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-equation-of-stategit 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-equation-of-state)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-equation-of-state"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-equation-of-state/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-equation-of-state"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-equation-of-state.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 | $0.00021 | $0.01184 |
| Opus 5 | $0.00010 | $0.00592 |
| Sonnet 5 | $0.00004 | $0.00237 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
mat-equation-of-state scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Equation of State Skill
This skill provides tools for calculating the equation of state (EOS) of crystalline materials using Machine Learning Interatomic Potentials (MLIPs). The EOS describes the relationship between volume, energy, and pressure, allowing extraction of bulk modulus and equilibrium volume.
Goal
Calculate the equation of state for a material by applying volumetric strains, computing the energy-volume relationship, and fitting to the Birch-Murnaghan equation to determine the bulk modulus ($B_0$) and equilibrium volume ($V_0$).
1. Prerequisites
- The appropriate MLIP wrapper must be available (
MACEWrapper,MatGLWrapper, orFAIRCHEMWrapper). matcalcmust be installed in the relevant conda environment.- A relaxed structure file (CIF, POSCAR, or other ASE-readable format).
2. Choosing a Foundation Potential
EOS calculations require accurate total energies across different volumes.
[!IMPORTANT]
- Use OMAT or MatPES trained models: These models (e.g.,
MACE-OMAT-0-small,CHGNet-MatPES-PBE,TensorNet-MatPES-r2SCAN) provide more reliable energy predictions.- MPtrj models can be used: Unlike phonon calculations, EOS is less sensitive to force accuracy, but OMAT/MatPES models are still recommended for best results.
Refer to the foundation-potentials skill for more details.
3. Calculation Workflow
To calculate the equation of state, use the calculate_eos.py script:
# Env: mace-agent
python .agents/skills/mat-equation-of-state/scripts/calculate_eos.py \
--structure path/to/relaxed_structure.cif \
--model_type mace \
--model_name MACE-OMAT-0-small \
--n_points 11 \
--max_abs_strain 0.1 \
--relax_structure \
--output_dir research/my_folder/eos
Key Parameters:
--n_points: Number of strain points (default: 11)--max_abs_strain: Maximum linear strain applied (default: 0.1 = ±10%, i.e. volumes spanning (1±0.1)^3)--relax_structure/--no-relax_structure(default on): fully relax the input cell (ions and cell vectors) before the strain scan, so the scan is centred on this model's own equilibrium volume rather than whatever volume the input file happens to have. It does not control the per-strain relaxation -- matcalc relaxes every strained point regardless.--allow_shape_change/--no-allow_shape_change(default on, matcalc >= 0.5): at each strain point relax the cell shape at constant volume as well as the ions. This is the E(V) a Birch-Murnaghan fit assumes -- the minimum energy at fixed volume. Symmetry forbids shape relaxation in cubic cells, so it changes nothing there; for anisotropic cells, freezing the shape overestimates B0.--fmax: Force convergence tolerance for relaxation (default: 0.1 eV/Å)
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.
- 6d ago First seen · 76 lines · 21 tokens per session scan A 247d486a3879
mat-equation-of-state is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (162 stars, last pushed 6d ago), licensed MIT. It adds 21 tokens to every session and 1,184 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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