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-convergencegit 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-convergence)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/dft-convergence"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-convergence/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-convergence"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/dft-convergence.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 | $0.07588 |
| Opus 5 | $0.00002 | $0.03794 |
| Sonnet 5 | $0.00001 | $0.01518 |
| Haiku 4.5 | $0.00000 | $0.00759 |
Grade A, and why
dft-convergence scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
# subprocess.run(["pw.x", "-in", "scf.in"], cwd=calc_dir, ...) How it starts
The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DFT Convergence
Description
This skill covers systematic convergence testing for plane-wave DFT calculations: cutoff energy (ENCUT / ecutwfc), k-point mesh density, smearing scheme and width, and SCF thresholds. Convergence requirements change significantly depending on the property of interest, the code, and the downstream use of the data. Invoke this skill before any production DFT run, whenever introducing a new element or pseudopotential family, and whenever changing codes or functional.
Domain Context
Plane-wave DFT is variational: the energy decreases monotonically toward the exact result as the basis set (ENCUT) and Brillouin zone sampling (k-points) are increased. This means convergence can always be tested systematically. However, two important subtleties apply:
- Energy differences converge faster than absolute energies. Systematic errors in ENCUT and k-points largely cancel when comparing two structures with the same composition and a similar geometry. Reaction energies and adsorption energies can be converged to 1 meV/atom with a lower ENCUT than is needed for the absolute total energy at the same tolerance.
- Different properties have different convergence rates. Forces, stresses, phonon frequencies, band gaps, magnetic moments, and dielectric constants each have their own sensitivity to basis set completeness and k-mesh density. A calculation converged for total energy is not automatically converged for phonon frequencies or surface energies.
The pseudopotential or PAW dataset defines a recommended cutoff (ENMAX in VASP POTCAR; wfc_cutoff or ecutwfc in Quantum ESPRESSO input), but these recommendations are conservative lower bounds, not validated production defaults. The density cutoff (ecutrho in QE, controlled implicitly by VASP) must also be checked for ultrasoft pseudopotentials.
Smearing introduces a fictitious electronic temperature to smooth the Fermi surface and improve SCF convergence in metals. The smearing width (SIGMA in VASP; degauss in QE) must be small enough that the physical result is not distorted, but large enough to achieve stable convergence. The correct smearing scheme depends on whether the system is metallic, semiconducting, or insulating.
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 · 462 lines · 4 tokens per session scan A 93f0bb2ba511
dft-convergence 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 7,588 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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