Agent skill

Cp2k Geo Opt

CP2K geometry optimization. An agent skill from Hello-QM/catgo-LRG.

AGPL-3.0Auto-check passed

Install Cp2k Geo Opt

skills CLI

$ npx skills add Hello-QM/catgo-LRG --skill cp2k-geo-opt -a claude-code

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG cp2k-geo-opt --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/cp2k-relax .claude/skills/cp2k-geo-opt && 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 "cp2k-geo-opt" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/cp2k-relax into .claude/skills/cp2k-geo-opt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cp2k-geo-opt", 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/cp2k-relax

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 cp2k-geo-opt -a codex

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG cp2k-geo-opt --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/cp2k-relax .agents/skills/cp2k-geo-opt && 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 "cp2k-geo-opt" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/cp2k-relax into .agents/skills/cp2k-geo-opt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cp2k-geo-opt", 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 cp2k-geo-opt -a cursor

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG cp2k-geo-opt --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/cp2k-relax .cursor/skills/cp2k-geo-opt && 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 "cp2k-geo-opt" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/cp2k-relax into .cursor/skills/cp2k-geo-opt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cp2k-geo-opt", 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/cp2k-relax

--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 cp2k-geo-opt -a gemini-cli

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

GitHub CLI

$ gh skill install Hello-QM/catgo-LRG cp2k-geo-opt --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/cp2k-relax .gemini/skills/cp2k-geo-opt && 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 "cp2k-geo-opt" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/cp2k-relax into .gemini/skills/cp2k-geo-opt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cp2k-geo-opt", 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 cp2k-geo-opt

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 cp2k-geo-opt -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/cp2k-relax .github/skills/cp2k-geo-opt && 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 "cp2k-geo-opt" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/cp2k-relax into .github/skills/cp2k-geo-opt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cp2k-geo-opt", 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 cp2k-geo-opt -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 cp2k-geo-opt --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/cp2k-relax .opencode/skills/cp2k-geo-opt && 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 "cp2k-geo-opt" agent skill from https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/cp2k-relax into .opencode/skills/cp2k-geo-opt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cp2k-geo-opt", 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: cp2k-geo-opt
GitHub stars: 205
Token cost: ~1.8k tokens
SKILL.md length: 383 words
Files: 1
Skills in repo: 75
Repo updated: Sep 22, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

CP2K geometry optimization. An agent skill from Hello-QM/catgo-LRG.

SKILL.md covers Scenario 1: Bulk Optimization, Scenario 2: Slab Optimization, Scenario 3: Adsorbate on Slab and Scenario 4: Large System (500+…, plus 6 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cp2k Geo Opt is an agent skill from Hello-QM/catgo-LRG. CP2K geometry optimization. Handles bulk, slab, and molecular systems with GPW method. Efficient for large systems (200+ atoms).

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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.

Example prompts

“/cp2k-geo-opt”

Requirements

Python 3

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

Cp2k Geo Opt loads about 1.8k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 383 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~35
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k

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). 383 words, ~1,802 tokens.

Download SKILL.mdSave it as .claude/skills/cp2k-geo-opt/SKILL.md (or your agent's skills folder).

name: cp2k-geo-opt
description: CP2K geometry optimization. Handles bulk, slab, and molecular systems with GPW method. Efficient for large systems (200+ atoms).

CP2K Geometry Optimization

Set up and submit CP2K geometry optimizations using the Gaussian and Plane-Wave (GPW) method. CP2K is the preferred code for systems larger than ~200 atoms where VASP becomes memory-limited.

Scenario 1: Bulk Optimization

Full cell and ionic relaxation for periodic bulk systems.

python

from catgo.workflow import Workflow

wf = Workflow("CP2K bulk MgO")
struct = wf.add_task("structure_input", structure=bulk_json)

opt = wf.add_task("geo_opt",
                  structure=struct.output.structure,
                  software="cp2k",
                  cell_opt=True,          # Relax cell + ions (like ISIF=3 in VASP)
                  cutoff=600,             # Ry
                  rel_cutoff=60,          # Ry
                  basis_set="DZVP-MOLOPT-SR-GTH",
                  xc_functional="PBE",
                  max_iter=200,           # Max geo_opt steps
                  eps_geo=3e-4,           # Force convergence (Hartree/Bohr)
                  system_name="bulk_MgO")

wf.submit()

MCP equivalent:

catgo_workflow_v2(action="create", params={"name": "CP2K bulk MgO"})

catgo_workflow_v2(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "structure_input",
  "structure": "<bulk_json>"
})

catgo_workflow_v2(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "geo_opt",
  "software": "cp2k",
  "structure": "{{t_001.output.structure}}",
  "cell_opt": true,
  "cutoff": 600,
  "basis_set": "DZVP-MOLOPT-SR-GTH",
  "system_name": "bulk_MgO"
})

catgo_workflow_v2(action="submit", params={"workflow_id": "wf_xxx"})

Scenario 2: Slab Optimization

Fixed cell, ionic relaxation with frozen bottom layers. Analogous to VASP ISIF=2.

python

wf = Workflow("CP2K TiO2 slab")
struct = wf.add_task("structure_input", structure=slab_json)

opt = wf.add_task("geo_opt",
                  structure=struct.output.structure,
                  software="cp2k",
                  cell_opt=False,         # Fix cell (slab)
                  cutoff=600,
                  basis_set="DZVP-MOLOPT-SR-GTH",
                  freeze_layers=2,        # Freeze bottom 2 layers
                  poisson_solver="MT",    # Martyna-Tuckerman for slab geometry
                  system_name="TiO2_slab")

wf.submit()

Slab-specific settings:

cell_opt=False — mandatory for slabs (equivalent to ISIF=2 in VASP)
freeze_layers=2 — freeze bottom layers to mimic bulk
poisson_solver="MT" — Martyna-Tuckerman solver handles the vacuum correctly for 2D-periodic systems. Use "PERIODIC" for bulk (3D-periodic) and "MT" or "WAVELET" for slabs

Scenario 3: Adsorbate on Slab

Same as slab, with adsorbate atoms free to relax:

python

opt = wf.add_task("geo_opt",
                  structure=adsorbate_slab_json,
                  software="cp2k",
                  cell_opt=False,
                  freeze_layers=2,
                  cutoff=600,
                  vdw_method="DFTD3",    # Dispersion for adsorption
                  poisson_solver="MT",
                  system_name="*OH_on_TiO2")

Scenario 4: Large System (500+ atoms)

CP2K's GPW method with OT (Orbital Transformation) SCF solver scales linearly for large systems:

python

opt = wf.add_task("geo_opt",
                  structure=large_system_json,
                  software="cp2k",
                  cutoff=400,                   # Lower cutoff acceptable for screening
                  basis_set="SZV-MOLOPT-SR-GTH", # Minimal basis for speed
                  ot_minimizer="DIIS",           # OT method for large systems
                  ot_preconditioner="FULL_ALL",
                  eps_scf=1e-5,
                  system_name="large_system")

OT vs diagonalization:

OT: O(N) scaling, no HOMO-LUMO gap requirement, default for > 100 atoms
Diagonalization: O(N^3) scaling, needed for metallic systems (zero gap)

For metals: OT does not work for metallic systems (zero band gap). Use Fermi-Dirac smearing with diagonalization:

python

opt = wf.add_task("geo_opt",
                  structure=metal_json,
                  software="cp2k",
                  scf_method="diag",          # Standard diagonalization
                  smearing_method="FERMI_DIRAC",
                  electronic_temperature=300,  # K
                  system_name="metal")

Key Parameters

Parameter	Default	Purpose
cutoff	600 Ry	PW cutoff for density grid
rel_cutoff	60 Ry	Multi-grid relative cutoff
basis_set	DZVP-MOLOPT-SR-GTH	Gaussian basis set
xc_functional	PBE	Exchange-correlation functional
max_iter	200	Max geometry optimization steps
eps_geo	3e-4	Force convergence (Hartree/Bohr, ~ 0.015 eV/A)
eps_scf	1e-6	SCF convergence (Hartree)
cell_opt	False	Whether to optimize cell parameters
freeze_layers	0	Number of bottom layers to freeze
vdw_method	None	Dispersion correction ("DFTD3", "DFTD3(BJ)")
poisson_solver	PERIODIC	Poisson solver ("PERIODIC", "MT", "WAVELET")

Show full SKILL.md (133 more words)Show less

Convergence Monitoring

catgo_workflow_v2(action="status", params={"workflow_id": "wf_xxx"})

catgo_analyze(action="convergence", params={"task_id": "t_opt"})
# Returns: energy vs step, max force vs step

catgo_analyze(action="forces", params={"task_id": "t_opt"})

Chain: CP2K Optimization then Frequency

python

wf = Workflow("CP2K opt + freq")
struct = wf.add_task("structure_input", structure=slab_oh_json)

opt = wf.add_task("geo_opt", structure=struct.output.structure,
                  software="cp2k", freeze_layers=2,
                  system_name="*OH")

frq = wf.add_task("freq", structure=opt.output.structure,
                  software="cp2k",
                  freeze_mode="layers", freeze_layers=4,
                  system_name="*OH")

gib = wf.add_task("gibbs_energy",
                  energy=opt.output.energy,
                  frequencies=frq.output.frequencies,
                  phase="adsorbed", system_name="*OH")

wf.submit()

Output

The geo_opt task produces:

output.structure — optimized structure (pymatgen dict as JSON string)
output.energy — total DFT energy in eV

Troubleshooting

Problem	Fix
SCF not converging	Use OT method, increase scf_max_iter, reduce mixing
Energy oscillations	Increase cutoff (try 800 Ry), check rel_cutoff
Forces not converging	Loosen eps_geo, increase max_iter
OT fails for metal	Switch to diagonalization with Fermi smearing
Memory error	Reduce cutoff, use SZV basis, increase nodes
Missing basis for element	Check CP2K basis set library, may need to download
Poisson solver error for slab	Use poisson_solver="MT" instead of "PERIODIC"

Unit Conversions

CP2K uses atomic units internally. CatGo converts automatically, but for reference:

1 Hartree = 27.2114 eV
1 Bohr = 0.529177 A
Force: 1 Ha/Bohr = 51.422 eV/A
eps_geo=3e-4 Ha/Bohr corresponds to ~0.015 eV/A (comparable to VASP EDIFFG=-0.02)

Files

Just SKILL.md in .claude/skills/cp2k-relax of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Cp2k Geo Opt 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.

Cp2k Geo Opt compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Cp2k Geo Opt this skillHello-QM/catgo-LRG	205	—	~1.8k	Automated safety check: Pass	AGPL-3.0	18 days ago
Error Handlingaffaan-m/ECC	276k	1 repos	~2.7k	Automated safety check: Pass	MIT	yesterday
Error Handlingthedaviddias/Front-End-Checklist	74k	—	~416	Automated safety check: Pass	MIT	4 days ago
Error Handlingaffaan-m/ECC	276k	—	~2.4k	Automated safety check: Pass	MIT	yesterday
Python Error Handlingwshobson/agents	40k	—	~1.5k	Automated safety check: Pass	MIT	6 days ago
Geo Meta Tags Auditthedaviddias/Front-End-Checklist	74k	—	~763	Automated safety check: Pass	MIT	4 days ago

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Questions about Cp2k Geo Opt

What does Cp2k Geo Opt do?

CP2K geometry optimization. An agent skill from Hello-QM/catgo-LRG. Cp2k Geo Opt is an agent skill from Hello-QM/catgo-LRG. CP2K geometry optimization.

How do I install Cp2k Geo Opt in Claude Code?

Run `npx skills add Hello-QM/catgo-LRG --skill cp2k-geo-opt -a claude-code`. Or copy the skill folder (.claude/skills/cp2k-relax in Hello-QM/catgo-LRG) into .claude/skills/cp2k-geo-opt in your project. Claude Code loads it when a task matches its description.

How do I install Cp2k Geo Opt in Codex?

Run `npx skills add Hello-QM/catgo-LRG --skill cp2k-geo-opt -a codex`. Or copy the skill folder (.claude/skills/cp2k-relax in Hello-QM/catgo-LRG) into .agents/skills/cp2k-geo-opt in your project. Codex loads it when a task matches its description.

Can I use Cp2k Geo Opt 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 cp2k-geo-opt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cp2k-geo-opt, .gemini/skills/cp2k-geo-opt, .github/skills/cp2k-geo-opt and .opencode/skills/cp2k-geo-opt in your project.

What does Cp2k Geo Opt need to run?

SKILL.md names no scripts, command-line tools or credentials: Cp2k Geo Opt is instructions for the agent only. Our summary lists: Python 3.

Does Cp2k Geo Opt 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 Cp2k Geo Opt 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 Cp2k Geo Opt use?

Cp2k Geo Opt 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 Cp2k Geo Opt use?

About 1.8k tokens (SKILL.md is roughly 7.2k 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 Cp2k Geo Opt?

Skills that share tags, products or a category with Cp2k Geo Opt: Error Handling (affaan-m/ECC, 276k stars), Error Handling (thedaviddias/Front-End-Checklist, 74k stars), Error Handling (affaan-m/ECC, 276k stars) and Python Error Handling (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cp2k Geo Opt?

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 Cp2k Geo Opt

Facts

At a glance

What it does

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

CP2K Geometry Optimization

Scenario 1: Bulk Optimization

Scenario 2: Slab Optimization

Scenario 3: Adsorbate on Slab

Scenario 4: Large System (500+ atoms)

Key Parameters

Convergence Monitoring

Chain: CP2K Optimization then Frequency

Output

Troubleshooting

Unit Conversions

Files

Compare with similar skills

Similar skills

More from Hello-QM/catgo-LRG

Questions about Cp2k Geo Opt