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Agent skill

Chem Docking Void

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.

MITAuto-check passedResearch & Science

Install Chem Docking Void

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill chem-docking-void -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills chem-docking-void --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/chem-docking-void .claude/skills/chem-docking-void && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
chem-docking-void
GitHub stars
175
Token cost
~918 tokens
SKILL.md length
343 words
Files
12 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.

  • Works in 3 steps: Identify Inputs → Basic Docking Run → Tuning Hyperparameters
  • Tasks that involve Drug discovery and cheminformatics
  • SKILL.md covers Goal, Instructions, Constraints and References
  • Runs Shell and Python scripts from its folder

What it does

Chem Docking Void is an agent skill from learningmatter-mit/AtomisticSkills. Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.

Its SKILL.md is about 920 tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts (for example `examples/README.md`, `examples/example_output/docking_results.json` and `examples/run_example.sh`).

It sits in Research & Science, covering Drug discovery and cheminformatics. It works with RDKit. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/chem-docking-void”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Identify Inputs
  2. Basic Docking Run
  3. Tuning Hyperparameters

What it can do on your machine

Read from SKILL.md and the folder at commit 7f2d86d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell and Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chem Docking Void loads about 918 tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 343 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~918

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 343 words, ~918 tokens.

Download SKILL.mdSave it as .claude/skills/chem-docking-void/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
chem-docking-void
description
Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.
metadata.category
materials, chemistry
metadata.venv
cpu

chem-docking-void

Goal

To perform molecular docking of a small-molecule ligand into a porous material structure (CIF format) using the VOID library. This skill aims to automatically generate a robust sampling of guest conformers using RDKit, optimize them, and then distribute them throughout the host framework using Voronoi-based cluster sampling and physics-informed collision filtering.

This will output:

  • Ranked docked complexes saved individually as standard CIF files.
  • A metadata summary (docking_results.json) capturing the generation parameters, associated RDKit conformer energies, and matched pose IDs.

Instructions

1. Identify Inputs

You will need:

  • The SMILES string of your guest molecule.
  • The CIF file path to your porous material (e.g. Zeolites, MOFs).
2. Basic Docking Run

A standard run accepts the chemical inputs and saves outputs to a designated folder.

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu+void python ${CLAUDE_SKILL_DIR}/scripts/run_docking.py \
  --smiles "CC12C3C4C5C6C1C7C2C3C4C5C67" \
  --host_cif /path/to/host/material.cif \
  --output_dir output/docked_poses \
  --num_conformers 5

(The SMILES here represents Adamantane or similar structures for testing.)

3. Tuning Hyperparameters

The clustering map and acceptance rates are highly sensitive to VOID's search parameters. Use the advanced arguments for dense loading or strict spatial tolerances:

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu+void python ${CLAUDE_SKILL_DIR}/scripts/run_docking.py \
  --smiles "CC(=O)Oc1ccccc1C(=O)O" \
  --host_cif /path/to/host/MOF.cif \
  --output_dir output/docked_poses \
  --num_conformers 10 \
  --threshold 1.8 \
  --attempts 2000 \
  --structs_per_loading 5 \
  --num_clusters 150 \
  --max_loading 1 \
  --max_subdock 200 \
  --remove_species "H2O" "Na"
Meaning of Key Hyperparameters:
  • --num_conformers: (RDKit) How many of the lowest-energy 3D geometries to test.
  • --threshold: The acceptable minimum distance (Å) between the host atoms and guest atoms. A lower value allows tighter squeezes but risks atomic clashes.
  • --attempts: How many random translation/rotation insertion guesses the Subdocker makes per BatchDocker queue limit.
  • --structs_per_loading: Maximum number of successful geometries to export out of all validated matches, per conformer tested.
  • --num_clusters & --min_radius: Settings for the VoronoiClustering sampler that determine the density and minimum pore volume of chosen docking nodes within the material.
  • --remove_species: Pre-cleans the CIF file of specified elements (like free solvent) before docking.

Constraints

  • Environment: Requires the cpu+void environment (venv/run cpu+void ...), where VOID, rdkit, and pymatgen are accessible.
  • Loading Size: By default, this script handles single-guest loadings per unit cell. Heavy multiple guest loading (--max_loading > 1) may scale exponentially in computational time depending on pore size.
  • Outputs: Everything is standardized to CIF files for compatibility with subsequent DFT or MLIP workflows.

References

  1. VOID Library
  2. Pymatgen pymatgen.core.Structure and pymatgen.core.Molecule
  3. RDKit cheminformatics (MMFF94 structural optimization)

Author: Mingrou Xie Contact: GitHub @mingrouxie

© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts) in skills/chem-docking-void of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/CHA.cif
  • examples/README.md
  • examples/example_output/docking_results.json
  • examples/example_output/pose_1_loading_1_conf_1.cif
  • examples/example_output/pose_2_loading_1_conf_1.cif
  • examples/example_output/pose_3_loading_2_conf_1.cif
  • examples/example_output/pose_4_loading_2_conf_1.cif
  • examples/example_output/pose_5_loading_3_conf_1.cif
  • examples/example_output/pose_6_loading_3_conf_1.cif
  • examples/run_example.sh
  • scripts/run_docking.py

Open the folder on GitHubat commit 7f2d86d

Compare with similar skills

Chem Docking Void next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Chem Docking Void compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Chem Docking Void this skilllearningmatter-mit/AtomisticSkills175—~918Automated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Edu Chem Reactionwy51ai/edulab1.4k—~1.2kAutomated safety check: PassApache-2.0
Biopipelineslocbp-uzh/biopipelines109—~2.4kAutomated safety check: PassMIT
RDKit Cheminformatics Practicesaiming-lab/AutoResearchClaw15k—~708Automated safety check: PassMIT
Rowanlamm-mit/scienceclaw2444 repos~3.1kAutomated safety check: WarnProprietary

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Works with

Categories

Questions about Chem Docking Void

What does Chem Docking Void do?

Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes. Chem Docking Void is an agent skill from learningmatter-mit/AtomisticSkills. Dock small-molecule guests into a porous host material using the VOID library (Voronoi Clustering), generating multiple 3D conformers with RDKit and ranking generated complexes.

When should I use Chem Docking Void?

Chem Docking Void fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Chem Docking Void in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-docking-void -a claude-code`. Or copy the skill folder (skills/chem-docking-void in learningmatter-mit/AtomisticSkills) into .claude/skills/chem-docking-void in your project. Claude Code loads it when a task matches its description.

How do I install Chem Docking Void in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-docking-void -a codex`. Or copy the skill folder (skills/chem-docking-void in learningmatter-mit/AtomisticSkills) into .agents/skills/chem-docking-void in your project. Codex loads it when a task matches its description.

Can I use Chem Docking Void in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-docking-void -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chem-docking-void, .gemini/skills/chem-docking-void, .github/skills/chem-docking-void and .opencode/skills/chem-docking-void in your project.

What does Chem Docking Void need to run?

Going by SKILL.md and its folder, Chem Docking Void needs a shell and Python for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Chem Docking Void access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Chem Docking Void safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Chem Docking Void use?

Chem Docking Void is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Chem Docking Void use?

About 918 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Chem Docking Void?

Skills that share tags, products or a category with Chem Docking Void: DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), Edu Chem Reaction (wy51ai/edulab, 1.4k stars), Biopipelines (locbp-uzh/biopipelines, 109 stars) and RDKit Cheminformatics Practices (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chem Docking Void?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.