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-lattice-thermal-conductivitygit 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-lattice-thermal-conductivity)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-lattice-thermal-conductivity"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-lattice-thermal-conductivity/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-lattice-thermal-conductivity"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-lattice-thermal-conductivity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 12 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00019 | $0.00897 |
| Opus 5 | $0.00010 | $0.00449 |
| Sonnet 5 | $0.00004 | $0.00179 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
mat-lattice-thermal-conductivity 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 5d 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.
Lattice Thermal Conductivity Calculation Skill
This skill provides tools for calculating lattice thermal conductivity of materials using anharmonic lattice dynamics with Machine Learning Interatomic Potentials (MLIPs).
[!WARNING] Lattice thermal conductivity only considers phonon-phonon interactions. It can be considered that lattice thermal conductivity accurately models the thermal conductivity of non-metallic materials. For metallic materials, electron-phonon interactions also need to be considered to accurately calculate thermal conductivity, which is beyond the scope of this skill.
1. Prerequisites
- The appropriate MLIP wrapper must be available (
MACEWrapper,MatGLWrapper, orFAIRCHEMWrapper). matcalc,phonopy, andphono3pymust be installed in the relevant conda environment.
Required Patch for phono3py ≥ 3.x
phono3py 3.x renamed ConductivityRTA.kappa_TOT_RTA to .kappa. Apply the following one-line fix in matcalc/src/matcalc/_phonon3.py:
-kappa = np.asarray(phonon3.thermal_conductivity.kappa_TOT_RTA)
+kappa = np.asarray(phonon3.thermal_conductivity.kappa)
2. Choosing a Foundation Potential
Phonon and thermal conductivity calculations are highly sensitive to the quality of the potential energy surface (PES).
[!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. Calculation Workflow
Step One: Verify given material is an insulator / semiconductor
First of all, using the mat-electronic-structure skill to calculate the band gap of the given material or retrieve the band gap from Materials Project. If the band gap does not exist, the material is a metal, and this skill cannot give a meaningful prediction on thermal conductivity. Otherwise, the material is an insulator, and we can proceed to next step.
What ships with it
4 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.
- 5d ago First seen · 76 lines · 19 tokens per session scan A b73fd30a3e20
mat-lattice-thermal-conductivity is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (162 stars, last pushed 6d ago), licensed MIT. It adds 19 tokens to every session and 897 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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