Automate molecular dynamics preparation workflows from structure acquisition to topology generation.
npx skills add https://github.com/matsunagalab/mdclaw --skill md-prepare
Please help me install this Agent Skill. Skill: MD Prepare Source: https://github.com/matsunagalab/mdclaw/tree/main/skills/md-prepare Command: npx skills add https://github.com/matsunagalab/mdclaw --skill md-prepare
MDPrepare automates end-to-end molecular dynamics preparation workflows for researchers, orchestrating structure acquisition, chain/ligand selection, structure cleaning, solvation, and topology generation using the MDClaw CLI.
Provide the target structure and run the MDPrepare flow to produce a solvated, topology-ready system.
High-intent search queries and answers about installing and using this skill.
Automate molecular dynamics preparation end-to-end by covering structure acquisition, chain and ligand selection, structure cleaning, solvation, and topology generation with deterministic defaults and DAG-based execution.
The preparation process for ligand parameterization and metal-ion handling involves running explicit- and implicit-solvent workflows that enforce robust validation and generate standardized topology files ready for simulation.
Yes, molecular dynamics preparation supports both explicit-water and implicit-solvent workflows, including metal-ion handling and ligand parameterization, to produce solvated and topology-ready systems.
Ensure reproducibility in structure cleaning and solvation through deterministic defaults, robust validation checks, and standardized progress tracking that enforces consistent decision points across workflows.
Provide the target structure to start the automated preparation workflow, which then handles chain and ligand selection, structure cleaning, solvation, and topology generation to produce a ready system.
Use automated MD preparation instead of manual structure cleaning when you need deterministic defaults, HITL-ready decision points, and DAG-based execution to handle complex ligand parameterization and metal-ion scenarios reliably.
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