh controls sampling of reciprocal space. Metals require denser meshes than insulators due to Fermi surface features. Convergence is material-dependent.
- Pseudopotential choice: VASP ships multiple PAW datasets per element (e.g.,
Fe,Fe_pv,Fe_sv). The choice affects which electrons are treated as valence, with direct consequences for accuracy and cost. - Exchange-correlation functional: PBE systematically underestimates band gaps and over-delocalizes d/f electrons. PBEsol corrects surface energies. SCAN is more accurate for many systems but harder to converge. HSE06 opens band gaps but is far more expensive. DFT+U is empirical but fast.
- Spin polarization: Magnetic systems require ISPIN=2 and careful MAGMOM initialization. Omitting spin polarization in Fe, Co, Ni, or Mn-containing systems will produce wrong energies.
- Periodic boundary conditions: VASP assumes 3D periodicity. Surfaces, defects, and molecules require appropriate vacuum spacing and dipole corrections.
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 · 386 lines · 4 tokens per session scan A 555f2af737ff
vasp-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 5,953 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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