Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill molecular-dynamicsgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/molecular-dynamics)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/molecular-dynamics"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/molecular-dynamics"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/molecular-dynamics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.03614 |
| Opus 5 | $0.00034 | $0.01807 |
| Sonnet 5 | $0.00013 | $0.00723 |
| 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 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.
Copies of this mod
6 near-identical copies found in the catalogue:
- molecular-dynamics — 100% identical, 6 lines differ
- molecular-dynamics — 100% identical, 8 lines differ
- molecular-dynamics — 98% identical, 7 lines differ
- molecular-dynamics — 98% identical, 7 lines differ
- molecular-dynamics — 98% identical, 7 lines differ
- molecular-dynamics — 98% identical, 7 lines differ
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
uv 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.
- 8d ago First seen · 459 lines · 67 tokens per session scan A ca248ad20180
molecular-dynamics is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 3,614 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.