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 agentmods add skills/learningmatter-mit/atomisticskills/mat-solid-free-energynpx skills add learningmatter-mit/AtomisticSkills --skill mat-solid-free-energygit 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-solid-free-energy)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-solid-free-energy"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-solid-free-energy.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.01633 |
| Opus 5 | $0.00020 | $0.00816 |
| Sonnet 5 | $0.00008 | $0.00327 |
| Haiku 4.5 | $0.00004 | $0.00163 |
Grade A, and why
mat-solid-free-energy 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 today.
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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solid Free Energy
This skill calculates the absolute free energy of a crystalline solid using Frenkel-Ladd switching, which is a specific form of Thermodynamic Integration (TI).
Thermodynamic integration computes the free energy difference between two states by integrating the derivative of the Hamiltonian along a continuous coupling path. In this skill, the path interpolates between a physical MLIP Hamiltonian (the target state) and a harmonic Einstein-crystal reference (where the exact absolute free energy is analytically known).
Goal
Compute the Helmholtz free energy $F$ of a pre-equilibrated periodic solid at a target temperature, and optionally the Gibbs free energy $G = F + PV$ if pressure is supplied.
Prerequisites
- A pre-equilibrated periodic solid structure in an ASE-readable format such as CIF or POSCAR.
- The transferable MLIP wrapper stack must be available in the target repo via
src.utils.mlips.loader.load_wrapper(...). - ASE and pymatgen must be installed in the relevant conda environment.
[!IMPORTANT] This skill only performs the Frenkel-Ladd free-energy workflow. It does not relax the structure, build a supercell, equilibrate the volume, perform alchemical switching, or apply center-of-mass corrections.
Choosing a Foundation Potential
Frenkel-Ladd switching is an MD-based free-energy method, so both energy and force stability matter.
[!IMPORTANT]
- Prefer materials models intended for PES or MD use, such as
MACE-OMAT-0-small,MACE-MH-1,CHGNet-MatPES-*, orTensorNet-MatPES-*.- Use smaller or faster models for long switching trajectories when practical.
- Avoid changing model family between preparation and Frenkel-Ladd unless you intentionally want a different free-energy reference.
Refer to the foundation-potentials skill for model selection guidance.
Preparing Inputs
This skill assumes the input structure is already appropriate for the target thermodynamic state point. For production workflows, the most useful upstream skills are:
What ships with it
3 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.
- today First seen · 142 lines · 40 tokens per session scan A a50214d0e837
mat-solid-free-energy is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,633 once invoked, about $0.0002 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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