Agent skill

Computational Input

AGPL-3.0Auto-check passedMobile

Install Computational Input

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill computational-input -a claude-code

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG computational-input --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/catbot-plugin/skills/computational-input .claude/skills/computational-input && 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 "computational-input" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/catbot-plugin/skills/computational-input into .claude/skills/computational-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computational-input", 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/catbot-plugin/skills/computational-input

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 computational-input -a codex

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG computational-input --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/catbot-plugin/skills/computational-input .agents/skills/computational-input && 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 "computational-input" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/catbot-plugin/skills/computational-input into .agents/skills/computational-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computational-input", 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 computational-input -a cursor

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG computational-input --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/catbot-plugin/skills/computational-input .cursor/skills/computational-input && 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 "computational-input" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/catbot-plugin/skills/computational-input into .cursor/skills/computational-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computational-input", 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 catbot-plugin/skills/computational-input

--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 computational-input -a gemini-cli

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG computational-input --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/catbot-plugin/skills/computational-input .gemini/skills/computational-input && 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 "computational-input" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/catbot-plugin/skills/computational-input into .gemini/skills/computational-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computational-input", 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 computational-input

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 computational-input -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/catbot-plugin/skills/computational-input .github/skills/computational-input && 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 "computational-input" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/catbot-plugin/skills/computational-input into .github/skills/computational-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computational-input", 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 computational-input -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 computational-input --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/catbot-plugin/skills/computational-input .opencode/skills/computational-input && 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 "computational-input" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/catbot-plugin/skills/computational-input into .opencode/skills/computational-input/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computational-input", 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: computational-input
GitHub stars: 205
Token cost: ~768 tokens
SKILL.md length: 224 words
Files: 1
Skills in repo: 75
Repo updated: Sep 22, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

The user asks to generate DFT input files (VASP
SKILL.md covers Quick Decision Guide, VASP Input, Quantum ESPRESSO Input and LAMMPS Input, plus 2 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Quantum ESPRESSO

What it does

Computational Input is an agent skill from Hello-QM/catgo-LRG. Use when the user asks to generate DFT input files (VASP, Quantum ESPRESSO, LAMMPS), optimize structures with ML potentials (MACE, CHGNet, M3GNet), compute energy, or set up any computational chemistry calculation.

Its SKILL.md is about 770 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 Mobile, covering Mobile testing and debugging. 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

The user asks to generate DFT input files (VASP
Quantum ESPRESSO
Optimize structures with ML potentials (MACE
Set up any computational chemistry calculation

Example prompts

“/computational-input”

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 json).
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

Computational Input loads about 768 tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 224 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~59
When it runs · the whole SKILL.md, loaded when a task matches: ~768

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). 224 words, ~768 tokens.

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

name: computational-input
description: Use when the user asks to generate DFT input files (VASP, Quantum ESPRESSO, LAMMPS), optimize structures with ML potentials (MACE, CHGNet, M3GNet), compute energy, or set up any computational chemistry calculation.

Computational Input Generation

Quick Decision Guide

Task	Tool
VASP input (INCAR/POSCAR/KPOINTS)	`catgo_vasp_generate`
Quantum ESPRESSO pw.x input	`catgo_qe_generate`
LAMMPS input + data file	`catgo_lammps_generate`
Multi-stage LAMMPS simulation	`catgo_lammps_sequential`
ML potential relaxation	`catgo_optimize`
Single-point energy/forces	`catgo_energy`
List available calculators	`catgo_calculators`

VASP Input

`catgo_vasp_generate`

Calculation types: opt, scf, freq, bader, dos, ddec, elf
Key params: encut (default 450 eV), gga ("PE"=PBE), ispin (2=spin-polarized), ivdw (12=D3-BJ), kspacing, fixed_indices/fixed_z_below

Common patterns:

Bulk optimization: calculation_type="opt", isif=3 (relax cell+ions)
Slab optimization: calculation_type="opt", isif=2, fixed_z_below=Z
DOS: calculation_type="dos", dense k-mesh

Call catgo_vasp_calc_types to list all available types with defaults.

Quantum ESPRESSO Input

`catgo_qe_generate`

Calculation types: scf, relax, vc-relax, nscf, bands
Key params: ecutwfc (default 60 Ry), ecutrho (default 480 Ry), kspacing, occupations, smearing ("mv"=Marzari-Vanderbilt), nspin

Call catgo_qe_templates for recommended settings per calculation type.

LAMMPS Input

`catgo_lammps_generate`

Simulation types: minimize, nve, nvt, npt
Key params: pair_style, pair_coeff, potential_file, temperature, pressure

`catgo_lammps_sequential` — Multi-stage MD protocol:

json

{"stages": [
  {"name": "minimize", "simulation_type": "minimize"},
  {"name": "heat", "simulation_type": "nvt", "temperature": 300, "run_steps": 10000},
  {"name": "equilibrate", "simulation_type": "npt", "temperature": 300, "run_steps": 50000},
  {"name": "production", "simulation_type": "nvt", "temperature": 300, "run_steps": 100000}
]}

Call catgo_lammps_pair_styles for available force fields. Call catgo_lammps_validate before generating to check configuration.

ML Potential Optimization

`catgo_optimize`

Quick relaxation using ML interatomic potentials:

mace: Best accuracy for most systems
chgnet: Good for oxides
m3gnet: General purpose
emt: Fast, metals only (testing)

Params: fmax (default 0.05 eV/A), max_steps (200), relax_cell (True to relax lattice)

`catgo_energy` — Single-point energy + forces without optimization.

Workflow Recipes

ML Pre-Optimization then DFT

catgo_optimize(calculator="mace", fmax=0.05) → 2. catgo_vasp_generate(calculation_type="opt")

VASP Slab Calculation

Build slab → 2. catgo_vasp_generate(calculation_type="opt", isif=2, fixed_z_below=Z, encut=520)

Files

Just SKILL.md in catbot-plugin/skills/computational-input of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Computational Input 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.

Computational Input compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Computational Input this skillHello-QM/catgo-LRG	205	—	~768	Automated safety check: Pass	AGPL-3.0	15 days ago
Orca iOS Simulator Controlstablyai/orca	87k	1 repos	~584	Automated safety check: Pass	Apache-2.0	today
UI Kitten Showcase QAakveo/react-native-ui-kitten	11k	—	~2.3k	Automated safety check: Pass	MIT	2 days ago
Apple Crash Log .NET Symbolicationdotnet/skills	5.6k	1 repos	~2.4k	Automated safety check: Pass	MIT	today
Mobilerun Docs Referencedroidrun/mobilerun	9.6k	—	~943	Automated safety check: Pass	MIT	2 days ago
VPhone Guest ControlLakr233/vphone-cli	15k	—	~1.6k	Automated safety check: Pass	MIT	today

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Questions about Computational Input

What does Computational Input do?

A skill your agent uses when the user asks to generate DFT input files (VASP, Quantum ESPRESSO, LAMMPS), optimize structures with ML potentials (MACE, CHGNet, M3GNet), compute energy, or set up any…. Computational Input is an agent skill from Hello-QM/catgo-LRG. Use when the user asks to generate DFT input files (VASP, Quantum ESPRESSO, LAMMPS), optimize structures with ML potentials (MACE, CHGNet, M3GNet), compute energy, or set up any computational chemistry calculation.

When should I use Computational Input?

Computational Input fits situations like: the user asks to generate DFT input files (VASP; quantum ESPRESSO; optimize structures with ML potentials (MACE; set up any computational chemistry calculation.

How do I install Computational Input in Claude Code?

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

How do I install Computational Input in Codex?

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

Can I use Computational Input 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 computational-input -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/computational-input, .gemini/skills/computational-input, .github/skills/computational-input and .opencode/skills/computational-input in your project.

What does Computational Input need to run?

SKILL.md names no scripts, command-line tools or credentials: Computational Input is instructions for the agent only.

Does Computational Input 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 Computational Input 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 Computational Input use?

Computational Input 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 Computational Input use?

About 768 tokens (SKILL.md is roughly 3.1k 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 Computational Input?

Skills that share tags, products or a category with Computational Input: Orca iOS Simulator Control (stablyai/orca, 87k stars), UI Kitten Showcase QA (akveo/react-native-ui-kitten, 11k stars), Apple Crash Log .NET Symbolication (dotnet/skills, 5.6k stars) and Mobilerun Docs Reference (droidrun/mobilerun, 9.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Computational Input?

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 Computational Input

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Computational Input Generation

Quick Decision Guide

VASP Input

catgo_vasp_generate

Quantum ESPRESSO Input

catgo_qe_generate

LAMMPS Input

catgo_lammps_generate

catgo_lammps_sequential — Multi-stage MD protocol:

ML Potential Optimization

catgo_optimize

catgo_energy — Single-point energy + forces without optimization.

Workflow Recipes

ML Pre-Optimization then DFT

VASP Slab Calculation

Files

Compare with similar skills

Similar skills

More from Hello-QM/catgo-LRG

Related

Questions about Computational Input

`catgo_vasp_generate`

`catgo_qe_generate`

`catgo_lammps_generate`

`catgo_lammps_sequential` — Multi-stage MD protocol:

`catgo_optimize`

`catgo_energy` — Single-point energy + forces without optimization.