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-phase-field-non-conservativegit 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-phase-field-non-conservative)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-phase-field-non-conservative"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-phase-field-non-conservative.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 5.1 | $0.00029 | $0.00824 |
| Opus 5 | $0.00015 | $0.00412 |
| Sonnet 5 | $0.00006 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
mat-phase-field-non-conservative 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 3d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Non-Conservative Phase-Field: Allen-Cahn
Goal
To simulate the morphological evolution of structural transformations (like solidification, melting, or curvature-driven grain growth) using the Allen-Cahn (time-dependent Ginzburg-Landau) equation. This tracks a non-conservative order parameter $\phi$ which distinguishes between phases (e.g., solid vs. liquid).
Instructions
1. Mathematical Formulation
The Allen-Cahn equation describes the evolution of a non-conserved order parameter $\phi$ down a free energy gradient: $$ \frac{\partial \phi}{\partial t} = -M \frac{\delta F}{\delta \phi} = M \left( \epsilon^2 \nabla^2 \phi - \frac{\partial f(\phi)}{\partial \phi} \right) $$ Where $M$ is the mobility, $\epsilon$ is the gradient energy coefficient controlling the interface thickness, and $f(\phi) = W \phi^2(1-\phi)^2$ is the double-well potential barrier between the two phases ($\phi=0$ and $\phi=1$).
Unlike Cahn-Hilliard, Allen-Cahn does not conserve the integral of $\phi$. It naturally drives systems to reduce their total interfacial area, resulting in curvature-driven boundary migration.
2. Running Curvature-Driven Grain Growth
Use the provided script to set up a 2D grid containing a circular solid grain in a liquid matrix and observe its capillarity-driven shrinkage.
# Env: phasefield-agent
python .agents/skills/mat-phase-field-non-conservative/scripts/run_grain_growth.py \
--grid-size 100 \
--radius 30 \
--steps 200 \
--dt 0.1 \
--output grain_growth.gif
Parameters:
--grid-size: Number of grid points per dimension (e.g.,100for a 100x100 2D grid).--radius: Initial radius of the circular grain in grid units.--steps: Total number of time steps to run.--dt: Time step size.--output: Filepath to save the resulting.gifanimation or.png.
Examples
Classic Shrinking Circular Grain
A universal mathematical benchmark for the Allen-Cahn equation is proving that a circular domain shrinks at a rate proportional to its curvature (the $v = M \gamma K$ law). The area of the circle must decrease linearly with time.
What ships with it
7 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.
- examples/benchmark-dendrite/dendrite.gif 791 KB
- examples/benchmark-dendrite/README.md 3.4 KB
- examples/benchmark-grain/grain_growth_area_decay.png 23 KB
- examples/benchmark-grain/grain_growth.gif 254 KB
- examples/benchmark-grain/README.md 3.2 KB
- scripts/run_dendrite_growth.py 4.2 KB runs code
- scripts/run_grain_growth.py 5.4 KB runs code
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.
- 3d ago First seen · 68 lines · 29 tokens per session scan A cfee5233e726
mat-phase-field-non-conservative is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (161 stars, last pushed 3d ago), licensed MIT. It adds 29 tokens to every session and 824 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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