molecular-dynamics

molecular-dynamics is a skill for Claude Code, Codex from Lord1Egypt/scientific-agent-toolkit. It costs 67 tokens per session (3,604 once invoked), scanned A, a copy of molecular-dynamics, MIT.

A Python workflow for simulating how proteins and small molecules move over time, then examining the resulting motion. It uses OpenMM to run simulations and MDAnalysis to study their recorded paths.

In plain words
What is it for?
Use it to prepare protein or small-molecule systems, run energy minimization and molecular simulations, and measure changes such as structural movement, contacts, and estimated free energy.
Why use it?
It helps researchers test molecular stability, flexibility, and drug binding computationally before relying only on laboratory experiments.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to prepare protein or small-molecule systems, run energy minimization and molecular simulations, and measure changes such as structural movement, contacts, and estimated free energy.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Lord1Egypt/scientific-agent-toolkit --skill molecular-dynamics
Clone the repo
git clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkit

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for molecular-dynamics

README.md
[![agentmods](https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics/github.svg)](https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics)
Your own site
<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for molecular-dynamics

Your own site · 80×15
<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/molecular-dynamics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,604 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 98% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.03604
Opus 5 $0.00034 $0.01802
Sonnet 5 $0.00013 $0.00721
Haiku 4.5 $0.00007 $0.00360

Measured 8d ago against content hash 39610e686445, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

molecular-dynamics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

98% identical to molecular-dynamics — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

scientific-skills/molecular-dynamics/SKILL.md · 458 lines

How it starts

The opening of the file, as written. The whole thing — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Molecular Dynamics

Overview

Molecular dynamics (MD) simulation computationally models the time evolution of molecular systems by integrating Newton's equations of motion. This skill covers two complementary tools:

  • OpenMM (https://openmm.org/): High-performance MD simulation engine with GPU support, Python API, and flexible force field support
  • MDAnalysis (https://mdanalysis.org/): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages

Installation:

conda install -c conda-forge openmm mdanalysis nglview
# or
pip install openmm mdanalysis

When to Use This Skill

Use molecular dynamics when:

  • Protein stability analysis: How does a mutation affect protein dynamics?
  • Drug binding simulations: Characterize binding mode and residence time of a ligand
  • Conformational sampling: Explore protein flexibility and conformational changes
  • Protein-protein interaction: Model interface dynamics and binding energetics
  • RMSD/RMSF analysis: Quantify structural fluctuations from a reference structure
  • Free energy estimation: Compute binding free energy or conformational free energy
  • Membrane simulations: Model proteins in lipid bilayers
  • Intrinsically disordered proteins: Study IDR conformational ensembles

Core Workflow: OpenMM Simulation

1. System Preparation

from openmm.app import *
from openmm import *
from openmm.unit import *
import sys

def prepare_system_from_pdb(pdb_file, forcefield_name="amber14-all.xml",
                              water_model="amber14/tip3pfb.xml"):
    """
    Prepare an OpenMM system from a PDB file.

    Args:
        pdb_file: Path to cleaned PDB file (use PDBFixer for raw PDB files)
        forcefield_name: Force field XML file
        water_model: Water model XML file

    Returns:
        pdb, forcefield, system, topology
    """
    # Load PDB
    pdb = PDBFile(pdb_file)

    # Load force field
    forcefield = ForceField(forcefield_name, water_model)

    # Add hydrogens and solvate
    modeller = Modeller(pdb.topology, pdb.positions)
    modeller.addHydrogens(forcefield)

    # Add solvent box (10 Å padding, 150 mM NaCl)
    modeller.addSolvent(
        forcefield,
        model='tip3p',
        padding=10*angstroms,
        ionicStrength=0.15*molar
    )

    print(f"System: {modeller.topology.getNumAtoms()} atoms, "
          f"{modeller.topology.getNumResidues()} residues")

    # Create system
    system = forcefield.createSystem(
        modeller.topology,
        nonbondedMethod=PME,         # Particle Mesh Ewald for long-range electrostatics
        nonbondedCutoff=1.0*nanometer,
        constraints=HBonds,           # Constrain hydrogen bonds (allows 2 fs timestep)
        rigidWater=True,
        ewaldErrorTolerance=0.0005
    )

    return modeller, system

Read the full file on GitHub · 458 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 458 lines · 67 tokens per session scan A 39610e686445

Subscribe to this mod's changes

molecular-dynamics is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 3,604 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to molecular-dynamics, differing in 7 lines, and is treated as a copy.

Related

Other skills, from other repositories

cellxgene-census-query

Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…

PharMolix/OpenBioMed · 105 tokens

alterlab-deep-research

Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology…

AlterLab-IEU/AlterLab-Academic-Skills · 239 tokens

alterlab-imaging-data-commons

Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…

AlterLab-IEU/AlterLab-Academic-Skills · 90 tokens

alterlab-pyhealth

Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…

AlterLab-IEU/AlterLab-Academic-Skills · 117 tokens

alterlab-cobrapy

Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…

AlterLab-IEU/AlterLab-Academic-Skills · 91 tokens

alterlab-deeptools

Process and visualize deep-sequencing coverage with the deepTools CLI — convert BAM to bigWig (bamCoverage), build log2 ratio tracks (bamCompare), run QC (multiBamSummary correlation, PCA, plotFingerprint), apply the ATAC-seq Tn5 shift (alignmentSieve --ATACshift), and make TSS/peak heatmaps and profiles…

AlterLab-IEU/AlterLab-Academic-Skills · 173 tokens