Agent skill

Gpaw

Generate and manage GPAW Python-based DFT calculations. An agent skill from Hello-QM/catgo-LRG.

AGPL-3.0Auto-check passed

Install Gpaw

skills CLI

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

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

GitHub CLI

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

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 gpaw -a codex

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

GitHub CLI

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

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

GitHub CLI

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

--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 gpaw -a gemini-cli

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

GitHub CLI

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

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 gpaw -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/gpaw .github/skills/gpaw && 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 "gpaw" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gpaw into .github/skills/gpaw/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpaw", 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 gpaw -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 gpaw --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/gpaw .opencode/skills/gpaw && 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 "gpaw" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/gpaw into .opencode/skills/gpaw/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gpaw", 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: gpaw
GitHub stars: 205
Token cost: ~1k tokens
SKILL.md length: 289 words
Files: 1
Skills in repo: 75
Repo updated: Sep 22, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

Generate and manage GPAW Python-based DFT calculations. An agent skill from Hello-QM/catgo-LRG.

Works in 3 steps: Verify structure → Create workflow → Add GPAW task via shell script
The user requests GPAW
SKILL.md covers When to Use, Prerequisites, Workflow Steps and Script Template — SCF, plus 4 more sections
Calls python

What it does

Gpaw is an agent skill from Hello-QM/catgo-LRG. Generate and manage GPAW Python-based DFT calculations. Use when the user requests GPAW, Python DFT, real-space grid DFT, or LCAO-DFT with ASE integration.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires GPAW and ASE installed in the Python environment on the HPC target. PAW datasets must be installed (gpaw install-data).

It works with Python. 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 requests GPAW
Real-space grid DFT
LCAO-DFT with ASE integration

Example prompts

“/gpaw”

Requirements

Python 3
Compatibility (from SKILL.md): Requires GPAW and ASE installed in the Python environment on the HPC target. PAW datasets must be installed (gpaw install-data).

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Verify structure
Create workflow
Add GPAW task via shell script

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
Shell commands in SKILL.md call:
- 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.
Compatibility
Requires GPAW and ASE installed in the Python environment on the HPC target. PAW datasets must be installed (gpaw install-data).
From compatibility in the SKILL.md frontmatter.

Context cost

Gpaw loads about 1k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 289 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~40
When it runs · the whole SKILL.md, loaded when a task matches: ~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); 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). 289 words, ~1,022 tokens.

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

name: gpaw
description: Generate and manage GPAW Python-based DFT calculations. Use when the user requests GPAW, Python DFT, real-space grid DFT, or LCAO-DFT with ASE integration.
compatibility: Requires GPAW and ASE installed in the Python environment on the HPC target. PAW datasets must be installed (gpaw install-data).

GPAW (Python DFT)

When to Use

User explicitly requests GPAW
User wants tight ASE integration (optimize with ASE, calculate with GPAW)
User needs real-space grid, LCAO, or plane-wave modes in a single code
User wants Python-scripted DFT workflows (no input files, pure Python)

Prerequisites

GPAW + ASE installed on HPC (gpaw --version, python -c "import gpaw")
PAW datasets installed (gpaw install-data)
Structure loaded in viewer — verify with catgo_view(action="get_state")

Workflow Steps

1. Verify structure

catgo_view(action="get_state")

2. Create workflow

catgo_workflow_engine(action="create", params={"name": "GPAW PBE relaxation"})

3. Add GPAW task via shell script

CatGo does not yet have a native GPAW engine. Use task_type: "shell" with a Python script.

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "gpaw_relax",
  "command": "python gpaw_relax.py",
  "input_files": {
    "gpaw_relax.py": "<script content>",
    "structure.json": "<pymatgen dict>"
  },
  "system_name": "TiO2_relax"
})

When a @register_engine("gpaw") is added to CatGo, use task_type: "geo_opt" with software: "gpaw" instead.

Script Template — SCF

python

from ase.io import read
from gpaw import GPAW, PW

atoms = read('structure.json')

calc = GPAW(
    mode=PW(500),            # Plane-wave mode, 500 eV cutoff
    xc='PBE',
    kpts={'density': 3.0},   # ~0.03 A^-1 k-point density
    txt='gpaw_scf.txt',
    occupations={'name': 'fermi-dirac', 'width': 0.05},
    convergence={'energy': 1e-5},
)

atoms.calc = calc
energy = atoms.get_potential_energy()
print(f'Total energy: {energy:.6f} eV')

Script Template — Relaxation

python

from ase.io import read, write
from ase.optimize import BFGS
from ase.constraints import FixAtoms
from gpaw import GPAW, PW

atoms = read('structure.json')

# Freeze bottom layers for slabs
c = FixAtoms(indices=[i for i, a in enumerate(atoms)
                      if a.position[2] < atoms.cell[2][2] * 0.4])
atoms.set_constraint(c)

calc = GPAW(
    mode=PW(500),
    xc='PBE',
    kpts={'density': 3.0},
    txt='gpaw_relax.txt',
    convergence={'energy': 1e-5},
)
atoms.calc = calc

opt = BFGS(atoms, trajectory='relax.traj', logfile='relax.log')
opt.run(fmax=0.02)

write('CONTCAR.vasp', atoms)

Parameter Guidance

Parameter	Typical value	Notes
mode	PW(500)	Plane-wave cutoff in eV; PW(600) for accurate forces
mode	LCAO(dzp)	LCAO mode for large systems (1000+ atoms)
xc	'PBE'	Also: 'RPBE', 'BEEF-vdW', 'mBEEF'
kpts	{'density': 3.0}	Auto k-mesh; higher = denser
convergence	{'energy': 1e-5}	In eV; tighten for phonon calcs
occupations	fermi-dirac, 0.05	Smearing width in eV
parallel	{'domain': 2, 'band': 2}	Domain decomposition for MPI

Calculation Modes

Mode	Best for	Speed
PW (plane-wave)	Accurate bulk/surface	Moderate
LCAO	Large systems, screening	Fast
FD (finite-difference)	Real-space, nanostructures	Slow but flexible

Common Pitfalls

Forgetting txt parameter — without it, GPAW writes no log and debugging is impossible
LCAO basis not installed — run gpaw install-data with --basis flag
Memory for large PW calculations — GPAW PW mode stores wavefunctions in memory; use LCAO for >500 atoms
No restart file — add calc.write('checkpoint.gpw') after SCF for restart capability
Parallel decomposition mismatch — domain * band * kpt must equal total MPI ranks
Slab k-points — use kpts={'size': (N, N, 1)} to avoid k-points along vacuum direction

Files

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

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Gpaw 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.

Gpaw compared with similar skills
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MCP Server Builderanthropics/skills	180k	63 repos	~2.3k	Automated safety check: Pass	Apache-2.0	today
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NotebookLM Research AssistantPleasePrompto/notebooklm-skill	7.8k	14 repos	~2.4k	Automated safety check: Notes	MIT	29 days ago
Manim Video Productionbrowser-use/video-use	28k	6 repos	~3k	Automated safety check: Pass	MIT	today
Code Review ChecklistshareAI-lab/learn-claude-code	78k	5 repos	~1.1k	Automated safety check: Pass	MIT	10 days ago

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Questions about Gpaw

What does Gpaw do?

Generate and manage GPAW Python-based DFT calculations. An agent skill from Hello-QM/catgo-LRG. Gpaw is an agent skill from Hello-QM/catgo-LRG. Generate and manage GPAW Python-based DFT calculations.

When should I use Gpaw?

Gpaw fits situations like: the user requests GPAW; real-space grid DFT; LCAO-DFT with ASE integration.

How do I install Gpaw in Claude Code?

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

How do I install Gpaw in Codex?

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

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

What does Gpaw need to run?

Going by SKILL.md and its folder, Gpaw needs the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires GPAW and ASE installed in the Python environment on the HPC target. PAW datasets must be installed (gpaw install-data). .

Does Gpaw 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 Gpaw 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 Gpaw use?

Gpaw 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 Gpaw use?

About 1k tokens (SKILL.md is roughly 4.1k 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 Gpaw?

Skills that share tags, products or a category with Gpaw: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gpaw?

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 Gpaw

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

Compatibility

Context cost

Safety

SKILL.md

GPAW (Python DFT)

When to Use

Prerequisites

Workflow Steps

1. Verify structure

2. Create workflow

3. Add GPAW task via shell script

Script Template — SCF

Script Template — Relaxation

Parameter Guidance

Calculation Modes

Common Pitfalls

Files

Compare with similar skills

Similar skills

More from Hello-QM/catgo-LRG

Related

Questions about Gpaw