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 learningmatter-mit/AtomisticSkills --skill mat-qha-thermal-expansiongit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion/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/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-qha-thermal-expansion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.00992 |
| Opus 5 | $0.00013 | $0.00496 |
| Sonnet 5 | $0.00005 | $0.00198 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
mat-qha-thermal-expansion 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 7d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QHA Thermal Expansion Skill
This skill provides tools for calculating thermal expansion and temperature-dependent Gibbs energy using Machine Learning Interatomic Potentials (MLIPs).
1. Prerequisites
- The appropriate MLIP wrapper must be available (
MACEWrapper,MatGLWrapper, orFAIRCHEMWrapper). matcalcmust be installed in the relevant conda environment.
2. Choosing a Foundation Potential
QHA calculations require accurate lattice expansion and vibrational properties.
[!IMPORTANT]
- Use OMAT or MatPES trained models: These models (e.g.,
MACE-OMAT-0-small,TensorNet-MatPES-r2SCAN) are specifically optimized for forces and vibrational stability.- Avoid MPtrj-trained models: Models trained primarily on the
MPtrjdataset (e.g.,CHGNet-MPtrj) suffer from the "softening" problem, where the calculated phonon frequencies are significantly lower than DFT values.
Refer to the foundation-potentials skill for more details.
3. Choosing the Volume Window
QHA fits the free energy against volume -- phonopy-qha fits E(V) + F_vib(V,T) to a
Vinet, Birch-Murnaghan or Murnaghan equation of state at each temperature and minimises
it -- so the sampled volume range is a real input, and you should report it alongside
the result.
The convention is +/-5% in LINEAR strain, which is -14% to +16% in volume:
| source | window | volume width |
|---|---|---|
matcalc QHACalc default scale_factors |
0.95-1.05 linear | 1.35x |
atomate2 QhaMaker default linear_strain |
(-0.05, 0.05) | 1.35x |
phonopy Si-QHA example e-v.dat |
140.03-189.07 A^3 | 1.35x |
Note the cube: a window quoted as "+/-5%" in lattice parameter is three times that in
volume. Read which convention a tool means before comparing windows across codes --
QHACalc scales the lattice (apply_strain), not the volume.
[!IMPORTANT] The window must bracket the free-energy minimum at your highest temperature. The lattice expands on heating, so a window adequate at 0 K can be too narrow at high T, and a minimiser that runs into the edge of the scan returns the edge rather than the minimum. Check that the equilibrium volume at your top temperature is interior to the sampled volumes, and widen
--volume_windowif it is not.phonopyrequires at least 5 volume points; 11 is the usual choice.
What ships with it
5 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.
- 7d ago First seen · 88 lines · 26 tokens per session scan A cbb368ee0e54
mat-qha-thermal-expansion is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (163 stars, last pushed 7d ago), licensed MIT. It adds 26 tokens to every session and 992 once invoked, about $0.0001 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 2026-09-03.
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