Agent skill

Vasp Md

Ab initio molecular dynamics (AIMD) with VASP. An agent skill from Hello-QM/catgo-LRG.

AGPL-3.0Auto-check passedResearch & Science

Install Vasp Md

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill vasp-md -a claude-code

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG vasp-md --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/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/vasp-md .claude/skills/vasp-md && 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/

Paste into Claude Code

Install the "vasp-md" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md into .claude/skills/vasp-md/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vasp-md", 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

$skill-installer install https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md

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 Hello-QM/catgo-LRG --skill vasp-md -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG vasp-md --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/vasp-md .agents/skills/vasp-md && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "vasp-md" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md into .agents/skills/vasp-md/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vasp-md", 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 Hello-QM/catgo-LRG --skill vasp-md -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG vasp-md --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/vasp-md .cursor/skills/vasp-md && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "vasp-md" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md into .cursor/skills/vasp-md/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vasp-md", 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

$ gemini skills install https://github.com/Hello-QM/catgo-LRG.git --path .claude/skills/vasp-md

--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 Hello-QM/catgo-LRG --skill vasp-md -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG vasp-md --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/vasp-md .gemini/skills/vasp-md && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "vasp-md" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md into .gemini/skills/vasp-md/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vasp-md", 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.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG vasp-md

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 Hello-QM/catgo-LRG --skill vasp-md -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/vasp-md .github/skills/vasp-md && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "vasp-md" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md into .github/skills/vasp-md/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vasp-md", 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 Hello-QM/catgo-LRG --skill vasp-md -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG vasp-md --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/vasp-md .opencode/skills/vasp-md && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "vasp-md" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/vasp-md into .opencode/skills/vasp-md/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vasp-md", 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: vasp-md
GitHub stars: 205
Token cost: ~1.6k tokens
SKILL.md length: 468 words
Files: 1
Skills in repo: 75
Repo updated: Sep 22, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

Ab initio molecular dynamics (AIMD) with VASP. An agent skill from Hello-QM/catgo-LRG.

Works in 5 steps: Thermal stability — check if a structure… → Diffusion — compute diffusion… → Reaction dynamics — observe bond… → …
Tasks that involve Physical and earth sciences
SKILL.md covers When to Use, Basic NVT Molecular Dynamics, MCP Workflow and Key Parameters, plus 9 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Vasp Md is an agent skill from Hello-QM/catgo-LRG. Ab initio molecular dynamics (AIMD) with VASP. NVT/NVE ensembles, temperature control, trajectory analysis.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Physical and earth sciences. The repository describes itself as: AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission… The licence is AGPL-3.0.

When your agent uses it

Tasks that involve Physical and earth sciences

Example prompts

“/vasp-md”

Requirements

Python 3

Workflow steps

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

Thermal stability — check if a structure is stable at operating temperature
Diffusion — compute diffusion coefficients (e.g., Li-ion conductors)
Reaction dynamics — observe bond breaking/forming at finite temperature
Free energy sampling — metadynamics or thermodynamic integration
Amorphous structures — melt-quench to generate amorphous phases

What it can do on your machine

Read from SKILL.md and the folder at commit fd6291b. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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

Vasp Md loads about 1.6k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 468 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: ~1.6k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 468 words, ~1,608 tokens.

Download SKILL.mdSave it as .claude/skills/vasp-md/SKILL.md (or your agent's skills folder).

name: vasp-md
description: Ab initio molecular dynamics (AIMD) with VASP. NVT/NVE ensembles, temperature control, trajectory analysis.

VASP Ab Initio Molecular Dynamics (AIMD)

Run Born-Oppenheimer molecular dynamics with DFT forces at each step. Expensive but provides finite-temperature behavior, diffusion coefficients, and reaction dynamics.

When to Use

Thermal stability — check if a structure is stable at operating temperature
Diffusion — compute diffusion coefficients (e.g., Li-ion conductors)
Reaction dynamics — observe bond breaking/forming at finite temperature
Free energy sampling — metadynamics or thermodynamic integration
Amorphous structures — melt-quench to generate amorphous phases

Basic NVT Molecular Dynamics

python

from catgo.workflow import Workflow
from catgo.workflow.builtins import geo_opt, md

wf = Workflow("AIMD at 600K")
struct = wf.add_task("structure_input", structure=structure_json)

# Optional: relax first
opt = wf.add_task(geo_opt, structure=struct.output.structure,
                  system_name="relax")

# NVT MD at 600 K
run = wf.add_task(md, structure=opt.output.structure,
                  IBRION=0,      # Molecular dynamics
                  NSW=5000,      # Number of MD steps
                  POTIM=1.0,     # Time step in fs
                  TEBEG=600,     # Starting temperature (K)
                  TEEND=600,     # Ending temperature (K)
                  SMASS=0,       # Nose-Hoover thermostat (NVT)
                  ISIF=2,        # Fix cell shape/volume
                  system_name="AIMD_600K")

wf.submit()

MCP Workflow

catgo_workflow_engine(action="create", params={"name": "AIMD 600K"})

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "structure_input",
  "structure": "<json>"
})

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "md",
  "software": "vasp",
  "structure": "{{t_001.output.structure}}",
  "NSW": 5000,
  "POTIM": 1.0,
  "TEBEG": 600,
  "TEEND": 600,
  "SMASS": 0,
  "system_name": "AIMD_600K"
})

catgo_workflow_engine(action="submit", params={"workflow_id": "wf_xxx"})

Key Parameters

Parameter	Default	Purpose
IBRION	0	Molecular dynamics mode
NSW	1000	Number of MD steps
POTIM	1.0	Time step in femtoseconds
TEBEG	300	Initial temperature (K)
TEEND	300	Final temperature (K). Set equal to TEBEG for isothermal
SMASS	-1	Thermostat: -1=NVE, 0=Nose-Hoover NVT, >0=Nose mass
ISIF	2	Fix cell (NVT). Use ISIF=3 for NPT (rare in AIMD)
NBLOCK	1	Write trajectory every NBLOCK steps

Thermostat Selection (SMASS)

SMASS	Ensemble	Use case
-1	NVE (microcanonical)	Energy conservation test, short dynamics
0	NVT Nose-Hoover	Standard production MD at fixed T
1-3	NVT with Nose mass	Larger SMASS = slower T coupling (less perturbation)
-3	Langevin thermostat	Better T control for small systems

Recommendation: Use SMASS=0 (Nose-Hoover) for most production runs. Use SMASS=-1 (NVE) for energy conservation checks and very short equilibration diagnostics.

Temperature Ramp (Heating/Cooling)

To heat from 300 K to 1500 K (simulated annealing or melt-quench):

python

run = wf.add_task(md, structure=s,
                  NSW=10000,
                  POTIM=2.0,
                  TEBEG=300,     # Start at 300 K
                  TEEND=1500,    # Ramp to 1500 K
                  SMASS=0,
                  system_name="heating_ramp")

Time Step Selection (POTIM)

System	Recommended POTIM (fs)	Reason
Heavy elements (Pt, Au, Ru)	2.0	Heavy atoms, slow dynamics
Oxides (TiO2, RuO2)	1.0-1.5	O is light, moderate step needed
Light elements (H, Li)	0.5-1.0	Fast H vibrations need small step
Proton transfer	0.5	H requires fine time resolution

Rule of thumb: if the total energy drifts upward in NVE, reduce POTIM.

Show full SKILL.md (182 more words)Show less

Performance Settings

AIMD is expensive. Optimize performance:

python

run = wf.add_task(md, structure=s,
                  NSW=5000,
                  POTIM=1.0,
                  TEBEG=600,
                  SMASS=0,
                  # Performance
                  ALGO="VeryFast",    # Fastest SCF convergence per step
                  NELM=60,            # Limit SCF steps (MD does not need tight SCF)
                  EDIFF=1e-4,         # Looser SCF for MD (still accurate forces)
                  LREAL="Auto",       # Real-space projection for speed
                  LWAVE=False,        # Do not write WAVECAR each step
                  NCORE=4,
                  system_name="AIMD")

EDIFF=1e-4 is acceptable for MD. Forces at 1e-4 SCF convergence are sufficiently accurate for MD trajectories. This saves 30-50% compute time vs 1e-5.

Supercell Size

AIMD requires large enough supercells to avoid finite-size effects:

Minimum: 64-100 atoms for bulk liquids/diffusion
Surfaces: use the slab supercell (typically 2x2 or 3x3 surface unit cell)
Small molecules on surface: existing slab supercell is usually fine

Build a supercell before MD:

catgo_structure(action="supercell", params={"scaling": [2, 2, 1]})

Output

The md task produces:

output.trajectory — atomic positions at each step (XDATCAR)
output.energy — energy vs time

Monitoring a Running MD

catgo_workflow_engine(action="status", params={"workflow_id": "wf_xxx"})
# Check NSW progress, temperature stability

catgo_analyze(action="convergence", params={"task_id": "t_md"})
# Energy vs step, temperature vs step

Troubleshooting

Problem	Fix
Temperature explodes	Reduce POTIM, check initial structure for overlapping atoms
Energy drift in NVE	Reduce POTIM, tighten EDIFF to 1e-5
SCF not converging at each step	Use ALGO=VeryFast, increase NELM
Too slow	Reduce ENCUT (400 eV ok for MD), use LREAL=Auto, fewer k-points
Atoms evaporating from slab	Add more vacuum, or constrain bottom layers

Typical Simulation Lengths

Purpose	NSW	POTIM	Total time
Quick stability check	1000	1.0	1 ps
Equilibration	5000	1.0	5 ps
Production (diffusion)	20000-50000	1.0-2.0	20-100 ps
Melt-quench	10000+	2.0	20+ ps

Files

Just SKILL.md in .claude/skills/vasp-md of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Vasp Md 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.

Vasp Md compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
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AstropyzLanqing/codex-claude-academic-skills	4.7k	13 repos	~2.9k	Automated safety check: Pass	BSD-3-Clause	4 mo ago
PymatgenzLanqing/codex-claude-academic-skills	4.7k	11 repos	~5k	Automated safety check: Pass	MIT	4 mo ago
Cantera Ignition DelayK-Dense-AI/scientific-agent-skills	48k	1 repos	~2.2k	Automated safety check: Pass	MIT	4 days ago
Weathertrpc-group/trpc-agent-go	1.9k	8 repos	~591	Automated safety check: Pass	Apache-2.0	today
Pymol VisualizationChatMol/ChatMol	373	—	~1.2k	Automated safety check: Pass	MIT	6 mo ago

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Questions about Vasp Md

What does Vasp Md do?

Ab initio molecular dynamics (AIMD) with VASP. An agent skill from Hello-QM/catgo-LRG. Vasp Md is an agent skill from Hello-QM/catgo-LRG. Ab initio molecular dynamics (AIMD) with VASP.

When should I use Vasp Md?

Vasp Md fits situations like: tasks that involve Physical and earth sciences.

How do I install Vasp Md in Claude Code?

Run `npx skills add Hello-QM/catgo-LRG --skill vasp-md -a claude-code`. Or copy the skill folder (.claude/skills/vasp-md in Hello-QM/catgo-LRG) into .claude/skills/vasp-md in your project. Claude Code loads it when a task matches its description.

How do I install Vasp Md in Codex?

Run `npx skills add Hello-QM/catgo-LRG --skill vasp-md -a codex`. Or copy the skill folder (.claude/skills/vasp-md in Hello-QM/catgo-LRG) into .agents/skills/vasp-md in your project. Codex loads it when a task matches its description.

Can I use Vasp Md 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 Hello-QM/catgo-LRG --skill vasp-md -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vasp-md, .gemini/skills/vasp-md, .github/skills/vasp-md and .opencode/skills/vasp-md in your project.

What does Vasp Md need to run?

SKILL.md names no scripts, command-line tools or credentials: Vasp Md is instructions for the agent only. Our summary lists: Python 3.

Does Vasp Md access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Vasp Md 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. Review the folder before installing.

What licence does Vasp Md use?

Vasp Md is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vasp Md use?

About 1.6k tokens (SKILL.md is roughly 6.4k 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 Vasp Md?

Skills that share tags, products or a category with Vasp Md: Astropy (zLanqing/codex-claude-academic-skills, 4.7k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.7k stars), Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars) and Weather (trpc-group/trpc-agent-go, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vasp Md?

Hello-QM (a GitHub user) maintains it in Hello-QM/catgo-LRG, which has 205 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on September 22, 2026.

Source: Hello-QM/catgo-LRG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Vasp Md

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

VASP Ab Initio Molecular Dynamics (AIMD)

When to Use

Basic NVT Molecular Dynamics

MCP Workflow

Key Parameters

Thermostat Selection (SMASS)

Temperature Ramp (Heating/Cooling)

Time Step Selection (POTIM)

Performance Settings

Supercell Size

Output

Monitoring a Running MD

Troubleshooting

Typical Simulation Lengths

Files

Compare with similar skills

Similar skills

More from Hello-QM/catgo-LRG

Related

Questions about Vasp Md