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Agent skill

Vasp Static

by Hello-QM in Hello-QM/catgo-LRG

VASP single-point energy calculation. An agent skill from Hello-QM/catgo-LRG.

AGPL-3.0Auto-check passed

Install Vasp Static

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

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

GitHub CLI
$ gh skill install Hello-QM/catgo-LRG vasp-static --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-static .claude/skills/vasp-static && 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/

Facts

Skill name
vasp-static
GitHub stars
205
Token cost
~1.5k tokens
SKILL.md length
430 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
AGPL-3.0

At a glance

VASP single-point energy calculation. An agent skill from Hello-QM/catgo-LRG.

  • Works in 5 steps: After geometry optimization — get a… → Before DOS calculation — generate CHGCAR… → Before band structure — generate CHGCAR… → …
  • SKILL.md covers When to Use, Discussion Checkpoints, Basic Single Point and After Optimization, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Vasp Static is an agent skill from Hello-QM/catgo-LRG. VASP single-point energy calculation. Used standalone, as a pre-step for DOS/band structure, or to evaluate energy at a fixed geometry.

Its SKILL.md is about 1.5k 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

  • “/vasp-static”

Requirements

  • Python 3

Workflow steps

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

  1. After geometry optimization — get a precise energy at the relaxed geometry with tighter settings
  2. Before DOS calculation — generate CHGCAR with fine k-mesh for subsequent non-SCF DOS
  3. Before band structure — generate CHGCAR for non-SCF band calculation
  4. Convergence testing — test ENCUT, k-points, or other parameters at fixed geometry
  5. Energy evaluation — compare energies of different configurations without relaxing

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 Static loads about 1.5k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 430 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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). 430 words, ~1,504 tokens.

Download SKILL.mdSave it as .claude/skills/vasp-static/SKILL.md (or your agent's skills folder).
name
vasp-static
description
VASP single-point energy calculation. Used standalone, as a pre-step for DOS/band structure, or to evaluate energy at a fixed geometry.

VASP Single Point Calculation

Compute the total energy (and optionally charge density, wavefunction) at a fixed geometry. No ionic relaxation.

When to Use

  1. After geometry optimization — get a precise energy at the relaxed geometry with tighter settings
  2. Before DOS calculation — generate CHGCAR with fine k-mesh for subsequent non-SCF DOS
  3. Before band structure — generate CHGCAR for non-SCF band calculation
  4. Convergence testing — test ENCUT, k-points, or other parameters at fixed geometry
  5. Energy evaluation — compare energies of different configurations without relaxing

Discussion Checkpoints

🔴 Must discuss with user:

  • Functional consistency — must match the functional used in the preceding geo_opt; mixing PBE geometry with SCAN single point introduces systematic errors

🟡 Recommend confirming:

  • LORBIT (default: 11) — needed for projected DOS; set to 11 for per-atom orbital projections, omit if only total energy is needed
  • NEDOS (default: 3001) — increase for DOS analysis to resolve fine features; 301 is sufficient for energy-only calculations
  • ISMEAR — use -5 (tetrahedron) for DOS calculations, 0 (Gaussian) for general single points, 1 (Methfessel-Paxton) for metals
  • LCHARG / LWAVE — set True if this single point feeds into a subsequent DOS or band structure calculation

🟢 Safe defaults:

  • NSW = 0 (no ionic relaxation)
  • IBRION = -1 (no ionic optimizer)
  • EDIFF = 1E-5
  • ISMEAR = 0, SIGMA = 0.05

Basic Single Point

python
from catgo.workflow import Workflow
from catgo.workflow.builtins import geo_opt, single_point

wf = Workflow("Single point energy")
struct = wf.add_task("structure_input", structure=structure_json)
sp = wf.add_task(single_point, structure=struct.output.structure,
                 system_name="TiO2_SP")
wf.submit()

MCP equivalent:

catgo_workflow_engine(action="create", params={"name": "Single point"})

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "single_point",
  "software": "vasp",
  "structure": "<json>",
  "system_name": "TiO2_SP"
})

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

After Optimization

Chain a single point after relaxation for a more accurate energy:

python
opt = wf.add_task(geo_opt, structure=struct.output.structure,
                  ISIF=2, system_name="relax")
sp = wf.add_task(single_point, structure=opt.output.structure,
                 ENCUT=600,     # Higher cutoff for precise energy
                 EDIFF=1e-6,    # Tighter SCF convergence
                 system_name="SP_precise")

Pre-DOS Single Point

Generate a converged charge density with a fine k-mesh for subsequent DOS:

python
sp = wf.add_task(single_point, structure=opt.output.structure,
                 LCHARG=True,    # Write CHGCAR (needed for DOS)
                 LWAVE=True,     # Write WAVECAR (optional, speeds up DOS)
                 ISMEAR=-5,      # Tetrahedron method (accurate DOS)
                 NEDOS=3001,     # Dense energy grid
                 EDIFF=1e-6,     # Tight convergence
                 system_name="SP_for_DOS")

Pre-Band Structure Single Point

Generate CHGCAR for non-SCF band calculation:

python
sp = wf.add_task(single_point, structure=opt.output.structure,
                 LCHARG=True,    # Write CHGCAR
                 ICHARG=2,       # Self-consistent (generate charge)
                 system_name="SP_for_bands")

Convergence Testing

Test multiple ENCUT values at a fixed geometry:

python
wf = Workflow("ENCUT convergence")
struct = wf.add_task("structure_input", structure=structure_json)

for encut in [400, 450, 500, 550, 600]:
    wf.add_task(single_point, structure=struct.output.structure,
                ENCUT=encut, system_name=f"ENCUT={encut}")

wf.submit()

MCP equivalent:

catgo_workflow_engine(action="create", params={"name": "ENCUT convergence"})

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

# Repeat for each ENCUT value
catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "single_point",
  "software": "vasp",
  "structure": "{{t_001.output.structure}}",
  "ENCUT": 400,
  "system_name": "ENCUT=400"
})
# ... repeat for 450, 500, 550, 600
Show full SKILL.md (173 more words)Show less

Key Parameters

ParameterDefaultPurpose
NSW0No ionic steps (fixed geometry)
IBRION-1No ionic optimizer
EDIFF1e-5SCF convergence (use 1e-6 for precise energy)
NEDOS3001Number of DOS points (increase for DOS calculations)
LCHARGFalseWrite CHGCAR (set True for DOS/band pre-calc)
LWAVEFalseWrite WAVECAR (set True to restart from wavefunction)
ISMEAR0Gaussian smearing (use -5 for DOS with tetrahedron)
LORBIT11Write projected DOS (DOSCAR with per-atom projections)

ISMEAR Guidance

System typeISMEARSIGMANotes
Insulator/semiconductor00.05Gaussian smearing (default)
Metal10.2Methfessel-Paxton
DOS calculation-5N/ATetrahedron with Blochl corrections
Molecule in box00.01Small sigma, Gamma-only

Rule: ISMEAR=-5 requires at least 3 k-points per direction. Do not use for Gamma-only calculations.

Output

The single_point task produces:

  • output.energy — total DFT energy in eV
  • output.structure — the (unchanged) input structure

Troubleshooting

ProblemFix
SCF not convergingTry ALGO=All, increase NELM=400, or AMIX=0.1
Negative NBANDS warningIncrease NBANDS explicitly
Memory errorReduce NCORE, or increase node count
Wrong energy for magnetic systemSet ISPIN=2, provide MAGMOM

© Hello-QM, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

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

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Vasp Static 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 Static compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vasp Static this skillHello-QM/catgo-LRG205—~1.5kAutomated safety check: PassAGPL-3.0
Energy Calculatorbenchflow-ai/skillsbench1.8k—~437Automated safety check: PassApache-2.0
Bio Free Energy CalculationsGPTomics/bioSkills1.2k1 repos~4.5kAutomated safety check: PassMIT
Developing StandaloneTriliumNext/Trilium38k—~3.4kAutomated safety check: PassAGPL-3.0
Azure Static Web Appsgithub/awesome-copilot40k1 repos~2.4kAutomated safety check: PassMIT
Energy Procurementaffaan-m/ECC275k4 repos~7.4kAutomated safety check: PassApache-2.0

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Questions about Vasp Static

What does Vasp Static do?

VASP single-point energy calculation. An agent skill from Hello-QM/catgo-LRG. Vasp Static is an agent skill from Hello-QM/catgo-LRG. VASP single-point energy calculation.

How do I install Vasp Static in Claude Code?

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

How do I install Vasp Static in Codex?

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

Can I use Vasp Static 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-static -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-static, .gemini/skills/vasp-static, .github/skills/vasp-static and .opencode/skills/vasp-static in your project.

What does Vasp Static need to run?

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

Does Vasp Static 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 Static 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 Static use?

Vasp Static 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 Static use?

About 1.5k tokens (SKILL.md is roughly 6k 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 Static?

Skills that share tags, products or a category with Vasp Static: Energy Calculator (benchflow-ai/skillsbench, 1.8k stars), Bio Free Energy Calculations (GPTomics/bioSkills, 1.2k stars), Developing Standalone (TriliumNext/Trilium, 38k stars) and Azure Static Web Apps (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vasp Static?

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.