Compute electronic transport properties (mobility, conductivity, Seebeck coefficient) using DFT and AMSET via atomate2.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electronic-transport --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mat-dft-electronic-transport .claude/skills/mat-dft-electronic-transport && rm -rf skills-srcUse ~/.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/
Install the "mat-dft-electronic-transport" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transport into .claude/skills/mat-dft-electronic-transport/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-electronic-transport", 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 install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transportType 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.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electronic-transport --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mat-dft-electronic-transport .agents/skills/mat-dft-electronic-transport && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mat-dft-electronic-transport" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transport into .agents/skills/mat-dft-electronic-transport/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-electronic-transport", 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.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electronic-transport --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mat-dft-electronic-transport .cursor/skills/mat-dft-electronic-transport && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mat-dft-electronic-transport" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transport into .cursor/skills/mat-dft-electronic-transport/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-electronic-transport", 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 https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/mat-dft-electronic-transport--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electronic-transport --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mat-dft-electronic-transport .gemini/skills/mat-dft-electronic-transport && rm -rf skills-srcUse ~/.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/
Install the "mat-dft-electronic-transport" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transport into .gemini/skills/mat-dft-electronic-transport/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-electronic-transport", 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.
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electronic-transportInstalls 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).
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mat-dft-electronic-transport .github/skills/mat-dft-electronic-transport && rm -rf skills-srcUse ~/.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/
Install the "mat-dft-electronic-transport" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transport into .github/skills/mat-dft-electronic-transport/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-electronic-transport", 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.
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-dft-electronic-transport --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mat-dft-electronic-transport .opencode/skills/mat-dft-electronic-transport && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mat-dft-electronic-transport" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-electronic-transport into .opencode/skills/mat-dft-electronic-transport/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-electronic-transport", 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.
mat-dft-electronic-transportCompute electronic transport properties (mobility, conductivity, Seebeck coefficient) using DFT and AMSET via atomate2.
Mat Dft Electronic Transport is an agent skill from learningmatter-mit/AtomisticSkills. Compute electronic transport properties (mobility, conductivity, Seebeck coefficient) using DFT and AMSET via atomate2.
Its SKILL.md is about 870 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `examples/GaAs/README.md`, `examples/GaAs/gaas_flow.json` and `scripts/generate_inputs.py`).
The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.
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.
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.
Links to these hosts (documentation or services it may open):
doi.orggithub.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Mat Dft Electronic Transport loads about 865 tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 331 words of instructions outside code blocks.
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.
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.
The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 331 words, ~865 tokens.
.claude/skills/mat-dft-electronic-transport/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.To determine high-fidelity electronic transport properties (e.g., carrier mobility $\mu$, Seebeck coefficient $S$, and electrical conductivity $\sigma$) across various doping concentrations and temperatures using the AMSET (Ab initio Scattering and Transport) package integrated directly into an atomate2 VASP computational flow.
Machine Learning Interatomic Potentials (MLIPs) only predict energies, forces, and stresses; they lack proper electronic wavefunction representations. True electronic transport capabilities require coupling dense Density Functional Theory (DFT) band structures with detailed scattering matrix calculations (acoustic deformation potential scattering, polar optical phonon scattering, etc.). This skill leverages VaspAmsetMaker to seamlessly chain these calculations natively.
Use the provided script to generate the sequence (DAG) of VASP computations targeting electronic transport. This automated DAG coordinates structure relaxation, uniform band structure extraction, evaluation of the elastic tensor, and calculations of deformation potentials.
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/generate_inputs.py --output amset_flow.jsonBecause this workflow contains numerous sequential and parallel VASP evaluations (e.g., generating strained supercells for deformation potentials), it should be passed to your job management framework rather than run individually. The default script simply serializes the theoretical DAG to JSON.
If operating on a compute-capable node with vasp_std available, it can be tested locally using:
import jobflow
# Assuming `flow` is the defined VaspAmsetMaker output
jobflow.run_locally(flow, create_folders=True)Once completed, the final node wraps the AMSET runner. Resulting transport parameters (Mobility, Conductivity) will be dumped into a structured .json and amset.log inside the final Job's folder. Parse these properties natively using standard amset.plot utilities.
Run the example demonstrating the DAG generation for GaAs transport calculations.
cd ${CLAUDE_SKILL_DIR}/examples/GaAs
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ../../scripts/generate_inputs.py --output gaas_flow.jsoncpu environment (atomate2); amset is its transport extra (cpu+transport), needed only where the AMSET jobs execute.40x40x40).Author: Bowen Deng Contact: GitHub
© 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
SKILL.md and 3 other files (scripts) in skills/mat-dft-electronic-transport of learningmatter-mit/AtomisticSkills.
Open the folder on GitHubat commit 6257444
Mat Dft Electronic Transport 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mat Dft Electronic Transport this skilllearningmatter-mit/AtomisticSkills | 176 | — | ~865 | Automated safety check: Pass | MIT | |
| Conducting Mobile App Penetration Testmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Mobile Testingthedaviddias/Front-End-Checklist | 74k | — | ~517 | Automated safety check: Pass | MIT | |
| Desktop ElectronOpenHands/OpenHands | 90k | — | ~302 | Automated safety check: Pass | MIT | |
| Agent Spec Mobile React Nativeruvnet/ruflo | 74k | 3 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Developing Capacitor MobileTriliumNext/Trilium | 38k | — | ~4.7k | Automated safety check: Pass | AGPL-3.0 |
mukul975/Anthropic-Cybersecurity-Skills
Conducts penetration testing of iOS and Android mobile applications following the OWASP Mobile Application Security Testing Guide (MASTG) to identify vulnerabilities in data storage, network…
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing CI coverage, automated checks, or test strategy related to Test on real mobile devices and viewports.
OpenHands/OpenHands
This skill should be used when the user asks to "change the desktop app", "package Electron", "build a universal macOS app", "bundle Node or uv", "fix Electron startup", or changes electron/…
ruvnet/ruflo
Agent skill for spec-mobile-react-native - invoke with $agent-spec-mobile-react-native
TriliumNext/Trilium
A skill your agent uses when working on the Trilium mobile app (apps/mobile, Capacitor for Android/iOS) or on the standalone code paths that only run inside it — request routing on capacitor:// vs…
thedaviddias/Front-End-Checklist
A skill your agent uses when implementing animated gradients, complex CSS transitions that involve custom property values, or building a typed design token system where custom property misuse should…
learningmatter-mit/AtomisticSkills
Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification.
learningmatter-mit/AtomisticSkills
Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation.
learningmatter-mit/AtomisticSkills
Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).
learningmatter-mit/AtomisticSkills
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
learningmatter-mit/AtomisticSkills
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
learningmatter-mit/AtomisticSkills
Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.
Compute electronic transport properties (mobility, conductivity, Seebeck coefficient) using DFT and AMSET via atomate2. Mat Dft Electronic Transport is an agent skill from learningmatter-mit/AtomisticSkills. Compute electronic transport properties (mobility, conductivity, Seebeck coefficient) using DFT and AMSET via atomate2.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a claude-code`. Or copy the skill folder (skills/mat-dft-electronic-transport in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-dft-electronic-transport in your project. Claude Code loads it when a task matches its description.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-electronic-transport -a codex`. Or copy the skill folder (skills/mat-dft-electronic-transport in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-dft-electronic-transport in your project. Codex loads it when a task matches its description.
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-dft-electronic-transport -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-dft-electronic-transport, .gemini/skills/mat-dft-electronic-transport, .github/skills/mat-dft-electronic-transport and .opencode/skills/mat-dft-electronic-transport in your project.
Going by SKILL.md and its folder, Mat Dft Electronic Transport needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md names 2 domains. As links in the text: doi.org and github.com. This is read from the text; nothing was executed.
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.
Mat Dft Electronic Transport is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 865 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Mat Dft Electronic Transport: Conducting Mobile App Penetration Test (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Mobile Testing (thedaviddias/Front-End-Checklist, 74k stars), Desktop Electron (OpenHands/OpenHands, 90k stars) and Agent Spec Mobile React Native (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.
