Agent skill

Mat Surface Energy

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).

MITAuto-check passed

Install Mat Surface Energy

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-surface-energy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mat-surface-energy .claude/skills/mat-surface-energy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "mat-surface-energy" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy into .claude/skills/mat-surface-energy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-surface-energy", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-surface-energy --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mat-surface-energy .agents/skills/mat-surface-energy && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mat-surface-energy" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy into .agents/skills/mat-surface-energy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-surface-energy", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-surface-energy --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mat-surface-energy .cursor/skills/mat-surface-energy && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mat-surface-energy" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy into .cursor/skills/mat-surface-energy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-surface-energy", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/mat-surface-energy

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-surface-energy --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mat-surface-energy .gemini/skills/mat-surface-energy && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "mat-surface-energy" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy into .gemini/skills/mat-surface-energy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-surface-energy", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-surface-energy

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mat-surface-energy .github/skills/mat-surface-energy && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "mat-surface-energy" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy into .github/skills/mat-surface-energy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-surface-energy", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills mat-surface-energy --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mat-surface-energy .opencode/skills/mat-surface-energy && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mat-surface-energy" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-surface-energy into .opencode/skills/mat-surface-energy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-surface-energy", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: mat-surface-energy
GitHub stars: 175
Token cost: ~1.3k tokens
SKILL.md length: 416 words
Files: 15 (incl. scripts)
Skills in repo: 129
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).

Works in 6 steps: Select Level of Theory: Choose the… → Relax Bulk Reference: Perform a… → Generate Slabs: Create oriented slabs… → …
SKILL.md covers Goal, Instructions, Examples and Constraints
Runs Python and Shell scripts from its folder

What it does

Mat Surface Energy is an agent skill from learningmatter-mit/AtomisticSkills. Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts (for example `examples/FCC_metals/README.md`, `examples/FCC_metals/get_bulk_cu.py` and `examples/FCC_metals/run_surface_energy.sh`).

It works with Model Context Protocol. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

Example prompts

“/mat-surface-energy”

Requirements

Python 3
A Bash shell

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Select Level of Theory: Choose the target accuracy level for surface energy calculations.
Relax Bulk Reference: Perform a high-accuracy relaxation of the bulk material to serve as the reference energy.
Generate Slabs: Create oriented slabs for the target (hkl) planes.
Relax Slabs: Perform structural relaxation on all generated slabs.
Calculate Surface Energy: Compute the surface energy for each plane.
Generate Wulff Shape: Construct the Wulff shape from the calculated surface energies.

What it can do on your machine

Read from SKILL.md and the folder at commit 7f2d86d. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python and Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mat Surface Energy loads about 1.3k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 416 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~31
When it runs · the whole SKILL.md, loaded when a task matches: ~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 416 words, ~1,307 tokens.

Download SKILL.mdSave it as .claude/skills/mat-surface-energy/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.

name: mat-surface-energy
description: Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).
metadata.category: materials
metadata.venv: cpu, mlip

Surface Energy Calculation

<!-- mcp-tools-note -->

[!NOTE] Steps written server.tool are MCP tool calls: matgl.load_model is the load_model tool of the matgl server (mcp__matgl__load_model, or mcp__plugin_atomistic-skills_matgl__load_model when installed as a plugin). Without a connected server, run the same tools from the shell. Tools named in one command share a process, so a model loaded by load_model stays loaded:
bash
${CLAUDE_SKILL_DIR}/../../venv/run mlip python -m src.mcp_server.cli matgl load_model key=value relax_structure key=value

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

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-PES-MatPES-r2SCAN-1M-2026.9 (MatGL), TensorNet-MatPES-r2SCAN-v2025.1-PES (MatGL), or MACE-MH-1 with matpes_r2scan head.
- See ml-foundation-potentials for detailed guidance.

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

bash

matgl.load_model(model_name="CHGNet-PES-MatPES-r2SCAN-1M-2026.9")
matgl.relax_structure(
    structure_data="bulk.cif",
    relax_cell=True,
    fmax=0.01,
    output_dir="bulk_relaxation/"
)

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

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

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/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.

Relax Slabs: Perform structural relaxation on all generated slabs.

bash

matgl.load_model(model_name="CHGNet-PES-MatPES-r2SCAN-1M-2026.9")
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.

Calculate Surface Energy: Compute the surface energy for each plane.
bash
```
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/calculate_surface_energy.py \
    --bulk_energy_per_atom -4.567 \
    --slab_dir slab_relaxations/ \
    --output surface_energies.json
```
Surface energy is calculated as: $$\gamma = \frac{E_{slab} - N \cdot E_{bulk}}{2A}$$ where $E_{slab}$ is the total energy of the slab, $N$ is the number of atoms in the slab, $E_{bulk}$ is the energy per atom of the bulk, and $A$ is the surface area.

Generate Wulff Shape: Construct the Wulff shape from the calculated surface energies.

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/generate_wulff.py \
    --energies_json surface_energies.json \
    --bulk bulk_relaxation/relaxed_structure.cif \
    --output wulff_shape.png

Show full SKILL.md (82 more words)Show less

Examples

Example 1: Aluminum (fcc) Surface Energy

bash

# Generate slabs up to index 1 (100, 110, 111)
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/create_slabs.py --bulk Al.cif --max_index 1

# Load and run relaxations using CHGNet
matgl.load_model(model_name="CHGNet-PES-MatPES-r2SCAN-1M-2026.9")
matgl.relax_structure(structure_data="Al.cif", relax_cell=True, output_dir="bulk_relax")
matgl.relax_structure(structure_data="slabs/", relax_cell=False, output_dir="slab_relax")

# Calculate and generate Wulff shape
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/calculate_surface_energy.py --bulk_energy_per_atom -3.36 --slab_dir slab_relax/
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/generate_wulff.py --energies_json surface_energies.json --bulk Al.cif

Constraints

Cell Relaxation: Do NOT relax the unit cell during slab relaxation. The surface area must remain constant to match the bulk reference.
Vacuum: Ensure sufficient vacuum (typically >15 Å) to avoid interactions between periodic images.
Slab Thickness: Ensure sufficient slab thickness (typically >10 Å) to recover bulk-like behavior in the center of the slab.
Consistency: Use the SAME MLIP and convergence settings for both bulk and slab relaxations.

Author: Bowen Deng Contact: GitHub @learningmatter-mit

Files

SKILL.md and 14 other files (scripts) in skills/mat-surface-energy of learningmatter-mit/AtomisticSkills.

SKILL.md
examples/FCC_metals/Cu_bulk.cif
examples/FCC_metals/README.md
examples/FCC_metals/get_bulk_cu.py
examples/FCC_metals/run_surface_energy.sh
examples/FCC_metals/slabs/slab_10-1_3.cif
examples/FCC_metals/slabs/slab_11-1_2.cif
examples/FCC_metals/slabs/slab_110_1.cif
examples/FCC_metals/slabs/slab_111_0.cif
examples/FCC_metals/surface_energies.json
examples/FCC_metals/wulff_shape.json
examples/FCC_metals/wulff_shape.png
scripts/calculate_surface_energy.py
scripts/create_slabs.py
scripts/generate_wulff.py

Open the folder on GitHubat commit 7f2d86d

Compare with similar skills

Mat Surface Energy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Mat Surface Energy

What does Mat Surface Energy do?

Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape). Mat Surface Energy is an agent skill from learningmatter-mit/AtomisticSkills. Calculate surface energy of various (hkl) planes and generate the equilibrium crystal shape (Wulff shape).

How do I install Mat Surface Energy in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a claude-code`. Or copy the skill folder (skills/mat-surface-energy in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-surface-energy in your project. Claude Code loads it when a task matches its description.

How do I install Mat Surface Energy in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a codex`. Or copy the skill folder (skills/mat-surface-energy in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-surface-energy in your project. Codex loads it when a task matches its description.

Can I use Mat Surface Energy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-surface-energy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mat-surface-energy, .gemini/skills/mat-surface-energy, .github/skills/mat-surface-energy and .opencode/skills/mat-surface-energy in your project.

What does Mat Surface Energy need to run?

Going by SKILL.md and its folder, Mat Surface Energy needs Python and a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Mat Surface Energy access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Mat Surface Energy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Mat Surface Energy use?

Mat Surface Energy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mat Surface Energy use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mat Surface Energy?

Skills that share tags, products or a category with Mat Surface Energy: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 37k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Surface Energy?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Mat Surface Energy

Install Mat Surface Energy

Facts

At a glance

What it does

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Surface Energy Calculation

Goal

Instructions

Examples

Example 1: Aluminum (fcc) Surface Energy

Constraints

Files

Compare with similar skills

Similar skills

MCP Server Builder

MCP Server Builder

MCP Integration for Plugins

Figma use_figma Plugin API Rules

Stitch to Remotion Walkthrough Videos

MCP Development

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Questions about Mat Surface Energy

Install Mat Surface Energy

Facts

At a glance

What it does

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Surface Energy Calculation

Goal

Instructions

Examples

Example 1: Aluminum (fcc) Surface Energy

Constraints

Files

Compare with similar skills

Similar skills

More from learningmatter-mit/AtomisticSkills

Related

Questions about Mat Surface Energy