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-stabilitynpx skills add learningmatter-mit/AtomisticSkills --skill mat-stabilitygit 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-stability)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-stability"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-stability.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 | $0.00028 | $0.02035 |
| Opus 5 | $0.00014 | $0.01018 |
| Sonnet 5 | $0.00006 | $0.00407 |
| Haiku 4.5 | $0.00003 | $0.00203 |
Grade A, and why
mat-stability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stability Calculation
Goal
To determine the thermodynamic stability of a material at 0K by computing the energy above the convex hull ($E_{hull}$) using pymatgen phase diagram analysis with structures from Materials Project.
[!TIP] Finite Temperature Stability: While this skill focuses on 0K stability (potential energy), you can construct a finite-temperature phase diagram by replacing potential energies with Free Energies ($G = U + F_{\text{vib}}$) calculated from the mat-qha-thermal-expansion skill.
Electrochemical Stability: The phase diagram constructed here can be seamlessly reused to calculate the material's electrochemical window (ECW) against a specific mobile ion (e.g., Li/Li+). See the mat-electrochemical-window skill for detailed methods.
Instructions
-
Select Level of Theory: Choose the target accuracy level for stability calculations.
- Recommended: r2SCAN-level foundation potentials for high accuracy
- Options:
TensorNet-MatPES-r2SCAN-v2025.1-PES(MatGL) orMACE-MH-1withmatpes_r2scanhead - See ml-foundation-potentials for detailed guidance
Note: r2SCAN shows high accuracy for predicting thermodynamic stability (MAE 80 meV/atom for formation energies vs PBE's 175 meV/atom)1. Using r2SCAN-trained potentials ensures consistency with Materials Project's r2SCAN entries.
-
Query Materials Project Hull: Retrieve all structures on the convex hull in the target material's chemical space.
# Env: base-agent python .agents/skills/mat-stability/scripts/query_mp_hull.py \ --formula "Li-Fe-P-O" \ --target "LiFePO4" \ --thermo_type "R2SCAN" \ --output hull_structures/
Footnotes
-
Kingsbury, R. et al. "Performance comparison of r2SCAN and SCAN metaGGA density functionals for solid materials via an automated, high-throughput computational workflow" Physical Review Materials 6, 013801 (2022). DOI: 10.1103/PhysRevMaterials.6.013801 ↩
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.
- today First seen · 146 lines · 28 tokens per session scan A 740ae486bd9d
mat-stability is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 2,035 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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