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-elasticitynpx skills add learningmatter-mit/AtomisticSkills --skill mat-elasticitygit 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-elasticity)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-elasticity"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-elasticity.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 | $0.00033 | $0.02218 |
| Opus 5 | $0.00016 | $0.01109 |
| Sonnet 5 | $0.00007 | $0.00444 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
mat-elasticity scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elastic Tensor Skill
This skill calculates the full elastic tensor ($C_{ij}$) and derived mechanical properties of crystalline materials using Machine Learning Interatomic Potentials (MLIPs). It applies a set of normal and shear strains, computes the resulting stresses, and fits the elastic constants via least-squares regression using MatCalc's ElasticityCalc.
Goal
Calculate the elastic tensor ($C_{ij}$) of a material by applying systematic deformations (normal and shear strains), computing the stress response with an MLIP, and extracting the full Voigt elastic tensor along with:
- Bulk modulus $B$ (Voigt-Reuss-Hill average)
- Shear modulus $G$ (Voigt-Reuss-Hill average)
- Young's modulus $E$
- Poisson's ratio $\nu$
1. Prerequisites
- The appropriate MLIP wrapper must be available (
MACEWrapper,MatGLWrapper, orFAIRCHEMWrapper). matcalcmust be installed in the relevant conda environment.- A structure file (CIF, POSCAR, or other ASE-readable format). The structure will be relaxed before deformation by default.
2. Choosing a Foundation Potential
Elastic tensor calculations require accurate stress predictions across multiple deformed structures.
[!IMPORTANT]
- Use OMAT or MatPES trained models: These models (e.g.,
MACE-OMAT-0-small,CHGNet-MatPES-PBE,TensorNet-MatPES-r2SCAN) are trained with stress labels and provide reliable stress predictions.- Stress accuracy is critical: Unlike EOS (which only uses energies), elasticity calculations directly depend on stress tensors. Models trained without stress labels may give poor results.
Refer to the foundation-potentials skill for more details.
3. Calculation Workflow
To calculate the elastic tensor, use the calculate_elasticity.py script:
# Env: mace-agent
python .agents/skills/mat-elasticity/scripts/calculate_elasticity.py \
--structure path/to/structure.cif \
--model_type mace \
--model_name MACE-OMAT-0-small \
--norm_strains -0.01 -0.005 0.005 0.01 \
--shear_strains -0.06 -0.03 0.03 0.06 \
--relax_structure \
--output_dir research/my_folder/elasticity
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.
- today First seen · 151 lines · 33 tokens per session scan A fd44e5a6ff30
mat-elasticity is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 2,218 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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