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.
npx skills add magic3007/dotfiles --skill molecular-dynamicsgit clone --depth 1 https://github.com/magic3007/dotfilesWrote 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.
[](https://agentmods.dev/skills/magic3007/dotfiles/molecular-dynamics)<a href="https://agentmods.dev/skills/magic3007/dotfiles/molecular-dynamics"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/molecular-dynamics.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.03612 |
| Opus 5 | $0.00034 | $0.01806 |
| Sonnet 5 | $0.00013 | $0.00722 |
| Haiku 4.5 | $0.00007 | $0.00361 |
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 4d 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.
This is a copy
100% identical to molecular-dynamics — 6 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.
How it starts
The opening of the file, as written. The whole thing — 459 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
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.
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.
- 4d ago First seen · 459 lines · 67 tokens per session scan A 34286765052a
molecular-dynamics is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 3,612 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to molecular-dynamics, differing in 6 lines, and is treated as a copy.
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