agentmods.dev

chem-docking-void

A docking tool that places small molecules inside porous materials such as zeolites or metal-organic frameworks. It generates and ranks multiple three-dimensional arrangements of the molecule in the material.

In plain words
What is it for?
Use it with a molecule's SMILES string and a porous material's CIF structure to create ranked docked complexes and a results summary.
Why use it?
It removes the need to place and test guest molecules manually across a material's empty spaces. The ranked results help compare possible fits.

Skill for Claude CodeCodex

154 7d ago A scan 42 tokens original MIT
View the source file learningmatter-mit/AtomisticSkills
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.

agentmods
npx agentmods add skills/learningmatter-mit/atomisticskills/chem-docking-void
Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill chem-docking-void
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 894 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00042 $0.00894
Opus 5 $0.00021 $0.00447
Sonnet 5 $0.00008 $0.00179
Haiku 4.5 $0.00004 $0.00089

Measured 2d ago against content hash a7a2fe88c7c3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

chem-docking-void 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 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (examples/run_example.sh, scripts/run_docking.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/chem-docking-void/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

# Env: atomistic-agent
python .agents/skills/chem-docking-void/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:

python .agents/skills/chem-docking-void/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.

Read the full file on GitHub · 79 lines

Files

What ships with it

11 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. 2d ago First seen · 79 lines · 42 tokens per session scan A a7a2fe88c7c3

Subscribe to this mod's changes

chem-docking-void is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (154 stars, last pushed 7d ago), licensed MIT. It adds 42 tokens to every session and 894 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.

Scientificchemistrymaterials
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