Ase

What it does

When your agent uses it

• Tasks that involve Physical and earth sciences
• Tasks that involve Mobile testing and debugging

Example prompts

• “/ase”

Requirements

• Python 3

Workflow steps

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

1. 1Structure Optimization
2. 2Electronic Structure Calculations
3. 3Molecular Properties
4. 4Phonon Analysis
5. 5Molecular Dynamics with Classical Force Fields

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. 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

  Ships 6 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • wiki.fysik.dtu.dk
  • openmopac.net
  • quantum-espresso.org

  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

Ase loads about 1.5k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 380 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~92

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

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); the scripts in this folder are not scanned.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its LGPL-3.0 licence (© lamm-mit). 380 words, ~1,466 tokens.

name

ase

description

Atomic Simulation Environment (ASE) for computational materials science. Perform DFT calculations, geometry optimization, band structure analysis, molecular property prediction, and periodic structure simulations. Supports VASP, MOPAC, Quantum ESPRESSO backends. For quick semi-empirical quantum chemistry, use mopac. For classical molecular dynamics, use openmm.

license

LGPL-3.0

metadata.skill-author

K-Dense Inc.

metadata.domain

computational-chemistry, materials-science

from ase import Atoms
from ase.optimize import BFGS
from ase.calculators.mopac import MOPAC

# Create structure
atoms = Atoms('H2O', positions=[[0, 0, 0], [1, 0, 0], [0, 1, 0]])

# Set calculator (semi-empirical quantum chemistry)
atoms.calc = MOPAC(method='PM6')

# Optimize geometry
dyn = BFGS(atoms)
dyn.run(fmax=0.01)

# Get optimized coordinates and energy
energy = atoms.get_potential_energy()
forces = atoms.get_forces()

from ase.build import bulk
from ase.calculators.mopac import MOPAC

# Create periodic structure (bulk silicon)
atoms = bulk('Si', 'diamond', a=5.4)

# Calculate band structure at high-symmetry k-points
atoms.calc = MOPAC(method='PM6-D3H4X')

# Get DOS at different energy levels
from ase.dft.band_structure import calculate_band_structure

# Geometry-optimized properties
- Dipole moment
- Polarizability
- Band gap (for semiconductors)
- Formation energy (for compounds)
- Cohesive energy (for crystals)

from ase.phonons import Phonons

# Create phonon object
phonons = Phonons(atoms, MOPAC_calc, supercell=(2, 2, 2))
phonons.run()

# Get phonon frequencies and DOS
phonon_frequencies = phonons.get_frequencies()

from ase.md.verlet import VelocityVerlet
from ase.md.langevin import Langevin
from ase import units

# NVT ensemble (constant T)
dyn = Langevin(atoms, timestep=1*units.fs, temperature_K=300, friction=0.02)

# Run for specified timesteps
for i in range(1000):
    dyn.run(1)

# 1. Optimize molecular structure
python ase_optimize.py --smiles "CCO" --method PM6

# 2. Calculate properties
python ase_properties.py --structure optimized.xyz

# 3. Run MD simulation
python ase_md.py --structure optimized.xyz --temperature 300 --timesteps 10000

# 4. Analyze phonons (materials)
python ase_phonons.py --structure crystal.xyz --supercell "2 2 2"

© lamm-mit, LGPL-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

SKILL.md and 6 other files (scripts) in skills/ase of lamm-mit/scienceclaw.

• SKILL.md
• scripts/__pycache__/ase_optimize.cpython-313.pyc
• scripts/__pycache__/ase_properties.cpython-313.pyc
• scripts/__pycache__/demo.cpython-313.pyc
• scripts/ase_optimize.py
• scripts/ase_properties.py
• scripts/demo.py

Open the folder on GitHubat commit ab9aba1