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Agent skill

Mat Disorder

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Generate ordered structures from disordered starting points with partial occupancies.

MITAuto-check passed

Install Mat Disorder

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-disorder -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills mat-disorder --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-disorder .claude/skills/mat-disorder && 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/

Facts

Skill name
mat-disorder
GitHub stars
176
Token cost
~1k tokens
SKILL.md length
408 words
Files
11 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Generate ordered structures from disordered starting points with partial occupancies.

  • Works in 2 steps: Identify Disordered Structures: Ensure… → Generate Ordered Candidates: Use the…
  • SKILL.md covers Goal, Instructions, Standalone Usage (Python API) and Iterative Cluster Expansion…, plus 1 more section
  • Runs Python scripts from its folder

What it does

Mat Disorder is an agent skill from learningmatter-mit/AtomisticSkills. Generate ordered structures from disordered starting points with partial occupancies.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts (for example `examples/CuAg_MC/README.md`, `examples/CuAg_MC/cluster_expansion.json` and `scripts/iterative_ce_training.py`).

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-disorder”

Requirements

  • Python 3

Workflow steps

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

  1. Identify Disordered Structures: Ensure your input structure (typically a CIF file) contains fractional occupancies or partial site…
  2. Generate Ordered Candidates: Use the ranking and sampling strategy based on Ewald energy to pick configurations that satisfy stoichiometry…

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. 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 4 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 Disorder loads about 1k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 408 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~1k

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 6257444, republished under its MIT licence (© learningmatter-mit). 408 words, ~1,015 tokens.

Download SKILL.mdSave it as .claude/skills/mat-disorder/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
mat-disorder
description
Generate ordered structures from disordered starting points with partial occupancies.
metadata.category
materials
metadata.venv
cpu

Disordered Material

Goal

To generate clean, ordered atomic configurations from disordered starting structures (e.g., experimental structures with fractional occupancies). These ordered candidates can be used for ground-state property calculations, phase stability analysis, or as starting points for MLIP training.

Instructions

  1. Identify Disordered Structures: Ensure your input structure (typically a CIF file) contains fractional occupancies or partial site occupancies.

  2. Generate Ordered Candidates: Use the ranking and sampling strategy based on Ewald energy to pick configurations that satisfy stoichiometry while minimizing electrostatic repulsion.

    bash
    ${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/run_ordering.py disordered.cif \
    --n_structures 50 --target_atoms 50 --output_dir ordered_results
Strategy: Ewald Energy Ranking

The script uses pymatgen's OrderDisorderedStructureTransformation with a fast Ewald-based solver (ALGO_FAST). It generates a large pool of candidates, ranks them by Ewald energy, and samples across the spectrum to ensure both low-energy (ground-state-like) and higher-energy (excited-state-like) configurations are captured.

Supercell Expansion

For structures with very few atoms per cell or complex stoichiometry, the script automatically searches for a supercell expansion that:

  1. Is close to the --target_atoms (default: 50).
  2. Maintains valid stoichiometry (total counts must be integers).
  3. Is as cubic as possible to avoid long, thin cells.
  • Limit: Avoid setting --target_atoms too high (>120) if you plan to follow up with DFT calculations.

Standalone Usage (Python API)

python
from .agents.skills.mat_disorder.scripts.order_disorder_sampler import OrderDisorderSampler
from ase.io import read

atoms = read("disordered.cif")
sampler = OrderDisorderSampler(
    atoms=atoms,
    n_structures=20,
    target_atoms=60,
    include_perturbation=1
)
ordered_structures = sampler.sample()

Iterative Cluster Expansion Training

For more accurate Cluster Expansions, use the iterative training workflow which cycles between structure generation, relaxation, model fitting, and Monte Carlo sampling to fully explore the configuration space.

[!TIP] Train your CE model first: Please refer to the ml-cluster-expansion skill to train a robust Cluster Expansion model. This skill focuses on using that trained model for simulations.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/iterative_ce_training.py \
    primordial.cif \
    --iterations 5 \
    --n_samples 20 \
    --mlip_model MACE-MP-medium \
    --output_dir ce_results
Show full SKILL.md (139 more words)Show less
Workflow
  1. Initial Sampling: Generates random ordered structures from the primordial structure.
  2. Relaxation: Relaxes structures using a MACE MLIP to get accurate energies.
  3. Mapping Check: Verifies if relaxed structures still map to the initial lattice configuration.
  4. Training: Fits a Cluster Expansion model to the valid training data.
  5. Active Learning: Runs Monte Carlo simulations with the current model to find new low-energy configurations.
  6. Coverage Check: Identifies if MC-sampled structures are "new" (uncovered by training set) and adds them to the next iteration loop.

Constraints

  • Environment:
    • cpu for basic sampling, iterative training and smol-based analysis.
  • Fractional Occupancies: The input must be a format that carries occupancy information (like CIF with _atom_site_occupancy).
  • Algorithm: Uses ALGO_FAST for efficiency; while fast, it may not find the global Ewald minimum for extremely large cells.

Author: Bowen Deng Contact: GitHub @learningmatter-mit

© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts) in skills/mat-disorder of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/CuAg_MC/README.md
  • examples/CuAg_MC/cluster_expansion.json
  • examples/CuAg_MC/mc_300K_energy.png
  • examples/CuAg_MC/mc_300K_final.cif
  • examples/CuAg_MC/mc_300K_initial.cif
  • examples/CuAg_MC/primordial.cif
  • scripts/iterative_ce_training.py
  • scripts/order_disorder_sampler.py
  • scripts/relax_wrapper.py
  • scripts/run_ordering.py

Open the folder on GitHubat commit 6257444

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Mat Disorder 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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CSS Orderthedaviddias/Front-End-Checklist74k—~404Automated safety check: PassMIT
Focus Orderthedaviddias/Front-End-Checklist74k—~519Automated safety check: PassMIT

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Questions about Mat Disorder

What does Mat Disorder do?

Generate ordered structures from disordered starting points with partial occupancies. Mat Disorder is an agent skill from learningmatter-mit/AtomisticSkills. Generate ordered structures from disordered starting points with partial occupancies.

How do I install Mat Disorder in Claude Code?

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

How do I install Mat Disorder in Codex?

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

Can I use Mat Disorder 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-disorder -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-disorder, .gemini/skills/mat-disorder, .github/skills/mat-disorder and .opencode/skills/mat-disorder in your project.

What does Mat Disorder need to run?

Going by SKILL.md and its folder, Mat Disorder needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Mat Disorder 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 Disorder 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 Disorder use?

Mat Disorder 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 Disorder use?

About 1k tokens (SKILL.md is roughly 4.1k 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 Disorder?

Skills that share tags, products or a category with Mat Disorder: Start (Donchitos/Claude-Code-Game-Studios, 26k stars), Structured Data (thedaviddias/Front-End-Checklist, 74k stars), Bio Structural Biology Modern Structure Prediction (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and CSS Order (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Disorder?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 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.