Install the "mat-amorphization" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-amorphization into .claude/skills/mat-amorphization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-amorphization", 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.
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-amorphization -a codex
Project install goes to .agents/skills/; add -g for ~/.codex/skills/.
Install the "mat-amorphization" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-amorphization into .agents/skills/mat-amorphization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-amorphization", 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-amorphization -a cursor
Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.
Install the "mat-amorphization" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-amorphization into .cursor/skills/mat-amorphization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-amorphization", 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.
--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-amorphization -a gemini-cli
Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.
Install the "mat-amorphization" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-amorphization into .gemini/skills/mat-amorphization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-amorphization", 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.
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-amorphization -a github-copilot
Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.
Install the "mat-amorphization" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-amorphization into .github/skills/mat-amorphization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-amorphization", 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-amorphization -a opencode
OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
Install the "mat-amorphization" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-amorphization into .opencode/skills/mat-amorphization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-amorphization", 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-amorphization
GitHub stars
175
Token cost
~1.3k tokens
SKILL.md length
551 words
Files
7 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT
At a glance
Generate amorphorized structures from crystalline starting points using a melt-quench MD protocol.
Works in 3 steps: Preparation → Execution (The Melt-Quench Cycle) → Analysis & Verification
SKILL.md covers Goal, Protocol: Melt-Quench, Instructions and Helper Scripts, plus 2 more sections
Runs Python scripts from its folder
What it does
Mat Amorphization is an agent skill from learningmatter-mit/AtomisticSkills. Generate amorphorized structures from crystalline starting points using a melt-quench MD protocol.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts (for example `examples/LiCl/README.md`, `scripts/analyze_amorphous.py` and `scripts/prep_supercell.py`).
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-amorphization”
Requirements
Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
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 2 files in scripts/ (Python), 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 Amorphization loads about 1.3k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 551 words of instructions outside code blocks.
Always· name and description, kept in context so the agent knows when to use it
~29
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.
Download SKILL.mdSave it as .claude/skills/mat-amorphization/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
mat-amorphization
description
Generate amorphorized structures from crystalline starting points using a melt-quench MD protocol.
metadata.category
materials
metadata.venv
cpu, mlip
Amorphorization
<!-- mcp-tools-note -->
[!NOTE]
Steps written server.tool are MCP tool calls: mace.load_model is the load_model
tool of the mace server (mcp__mace__load_model, or
mcp__plugin_atomistic-skills_mace__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:
To generate disordered, amorphous structures from crystalline inputs using molecular dynamics (MD). This is achieved through a "melt-quench" protocol, where the material is heated above its melting point and then rapidly cooled to "freeze" the liquid-like disorder.
Protocol: Melt-Quench
The standard Computational amorphization protocol involves:
Supercell Setup: The system must be large enough to avoid spurious periodicity effects in the amorphous state. Generally, $>100$ atoms is recommended.
Melting (Stage A): Heat the system to $T_{melt}$. $T_{melt}$ should be significantly higher than the experimental melting point (often 1000K higher) to ensure rapid loss of crystalline memory within MD timescales.
Equilibration (Stage A/B): Maintain the liquid at $T_{melt}$ for several picoseconds to ensure structural randomized.
Quenching (Stage B): Cool the system linearly to the target temperature (e.g., 300K).
Cooling Rate: A critical parameter. Typical MD cooling rates are $1-10$ K/ps ($10^{12}-10^{13}$ K/s). Slower rates yield more stable, realistic amorphous structures but are computationally expensive.
Annealing/Equilibration (Stage C): Relax the density and local structure at the target temperature.
Quenched/Static Relaxation (Stage D): Perform a final geometry optimization (0K) to find the local energy minimum of the amorphous state.
Instructions
1. Preparation
Supercell: Use the prep_supercell.py helper script. By default, it generates an orthorhombic conventional supercell with approximately 100 atoms, ensuring a robust starting point for amorphization.
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/prep_supercell.py --input crystalline.cif --output supercell.cif
Foundation Potential: Select a robust model like MACE-MP-large or CHGNet using the mace.load_model (or similar) tool.
2. Execution (The Melt-Quench Cycle)
Amorphization is performed by calling the run_md tool in a sequence:
Show full SKILL.md (235 more words)Show less
Stage 1: Melting
Heat the system to a high temperature (e.g., 3000K) to eliminate crystalline order.
Tool: mace.run_md
Thermostat: nvt_langevin (Robust for high-T dynamics).
Use the analyze_amorphous.py script to verify the results:
RDF (Radial Distribution Function): Confirm the absence of long-range order.
Coordination Number: Check local bonding environments.
Helper Scripts
prep_supercell.py: Expands a unit cell to a supercell.
analyze_amorphous.py: Calculates RDF and coordination numbers from the final structure.
RDF (Radial Distribution Function): Crystalline structures show discrete, sharp peaks at long distances. Amorphous structures show a sharp first peak, a broader second peak, and then decay to 1.0 (no long-range order).
Coordination Number: Check if the local coordination (e.g., 4 for Si) is maintained despite the global disorder.
Mat Amorphization 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.
Mat Amorphization compared with similar skills
Skill
Stars
Used in
Tokens
Auto-check
Licence
Repo updated
Mat Amorphization this skilllearningmatter-mit/AtomisticSkills
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
Required groundwork before any use_figma call: the rules and reference files for running JavaScript in a Figma file through the Plugin API without common failures.
Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification.
Generate amorphorized structures from crystalline starting points using a melt-quench MD protocol. Mat Amorphization is an agent skill from learningmatter-mit/AtomisticSkills. Generate amorphorized structures from crystalline starting points using a melt-quench MD protocol.
How do I install Mat Amorphization in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-amorphization -a claude-code`. Or copy the skill folder (skills/mat-amorphization in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-amorphization in your project. Claude Code loads it when a task matches its description.
How do I install Mat Amorphization in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-amorphization -a codex`. Or copy the skill folder (skills/mat-amorphization in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-amorphization in your project. Codex loads it when a task matches its description.
Can I use Mat Amorphization 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-amorphization -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-amorphization, .gemini/skills/mat-amorphization, .github/skills/mat-amorphization and .opencode/skills/mat-amorphization in your project.
What does Mat Amorphization need to run?
Going by SKILL.md and its folder, Mat Amorphization needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Mat Amorphization 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 Amorphization 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 Amorphization use?
Mat Amorphization 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 Amorphization use?
About 1.3k tokens (SKILL.md is roughly 5.1k 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 Amorphization?
Skills that share tags, products or a category with Mat Amorphization: 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 Amorphization?
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.
