drug-protein-ligand-md

drug-protein-ligand-md is a skill for Claude Code, Codex from learningmatter-mit/AtomisticSkills. It costs 42 tokens per session (1,571 once invoked), scanned A, original, MIT.

A workflow for running a protein–ligand molecular dynamics simulation in OpenMM, a software toolkit for simulating molecular motion. It minimizes energy, equilibrates the system, and runs a production simulation.

In plain words
What is it for?
Use it to simulate how a protein and ligand move together, producing a trajectory and checkpoint files for later analysis.
Why use it?
It provides a defined sequence for turning a prepared molecular system into simulation results. This reduces the need to manually manage heating, pressure adjustment, restraints, and saved checkpoints.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to simulate how a protein and ligand move together, producing a trajectory and checkpoint files for later analysis.

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Install with agentmods
npx agentmods add skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md
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 learningmatter-mit/AtomisticSkills --skill drug-protein-ligand-md
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

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 drug-protein-ligand-md

README.md
[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md/github.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md)
Your own site
<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md/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 drug-protein-ligand-md

Your own site · 80×15
<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-protein-ligand-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,571 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00042 $0.01571
Opus 5 $0.00021 $0.00785
Sonnet 5 $0.00008 $0.00314
Haiku 4.5 $0.00004 $0.00157

Measured 10d ago against content hash 005ed4358252, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

drug-protein-ligand-md 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 10d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/run_md.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/drug-protein-ligand-md/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

drug-protein-ligand-md

Goal

To run a complete protein-ligand molecular dynamics simulation using OpenMM, starting from a system bundle produced by drug-complex-system-builder. The workflow includes:

  1. Energy minimization
  2. NVT equilibration with positional restraints on heavy atoms
  3. NPT equilibration with restraints gradually released
  4. NPT production run

The output is a DCD trajectory + final state checkpoint suitable for drug-trajectory-analysis.

Instructions

1. Prepare inputs

Required from drug-complex-system-builder:

  • system.xml: serialized OpenMM System
  • complex_solvated.pdb: solvated complex PDB (used as topology reference)

2. Run the simulation

# Env: drugmd-agent
python .agents/skills/drug-protein-ligand-md/scripts/run_md.py \
  --system_xml md/system/system.xml \
  --input_pdb md/system/complex_solvated.pdb \
  --temperature 300 \
  --pressure 1.0 \
  --timestep 4.0 \
  --minimize_steps 5000 \
  --equil_nvt_steps 25000 \
  --equil_npt_steps 50000 \
  --production_steps 2500000 \
  --restraint_k 50.0 \
  --reporting_interval 5000 \
  --checkpoint_interval 25000 \
  --output_dir md/run/

Key parameters:

  • --temperature: simulation temperature in Kelvin (default: 300).
  • --pressure: target pressure in atm (default: 1.0).
  • --timestep: integration timestep in fs (default: 4.0). 4 fs is safe with hydrogen mass repartitioning (HMR) from the system builder; use 2 fs without HMR.
  • --minimize_steps: max minimization steps (default: 5000). Set to 0 to skip.
  • --equil_nvt_steps: NVT equilibration steps with restraints on protein/ligand heavy atoms (default: 25000 = 100 ps at 4 fs).
  • --equil_npt_steps: NPT equilibration steps with restraints released (default: 50000 = 200 ps).
  • --production_steps: production NPT steps (default: 2500000 = 10 ns at 4 fs).
  • --restraint_k: restraint force constant for equilibration in kJ/mol/nm^2 (default: 50.0).
  • --reporting_interval: write trajectory frame every N steps (default: 5000 = 20 ps).
  • --checkpoint_interval: write checkpoint every N steps (default: 25000).

Read the full file on GitHub · 138 lines

Files

What ships with it

3 files 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. 10d ago First seen · 138 lines · 42 tokens per session scan A 005ed4358252

Subscribe to this mod's changes

drug-protein-ligand-md is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (163 stars, last pushed 7d ago), licensed MIT. It adds 42 tokens to every session and 1,571 once invoked, about $0.0002 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 2026-08-30.

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