agentmods.dev

mat-surface-energy

mat-surface-energy is a skill for Claude Code, Codex from learningmatter-mit/AtomisticSkills. It costs 27 tokens per session (1,246 once invoked), scanned A, original, MIT.

A workflow that calculates the energy needed to create different crystal surfaces and uses those results to build a Wulff shape, the predicted equilibrium shape of a crystal.

In plain words
What is it for?
It is used to relax a bulk crystal, calculate energies for selected crystallographic planes, and generate the corresponding equilibrium crystal shape.
Why use it?
It helps show which crystal faces are likely to appear most often when a crystal reaches equilibrium.

Skill for Claude CodeCodex

not rated 158 yesterday A scan 27 tokens original MIT
View the source file learningmatter-mit/AtomisticSkills
Install

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.

agentmods
npx agentmods add skills/learningmatter-mit/atomisticskills/mat-surface-energy
Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for mat-surface-energy

README.md
[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-surface-energy.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-surface-energy)
Your own site
<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/mat-surface-energy"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/mat-surface-energy.svg" alt="Measured on agentmods" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,246 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00027 $0.01246
Opus 5 $0.00014 $0.00623
Sonnet 5 $0.00005 $0.00249
Haiku 4.5 $0.00003 $0.00125

Measured yesterday against content hash e979a13178b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mat-surface-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 yesterday.

The scan reads SKILL.md. This mod also ships 5 executable files (examples/FCC_metals/get_bulk_cu.py, examples/FCC_metals/run_surface_energy.sh, scripts/calculate_surface_energy.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/mat-surface-energy/SKILL.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Surface Energy Calculation

Goal

To determine the surface energy ($\gamma$) of different crystallographic planes (hkl) and construct the equilibrium crystal shape (Wulff shape) using structural relaxation with Machine Learning Interatomic Potentials (MLIPs).

Instructions

  1. Select Level of Theory: Choose the target accuracy level for surface energy calculations.

    • Recommended: r2SCAN-level foundation potentials for high accuracy in inorganic systems.
    • Examples: CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES (MatGL), TensorNet-MatPES-r2SCAN-v2025.1-PES (MatGL), or MACE-MH-1 with matpes_r2scan head.
    • See ml-foundation-potentials for detailed guidance.

  2. Relax Bulk Reference: Perform a high-accuracy relaxation of the bulk material to serve as the reference energy.

    # Env: matgl-agent
mcp_matgl_load_model(model_name="CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES")
mcp_matgl_relax_structure(
    structure_data="bulk.cif",
    relax_cell=True,
    fmax=0.01,
    output_dir="bulk_relaxation/"
)

    Note: Record the final energy per atom ($E_{bulk}$).

  3. Generate Slabs: Create oriented slabs for the target (hkl) planes.

    # Env: base-agent
python .agents/skills/mat-surface-energy/scripts/create_slabs.py \
    --bulk bulk_relaxation/relaxed_structure.cif \
    --max_index 1 \
    --min_thickness 10.0 \
    --vacuum 15.0 \
    --output slabs/

    This script generates slabs for all unique planes up to the specified max_index.

  4. Relax Slabs: Perform structural relaxation on all generated slabs.

    # Env: matgl-agent
mcp_matgl_load_model(model_name="CHGNet-MatPES-r2SCAN-2025.2.10-2.7M-PES")
mcp_matgl_relax_structure(
    structure_data="slabs/",
    relax_cell=False,  # DO NOT relax cell for slabs (fixed area)
    fmax=0.02,
    output_dir="slab_relaxations/"
)

    Important: Keep the unit cell fixed (relax_cell=False) to maintain the target surface area.

Read the full file on GitHub · 104 lines

Files

What ships with it

14 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.

Changes

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.

  1. yesterday First seen · 104 lines · 27 tokens per session scan A e979a13178b9

Subscribe to this mod's changes

mat-surface-energy is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 1,246 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.

Scientificmaterials
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