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Agent skill

Mat Md Probability Density

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory.

MITAuto-check passedResearch & Science

Install Mat Md Probability Density

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-md-probability-density -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills mat-md-probability-density --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-md-probability-density .claude/skills/mat-md-probability-density && 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-md-probability-density
GitHub stars
176
Token cost
~928 tokens
SKILL.md length
451 words
Files
6 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory.

  • Works in 3 steps: MD Simulation: Run an MD simulation at… → Calculate Probability Density: Use the… → Visualize in VESTA
  • Tasks that involve Physical and earth sciences
  • SKILL.md covers Goal, Instructions, Examples and Constraints
  • Runs Python scripts from its folder

What it does

Mat Md Probability Density is an agent skill from learningmatter-mit/AtomisticSkills. Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory.

Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `examples/LiErBr/README.md` and `scripts/calculate_probability_density.py`).

It sits in Research & Science, covering Physical and earth sciences. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

  • Tasks that involve Physical and earth sciences

Example prompts

  • “/mat-md-probability-density”

Requirements

  • Python 3

Workflow steps

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

  1. MD Simulation: Run an MD simulation at an appropriate temperature to observe sufficient diffusion events.
  2. Calculate Probability Density: Use the provided script to extract the fractional coordinates of the targeted species over time and convert…
  3. Visualize in VESTA

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 1 file 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 Md Probability Density loads about 928 tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 451 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~928

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). 451 words, ~928 tokens.

Download SKILL.mdSave it as .claude/skills/mat-md-probability-density/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
mat-md-probability-density
description
Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory.
metadata.category
materials
metadata.venv
cpu

MD Probability Density Visualization

Goal

To visualize the spatial probability density of mobile ions (e.g., Li, Na) from an MD simulation trajectory. This helps in understanding conduction pathways and identifying preferred occupation sites within the crystal structure. The output is a volumetric data object in CHGCAR format, which can be easily visualized using VESTA.

Instructions

  1. MD Simulation: Run an MD simulation at an appropriate temperature to observe sufficient diffusion events.

    Note: Short MD trajectories (e.g., ≤10 ps) often have too few discrete ion hops to naturally form continuous probability density tubes. The resulting density will look like isolated blobs exactly at the crystal lattice sites. To visualize continuous macroscopic diffusion pathways for short trajectories, use the --log compression flag to mathematically connect the sparse pathways.

    • The trajectory is typically saved to trajectory.traj.
    • Ensure supercell_min_length is reasonably large (>10 Å) to avoid finite-size artifacts in the density mapping.
    • Allow the simulation to run long enough so that the ions sample the entire available volume (e.g., 50-100 ps or more).

  2. Calculate Probability Density: Use the provided script to extract the fractional coordinates of the targeted species over time and convert them into a spatial density grid.

    bash
    ${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/calculate_probability_density.py \
    results/md_600K/trajectory.traj \
    --species Li \
    --interval 0.2 \
    --ignore_ps 5.0 \
    --output_chgcar results/md_600K/CHGCAR_proba
    • --species: The specific diffusing ion to visualize.
    • --interval: Grid spacing in Angstroms (0.1 to 0.5 is recommended). Smaller values give smoother isosurfaces but take longer to process and generate larger files. Defaults to 0.2 Å.
    • --ignore_ps: The equilibration time to discard from the beginning of the trajectory.
    • The script will automatically detect the frame time step if a .log file is available alongside the .traj file.

  3. Visualize in VESTA:

    • Open the output CHGCAR (or CHGCAR_proba) file in VESTA.
    • Crucial VESTA Settings:
      • Because we apply Gaussian smoothing and optional logarithmic compression, simply open Objects panel > Properties > Isosurfaces to adjust colors (e.g. set the surface to yellow).
      • If you did not use --log compression on a sparse timeline, the peak values may be spread out. If you don't see any 3D clouds, your Isosurface level is too high. Try lowering it (e.g., to 0.001 or lower) until you see continuous 3D ion diffusion channels connecting the lattice sites. If you used --log, the default Isosurface level will usually connect the pathway out of the box.
Show full SKILL.md (67 more words)Show less

Examples

  • A fast workflow description based on a $Li_{12}Er_{12}Br_{48}$ Solid-State Electrolyte can be found in the LiErBr Example.

Constraints

  • Environments: The script requires the cpu environment.
  • File formats: The trajectory should be provided in ASE .traj format.
  • Memory Limits: Avoid setting --interval too small ($< 0.1$) on large supercells, as this may result in extremely large 3D grid sizes and out-of-memory errors.

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 5 other files (scripts) in skills/mat-md-probability-density of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/LiErBr/Br48Er12Li12_500.0K_nvt.traj
  • examples/LiErBr/CHGCAR_proba
  • examples/LiErBr/LiErBr.cif
  • examples/LiErBr/README.md
  • scripts/calculate_probability_density.py

Open the folder on GitHubat commit 6257444

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Categories

Questions about Mat Md Probability Density

What does Mat Md Probability Density do?

Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory. Mat Md Probability Density is an agent skill from learningmatter-mit/AtomisticSkills. Calculate and visualize the probability density of diffusing ions from a Molecular Dynamics (MD) trajectory.

When should I use Mat Md Probability Density?

Mat Md Probability Density fits situations like: tasks that involve Physical and earth sciences.

How do I install Mat Md Probability Density in Claude Code?

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

How do I install Mat Md Probability Density in Codex?

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

Can I use Mat Md Probability Density 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-md-probability-density -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-md-probability-density, .gemini/skills/mat-md-probability-density, .github/skills/mat-md-probability-density and .opencode/skills/mat-md-probability-density in your project.

What does Mat Md Probability Density need to run?

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

Does Mat Md Probability Density 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 Md Probability Density 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 Md Probability Density use?

Mat Md Probability Density 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 Md Probability Density use?

About 928 tokens (SKILL.md is roughly 3.7k 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 Md Probability Density?

Skills that share tags, products or a category with Mat Md Probability Density: Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars), Astropy (zLanqing/codex-claude-academic-skills, 4.6k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.6k stars) and Weather (trpc-group/trpc-agent-go, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mat Md Probability Density?

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.