Prepare input files for molecular docking software, automatically determine Grid Box center and size. Supports AutoDock Vina, AutoDock4, and other mainstream docking tools.
Install with Tessl CLI
npx tessl i github:aipoch/medical-research-skills --skill protein-docking-configurator72
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Vina config from active site residues
Script invocation
100%
100%
Active site flag
100%
100%
Residue chain format
100%
100%
Software flag vina
100%
100%
Exhaustiveness value
100%
100%
Center coordinates present
100%
100%
Size fields present
100%
100%
Minimum box size
100%
100%
Receptor entry
100%
100%
Ligand entry
100%
100%
Without context: $0.4359 · 1m 44s · 13 turns · 17 in / 7,928 out tokens
With context: $0.5355 · 1m 49s · 24 turns · 62 in / 5,169 out tokens
AutoDock4 GPF from reference ligand
Script invocation
100%
62%
Reference ligand flag
100%
100%
Software autodock4
100%
100%
GPF file extension
100%
100%
Spacing value
100%
100%
Even npts values
100%
100%
Minimum npts
100%
100%
Receptor atom types
100%
100%
Ligand atom types with Cl
100%
100%
Gridcenter field
100%
100%
Without context: $0.5068 · 1m 49s · 17 turns · 24 in / 7,851 out tokens
With context: $0.7241 · 1m 57s · 33 turns · 187 in / 6,467 out tokens
Python module DockingConfigurator API
Correct import path
100%
100%
No subprocess usage
100%
100%
DockingConfigurator instantiation
100%
100%
Site or ligand method
100%
100%
write_vina_config call
100%
100%
write_autodock4_gpf call
100%
100%
Exhaustiveness 32
100%
100%
Spacing 0.375
100%
100%
Vina config output
100%
100%
AutoDock4 GPF output
100%
100%
Without context: $0.6924 · 2m 17s · 26 turns · 30 in / 10,154 out tokens
With context: $0.5977 · 1m 47s · 26 turns · 212 in / 6,001 out tokens
Table of Contents
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