Install the "mat-dft-vasp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-vasp into .claude/skills/mat-dft-vasp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-vasp", 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-dft-vasp -a codex
Project install goes to .agents/skills/; add -g for ~/.codex/skills/.
Install the "mat-dft-vasp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-vasp into .agents/skills/mat-dft-vasp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-vasp", 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-dft-vasp -a cursor
Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.
Install the "mat-dft-vasp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-vasp into .cursor/skills/mat-dft-vasp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-vasp", 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-dft-vasp -a gemini-cli
Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.
Install the "mat-dft-vasp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-vasp into .gemini/skills/mat-dft-vasp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-vasp", 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-dft-vasp -a github-copilot
Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.
Install the "mat-dft-vasp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-vasp into .github/skills/mat-dft-vasp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-vasp", 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-dft-vasp -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-dft-vasp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-dft-vasp into .opencode/skills/mat-dft-vasp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-dft-vasp", 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-dft-vasp
GitHub stars
175
Token cost
~1.1k tokens
SKILL.md length
434 words
Files
7 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT
At a glance
Prepare VASP input files, run DFT calculations (locally or remotely via atomate2), and parse VASP output results.
Works in 2 steps: Prepare VASP Inputs → Parse VASP Results
SKILL.md covers Goal, Instructions, Constraints and References
Runs Python scripts from its folder
What it does
Mat Dft Vasp is an agent skill from learningmatter-mit/AtomisticSkills. Prepare VASP input files, run DFT calculations (locally or remotely via atomate2), and parse VASP output results.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts (for example `examples/vasp-workflow/README.md`, `scripts/parse_vasp_results.py` and `scripts/prepare_vasp_inputs.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-dft-vasp”
Requirements
Python 3
Workflow steps
2 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):
doi.org
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 Dft Vasp loads about 1.1k tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 434 words of instructions outside code blocks.
Always· name and description, kept in context so the agent knows when to use it
~32
When it runs· the whole SKILL.md, loaded when a task matches
~1.1k
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.
Download SKILL.mdSave it as .claude/skills/mat-dft-vasp/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
mat-dft-vasp
description
Prepare VASP input files, run DFT calculations (locally or remotely via atomate2), and parse VASP output results.
metadata.category
materials
metadata.venv
cpu
mat-dft-vasp
<!-- mcp-tools-note -->
[!NOTE]
Steps written server.tool are MCP tool calls: atomate2.run_atomate2_vasp_calculation is the run_atomate2_vasp_calculation
tool of the atomate2 server (mcp__atomate2__run_atomate2_vasp_calculation, or
mcp__plugin_atomistic-skills_atomate2__run_atomate2_vasp_calculation 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 cpu python -m src.mcp_server.cli atomate2 run_atomate2_vasp_calculation key=value
Goal
To prepare VASP input files (INCAR, POTCAR, KPOINTS, POSCAR) locally for a structure or list of structures, and to parse the resulting VASP output files (vasprun.xml, OUTCAR) to extract the final energies, forces, stress, and geometries.
[!TIP]
Atomate2 Recommendation: It is highly recommended to run VASP through the atomate2 MCP server/tools instead of manually using this skill. atomate2 natively handles automatic SLURM job submission, dynamic error handling and on-the-fly corrections, automated result parsing, and MongoDB cloud storage.
Instructions
Step 1. Prepare VASP Inputs
Use the prepare_vasp_inputs.py script to generate local input files from a structure (CIF, XYZ, POSCAR) or a directory of structures.
structure_path: Path to a single structure or a directory of structures.
output_dir: Location to write the inputs. If structure_path is a directory, subdirectories will be created.
--preset_type: Standard VASP presets. Options include omat, mp, matpes-pbe, and matpes-r2scan.
--calculation_type: Defaults to relaxation. Use static for SCF static single-point.
(Note: Once inputs are generated, you can submit the VASP jobs to an HPC or local cluster. If you instead want to run VASP jobs automatically through Jobflow on configured remote resources, consider using the atomate2.run_atomate2_vasp_calculation MCP tool).
Show full SKILL.md (182 more words)Show less
Step 2. Parse VASP Results
After the VASP calculation has concluded, extract the output data (energy, forces, stress, structure) using parse_vasp_results.py. This handles both single directories (containing a vasprun.xml) and root directories with multiple subdirectories.
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/parse_vasp_results.py \
<vasp-output-dir> \
--save_to_file parsed_results.json
Constraints
Environments: The scripts require the cpu environment.
Parsing Robustness: The parser requires at a minimum vasprun.xml to succeed. OUTCAR is read supplementary.
POTCARs: Note that prepare_vasp_inputs.py relies on pymatgen to write POTCAR files, which requires your PMG_DEFAULT_FUNCTIONAL or .pmgrc.yaml to point to a valid POTCAR directory.
KPOINTS pitfall: The installed pymatgen's VaspInput.write_input may skip writing KPOINTS (the script prints it as saved anyway, and vis.kpoints stays None). Verify KPOINTS exists after generation; if missing, write the Gamma-centered mesh manually (e.g. Kpoints.gamma_automatic(lattice, kpts=0.22) from pymatgen.io.vasp.outputs) before submitting.
Remote runs: mcp__atomate2__run_atomate2_vasp_calculation is the preferred submission path, but it can only target hosts registered in ~/.config/jobflow-remote/jobflow_remote.yaml To submit to another cluster, add a worker entry for it there first.
References
Kresse, G. & Furthmüller, J., "Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set". Physical Review B, 54, 11169. DOI
Mat Dft Vasp 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 Dft Vasp compared with similar skills
Skill
Stars
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Auto-check
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Repo updated
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Prepare VASP input files, run DFT calculations (locally or remotely via atomate2), and parse VASP output results. Mat Dft Vasp is an agent skill from learningmatter-mit/AtomisticSkills. Prepare VASP input files, run DFT calculations (locally or remotely via atomate2), and parse VASP output results.
How do I install Mat Dft Vasp in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-vasp -a claude-code`. Or copy the skill folder (skills/mat-dft-vasp in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-dft-vasp in your project. Claude Code loads it when a task matches its description.
How do I install Mat Dft Vasp in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-dft-vasp -a codex`. Or copy the skill folder (skills/mat-dft-vasp in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-dft-vasp in your project. Codex loads it when a task matches its description.
Can I use Mat Dft Vasp 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-dft-vasp -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-vasp, .gemini/skills/mat-dft-vasp, .github/skills/mat-dft-vasp and .opencode/skills/mat-dft-vasp in your project.
What does Mat Dft Vasp need to run?
Going by SKILL.md and its folder, Mat Dft Vasp needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Mat Dft Vasp access the network?
SKILL.md names 2 domains. As links in the text: doi.org and github.com. This is read from the text; nothing was executed.
Is Mat Dft Vasp 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 Dft Vasp use?
Mat Dft Vasp 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 Dft Vasp use?
About 1.1k tokens (SKILL.md is roughly 4.3k 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 Dft Vasp?
Skills that share tags, products or a category with Mat Dft Vasp: 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 Dft Vasp?
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.
