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Agent skill

Openmm

by lamm-mit in lamm-mit/scienceclaw

OpenMM molecular dynamics engine for protein and ligand simulations.

MITAuto-check passedResearch & Science

Install Openmm

skills CLI
$ npx skills add lamm-mit/scienceclaw --skill openmm -a claude-code

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

GitHub CLI
$ gh skill install lamm-mit/scienceclaw openmm --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/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/openmm .claude/skills/openmm && 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
openmm
GitHub stars
244
Token cost
~1.5k tokens
SKILL.md length
384 words
Files
3 (incl. scripts)
Skills in repo
85
Repo updated
First seen
Licence
MIT

At a glance

OpenMM molecular dynamics engine for protein and ligand simulations.

  • Works in 5 steps: Protein Dynamics Simulation → Ligand Binding Exploration → Conformational Sampling → …
  • Tasks that involve Physical and earth sciences
  • SKILL.md covers Overview, Core Capabilities, Available Force Fields and Use Cases, plus 5 more sections
  • Runs Python scripts from its folder; calls python

What it does

Openmm is an agent skill from lamm-mit/scienceclaw. OpenMM molecular dynamics engine for protein and ligand simulations. Run NVE/NVT/NPT ensembles, compute free energies, analyze dynamics. Supports AMBER, CHARMM, OPLS force fields and GPU acceleration. For classical MD with periodic systems, use ase. For quick quantum chemistry, use mopac.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/openmm_md.py`).

It sits in Research & Science, covering Physical and earth sciences. The licence is MIT.

When your agent uses it

  • Tasks that involve Physical and earth sciences

Example prompts

  • “/openmm”

Requirements

  • Python 3

Workflow steps

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

  1. Protein Dynamics Simulation
  2. Ligand Binding Exploration
  3. Conformational Sampling
  4. Energy Minimization
  5. Analysis

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 2 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):

    • openmm.org
    • ambermd.org
    • charmm.org
    • github.com

    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

Openmm loads about 1.5k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 384 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
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); the scripts in this folder are not scanned.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its MIT licence (© lamm-mit). 384 words, ~1,524 tokens.

Download SKILL.mdSave it as .claude/skills/openmm/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
openmm
description
OpenMM molecular dynamics engine for protein and ligand simulations. Run NVE/NVT/NPT ensembles, compute free energies, analyze dynamics. Supports AMBER, CHARMM, OPLS force fields and GPU acceleration. For classical MD with periodic systems, use ase. For quick quantum chemistry, use mopac.
license
MIT
metadata.skill-author
K-Dense Inc.
metadata.domain
computational-chemistry, biomolecular-simulation

OpenMM Molecular Dynamics Engine

Overview

OpenMM is a toolkit for molecular simulation, particularly suited for biomolecular systems (proteins, ligands, membranes). This skill provides computational investigation capabilities for protein dynamics, ligand binding exploration, conformational sampling, and free energy calculations. OpenMM supports GPU acceleration for high-performance simulations and multiple force fields (AMBER, CHARMM, OPLS).

Core Capabilities

1. Protein Dynamics Simulation

Basic MD Run:

Simulate protein motion in explicit solvent:

python
from openmm import *
from openmm.app import *
from openmm.unit import *

# Load structure
pdb = PDBFile('protein.pdb')

# Create force field and system
forcefield = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = forcefield.createSystem(pdb.topology, nonbondedMethod=PME)

# Create integrator (NVT ensemble)
integrator = LangevinIntegrator(300*kelvin, 1/picosecond, 2*femtoseconds)

# Run simulation
simulation = Simulation(pdb.topology, system, integrator)
simulation.context.setPositions(pdb.positions)
simulation.minimizeEnergy()
simulation.reporters.append(PDBReporter('trajectory.pdb', 1000))
simulation.step(100000)  # 200 ps

Key Ensembles:

  • NVE: Constant volume, constant energy (microcanonical)
  • NVT: Constant volume, constant temperature (Langevin/Nose-Hoover)
  • NPT: Constant pressure, constant temperature (isothermal-isobaric)
2. Ligand Binding Exploration

Protein-Ligand Complex Dynamics:

Simulate ligand movement in protein binding pocket:

python
# Load complex (protein + ligand)
pdb = PDBFile('complex.pdb')

# Create system with AMBER FF
forcefield = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = forcefield.createSystem(
    pdb.topology,
    nonbondedMethod=PME,
    constraints=HBonds
)

# Run with restraints on protein (ligand free)
# Can use positional restraints to keep protein stable

Binding Free Energy (Alchemical):

Calculate free energy of ligand binding:

python
# Thermodynamic Integration (TI) or
# Free Energy Perturbation (FEP)
# Alchemically transform ligand from bound → unbound state
3. Conformational Sampling

Enhanced Sampling Techniques:

Explore conformational landscape:

python
# Replica Exchange Molecular Dynamics (REMD)
# Multiple replicas at different temperatures
# Exchanges improve sampling efficiency

# Or: Simulated annealing
# Gradual temperature reduction
4. Energy Minimization

Structure Optimization:

Relax structures before MD:

python
simulation.minimizeEnergy(maxIterations=1000)

Finds local energy minimum without dynamics.

5. Analysis

Extract Properties:

Compute from trajectories:

python
- Root-mean-square deviation (RMSD)
- Radius of gyration (Rg)
- Hydrogen bond occupancy
- Dihedral angles (phi/psi for proteins)
- Free energy differences
- Binding free energies (MM-PBSA)

Available Force Fields

Biomolecules
  • AMBER14: AMBER force field with TIP3P water
  • CHARMM36: CHARMM force field
  • OPLS: OPLS-AA force field
Water Models
  • TIP3P: 3-point, commonly used
  • TIP4P, TIP5P: Higher accuracy
  • OPC: Optimized charge-on-springy particle water
Ions
  • Standard monovalent ions (Na+, Cl-, K+)
  • Divalent ions (Ca2+, Mg2+)
  • Implicit solvent models (generalized Born)

Use Cases

Computational Drug Discovery:

  • Predict protein-ligand binding modes
  • Calculate binding free energies
  • Identify transient binding pockets
  • Screen multiple ligands

Protein Engineering:

  • Validate designed proteins
  • Predict stability (thermal stability from Tm calculations)
  • Explore conformational changes
  • Design allosteric mechanisms

Structural Biology:

  • Simulate protein-protein interactions
  • Model conformational ensembles
  • Compute flexibility maps
  • Validate X-ray/cryo-EM structures

Membrane Systems:

  • Lipid bilayer simulations
  • Protein-membrane interactions
  • Ion channel dynamics
  • Membrane protein folding
Show full SKILL.md (127 more words)Show less

Integration with Other Skills

Input:

  • Structures from pdb (X-ray structures)
  • Structures from ase (optimized geometries)
  • SMILES from pubchem (ligand structures)
  • Sequences from uniprot (homology modeling)

Output:

  • Trajectories for analysis
  • Binding affinities for comparison with tdc
  • Conformational ensembles for property prediction
  • Dynamics data for publication in arxiv

Performance Characteristics

Timescales:

  • Energy minimization: seconds
  • Short equilibration (10 ns): minutes
  • Microsecond-scale sampling: hours (with GPU)
  • Millisecond sampling: days-weeks

GPU Acceleration:

  • 50-100x speedup on NVIDIA GPUs (CUDA)
  • AMD GPUs supported (HIP)
  • CPU fallback available (slow)

Limitations

  • Requires high-performance hardware (GPU recommended)
  • Classical force fields have accuracy limits
  • Long timescales need multiple runs/ensemble averaging
  • Cannot include quantum effects (proton transfer, bond breaking)
  • Protein topology fixed during simulation

Example Workflow

bash
# 1. Prepare protein structure
python openmm_setup.py --pdb protein.pdb --force-field amber14

# 2. Run MD simulation
python openmm_md.py --structure prepared.pdb --temperature 300 \
    --ensemble nvt --duration 100  # ns

# 3. Analyze trajectory
python openmm_analysis.py --trajectory trajectory.dcd \
    --reference protein.pdb --metrics rmsd radius-of-gyration

References

© lamm-mit, MIT. 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 2 other files (scripts) in skills/openmm of lamm-mit/scienceclaw.

  • SKILL.md
  • scripts/__pycache__/openmm_md.cpython-313.pyc
  • scripts/openmm_md.py

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Openmm 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.

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Categories

Questions about Openmm

What does Openmm do?

OpenMM molecular dynamics engine for protein and ligand simulations. Openmm is an agent skill from lamm-mit/scienceclaw. OpenMM molecular dynamics engine for protein and ligand simulations.

When should I use Openmm?

Openmm fits situations like: tasks that involve Physical and earth sciences.

How do I install Openmm in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill openmm -a claude-code`. Or copy the skill folder (skills/openmm in lamm-mit/scienceclaw) into .claude/skills/openmm in your project. Claude Code loads it when a task matches its description.

How do I install Openmm in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill openmm -a codex`. Or copy the skill folder (skills/openmm in lamm-mit/scienceclaw) into .agents/skills/openmm in your project. Codex loads it when a task matches its description.

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

What does Openmm need to run?

Going by SKILL.md and its folder, Openmm needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Openmm access the network?

SKILL.md names 4 domains. As links in the text: openmm.org, ambermd.org, charmm.org and github.com. This is read from the text; nothing was executed.

Is Openmm 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Openmm use?

Openmm is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Openmm use?

About 1.5k tokens (SKILL.md is roughly 6.1k 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 Openmm?

Skills that share tags, products or a category with Openmm: Astropy (zLanqing/codex-claude-academic-skills, 4.6k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.6k stars), Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars) and Weather (trpc-group/trpc-agent-go, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Openmm?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.