New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Computational Chemistry Guide

by wentorai in wentorai/research-plugins

DFT, molecular simulation, and reaction prediction tools for chemists

MITAuto-check passedResearch & Science

Install Computational Chemistry Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill computational-chemistry-guide -a claude-code

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

GitHub CLI
$ gh skill install wentorai/research-plugins computational-chemistry-guide --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/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/domains/chemistry/computational-chemistry-guide .claude/skills/computational-chemistry-guide && 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
computational-chemistry-guide
GitHub stars
298
Used in
1 other repo
Token cost
~2.3k tokens
SKILL.md length
504 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

DFT, molecular simulation, and reaction prediction tools for chemists

  • Tasks that involve Drug discovery and cheminformatics
  • SKILL.md covers Overview, Density Functional Theory (DFT), Molecular Dynamics Simulations and Machine Learning in…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Computational Chemistry Guide is an agent skill from wentorai/research-plugins. DFT, molecular simulation, and reaction prediction tools for chemists

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Drug discovery and cheminformatics. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/computational-chemistry-guide”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).

    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):

    • faccts.de
    • docs.openmm.org
    • rdkit.org
    • psicode.org
    • onlinelibrary.wiley.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

Computational Chemistry Guide loads about 2.3k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 504 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 504 words, ~2,286 tokens.

Download SKILL.mdSave it as .claude/skills/computational-chemistry-guide/SKILL.md (or your agent's skills folder).
name
computational-chemistry-guide
description
DFT, molecular simulation, and reaction prediction tools for chemists

Computational Chemistry Guide

Overview

Computational chemistry bridges quantum mechanics and practical chemistry, enabling researchers to predict molecular properties, reaction mechanisms, and material behaviors without stepping into a wet lab. From drug design to catalyst optimization, computational methods accelerate discovery by screening thousands of candidates before committing to synthesis.

This guide covers the major computational chemistry paradigms: Density Functional Theory (DFT) for electronic structure calculations, molecular dynamics (MD) for simulating atomic motion, machine learning potentials for scaling up simulations, and reaction prediction tools for retrosynthesis and mechanism elucidation. Each section includes tool recommendations, typical workflows, and code examples.

Whether you are a chemistry PhD student running your first Gaussian calculations, a materials scientist exploring new alloys with VASP, or a medicinal chemist using ML-based property prediction, this skill provides the conceptual framework and practical recipes to get productive quickly.

Density Functional Theory (DFT)

When to Use DFT

DFT is the workhorse of quantum chemistry. It provides a good balance of accuracy and computational cost for systems of up to a few hundred atoms.

PropertyDFT SuitabilityTypical Error
Molecular geometryExcellent< 0.02 Angstrom
Vibrational frequenciesGood3-5%
Reaction barriersGood with correction2-5 kcal/mol
Band gapsFair (tends to underestimate)0.5-1.0 eV
Van der Waals interactionsRequires dispersion correctionVaries
Excited statesFair (TD-DFT)0.2-0.5 eV
Software Comparison
SoftwareLicenseStrengthsBasis Sets
GaussianCommercialBroad functionality, well-documentedGaussian-type
ORCAFree (academic)DFT + wavefunction methods, excellent supportGaussian-type
VASPCommercialPeriodic systems, materials sciencePlane-wave
Quantum ESPRESSOOpen sourcePeriodic DFT, phononsPlane-wave
Psi4Open sourceReference implementations, Python APIGaussian-type
CP2KOpen sourceMixed Gaussian/plane-wave, large systemsMixed
ORCA DFT Workflow Example
# geometry_optimization.inp
! B3LYP def2-TZVP D3BJ OPT FREQ
# B3LYP functional, triple-zeta basis, D3 dispersion, optimize + frequencies

%pal
  nprocs 8
end

%maxcore 4000

* xyz 0 1
C  0.000  0.000  0.000
O  1.200  0.000  0.000
H -0.500  0.866  0.000
H -0.500 -0.866  0.000
*

Run with:

bash
orca geometry_optimization.inp > geometry_optimization.out
Analyzing DFT Results with Python
python
from ase.io import read
from ase.visualize import view

# Read optimized geometry from ORCA output
atoms = read('geometry_optimization.xyz')

# Extract energies from output file
import re

with open('geometry_optimization.out') as f:
    text = f.read()

# Total energy
energy = float(re.search(r'FINAL SINGLE POINT ENERGY\s+([-\d.]+)', text).group(1))
print(f"Total energy: {energy:.6f} Hartree")
print(f"Total energy: {energy * 627.509:.2f} kcal/mol")

# Thermochemistry
gibbs_match = re.search(r'Final Gibbs free energy\s+\.\.\.\s+([-\d.]+)', text)
if gibbs_match:
    gibbs = float(gibbs_match.group(1))
    print(f"Gibbs free energy: {gibbs:.6f} Hartree")

Molecular Dynamics Simulations

MD Pipeline
Initial Structure (.pdb/.mol2)
    |
    v
[Parameterization] --> Force field assignment (AMBER, CHARMM, OPLS)
    |
    v
[Solvation] --> Add solvent box, ions
    |
    v
[Minimization] --> Energy minimization (steepest descent)
    |
    v
[Equilibration] --> NVT then NPT ensemble (100 ps - 1 ns)
    |
    v
[Production] --> NPT ensemble (10 ns - microseconds)
    |
    v
[Analysis] --> RMSD, RMSF, hydrogen bonds, free energy
OpenMM Quick Start
python
from openmm.app import *
from openmm import *
from openmm.unit import *

# Load structure
pdb = PDBFile('protein.pdb')
forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')

# Create system
modeller = Modeller(pdb.topology, pdb.positions)
modeller.addSolvent(forcefield, model='tip3p', padding=1.0*nanometers)

system = forcefield.createSystem(
    modeller.topology,
    nonbondedMethod=PME,
    nonbondedCutoff=1.0*nanometers,
    constraints=HBonds
)

# Set up simulation
integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds)
simulation = Simulation(modeller.topology, system, integrator)
simulation.context.setPositions(modeller.positions)

# Minimize
simulation.minimizeEnergy()

# Run production (10 ns)
simulation.reporters.append(DCDReporter('trajectory.dcd', 1000))
simulation.reporters.append(
    StateDataReporter('log.csv', 1000, step=True,
                      potentialEnergy=True, temperature=True)
)
simulation.step(2500000)  # 10 ns at 4 fs timestep

Machine Learning in Computational Chemistry

Show full SKILL.md (212 more words)Show less
ML Potential Energy Surfaces

Machine learning potentials achieve near-DFT accuracy at a fraction of the cost:

MethodSpeed vs DFTAccuracyTraining Data
ANI1000x faster~1 kcal/molPre-trained
SchNet100-1000x~1 kcal/mol1K-100K configs
MACE100-1000x< 1 kcal/mol1K-100K configs
GemNet100-1000x< 1 kcal/mol1K-100K configs
Property Prediction with RDKit
python
from rdkit import Chem
from rdkit.Chem import Descriptors, AllChem
import numpy as np

def compute_molecular_features(smiles):
    """Compute molecular descriptors from SMILES string."""
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return None

    features = {
        'molecular_weight': Descriptors.MolWt(mol),
        'logp': Descriptors.MolLogP(mol),
        'hbd': Descriptors.NumHDonors(mol),
        'hba': Descriptors.NumHAcceptors(mol),
        'tpsa': Descriptors.TPSA(mol),
        'rotatable_bonds': Descriptors.NumRotatableBonds(mol),
        'aromatic_rings': Descriptors.NumAromaticRings(mol),
        'heavy_atoms': mol.GetNumHeavyAtoms(),
    }

    # Morgan fingerprint (ECFP4)
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048)
    features['fingerprint'] = np.array(fp)

    return features

# Lipinski's Rule of Five check
def check_druglikeness(smiles):
    feats = compute_molecular_features(smiles)
    if feats is None:
        return False
    return (feats['molecular_weight'] <= 500 and
            feats['logp'] <= 5 and
            feats['hbd'] <= 5 and
            feats['hba'] <= 10)

Reaction Prediction

Retrosynthesis Tools
ToolApproachAccess
ASKCOSTemplate-based + MLMIT, web interface
IBM RXNTransformer-basedFree API
SyntheseusMulti-model frameworkOpen source
RetroTRAETransformerOpen source
Using RDKit for Reaction Processing
python
from rdkit.Chem import AllChem, Draw

# Define a reaction (Suzuki coupling)
rxn_smarts = '[c:1][B](O)O.[c:2][Cl]>>[c:1][c:2]'
rxn = AllChem.ReactionFromSmarts(rxn_smarts)

# Apply reaction
reactant1 = Chem.MolFromSmiles('c1ccc(B(O)O)cc1')  # Phenylboronic acid
reactant2 = Chem.MolFromSmiles('c1ccc(Cl)cc1')      # Chlorobenzene

products = rxn.RunReactants((reactant1, reactant2))
for product_set in products:
    for product in product_set:
        print(Chem.MolToSmiles(product))  # Biphenyl

Best Practices

  • Benchmark your method. Always validate your computational protocol against known experimental data before applying it to new systems.
  • Use appropriate levels of theory. Do not use MP2 when B3LYP suffices, and do not use B3LYP when you need CCSD(T) accuracy.
  • Include dispersion corrections. D3BJ or D4 corrections are essential for non-covalent interactions.
  • Check convergence. Verify that geometry optimizations, SCF calculations, and MD simulations have properly converged.
  • Report computational details completely. Functional, basis set, dispersion correction, solvent model, and software version should all be stated.
  • Archive your input/output files. Computational chemistry is reproducible only if all parameters are preserved.

References

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

Files

Just SKILL.md in skills/domains/chemistry/computational-chemistry-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Computational Chemistry Guide 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.

Computational Chemistry Guide compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Computational Chemistry Guide this skillwentorai/research-plugins2981 repos~2.3kAutomated safety check: PassMIT
MolecodeAtomFlow-AI/MoleCode306—~1.9kAutomated safety check: PassMIT
Drug DiscoveryTommy-yw/RunbookHermes5461 repos~2.3kAutomated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biomedical Analysis Dispatchxjtulyc/MedgeClaw6171 repos~2kAutomated safety check: PassNone
Biopipelineslocbp-uzh/biopipelines109—~2.4kAutomated safety check: PassMIT

Similar skills

  • Molecode

    AtomFlow-AI/MoleCode

    A skill your agent uses for deterministic molecule understanding, graph-level editing, generation, and validation with MoleCode — an explicit Mermaid graph in which every atom and bond is a typed…

    306 GitHub stars~1.9k tokensUpdated 4 mo ago
    Research & ScienceAuto-check passed

  • Drug Discovery

    Tommy-yw/RunbookHermes

    Pharmaceutical research assistant for drug discovery workflows.

    546 GitHub starsUsed in 1 repo~2.3k tokens
    Research & ScienceAuto-check passed

  • DiffDock Molecular Docking

    K-Dense-AI/scientific-agent-skills

    Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.

    48k GitHub starsUsed in 1 repo~3k tokens
    Research & ScienceAuto-check: notes

  • Biomedical Analysis Dispatch

    xjtulyc/MedgeClaw

    Routes bioinformatics, drug discovery, clinical and multi-omics tasks from a chat interface to Claude Code sessions running K-Dense scientific skills, with a live dashboard per task.

    617 GitHub starsUsed in 1 repo~2k tokens
    Research & ScienceAuto-check passed

  • Biopipelines

    locbp-uzh/biopipelines

    Design and run computational protein and ligand workflows on a GPU: binder and enzyme design, de novo backbone generation, inverse folding and sequence redesign, structure prediction, protein-ligand…

    109 GitHub stars~2.4k tokensUpdated 10 days ago
    Research & ScienceAuto-check passed

  • Tooluniverse Chemical Compound Retrieval

    wu-yc/LabClaw

    Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment.

    1.1k GitHub starsUsed in 2 repos~2.3k tokens
    Research & ScienceAuto-check passed

More from wentorai/research-plugins

All 405 skills in this repo

  • Abstract Writing Guide

    wentorai/research-plugins

    Craft structured research abstracts that maximize clarity and journal acceptance

    298 GitHub starsUsed in 1 repo~1.7k tokens
    Auto-check passed

  • Academic Citation Manager

    wentorai/research-plugins

    Manage academic citations across BibTeX, APA, MLA, and Chicago formats

    298 GitHub starsUsed in 1 repo~2.7k tokens
    Auto-check passed

  • Academic Paper Summarizer

    wentorai/research-plugins

    Summarize academic papers with structured extraction of key elements

    298 GitHub starsUsed in 1 repo~1.4k tokens
    Auto-check passed

  • Academic Study Methods

    wentorai/research-plugins

    Evidence-based study techniques for academic learning and retention

    298 GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check passed

  • Academic Tone Guide

    wentorai/research-plugins

    Adjust writing tone and register for academic audiences and venues

    298 GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check passed

  • Academic Translation Guide

    wentorai/research-plugins

    Academic translation, post-editing, and Chinglish correction guide

    298 GitHub starsUsed in 1 repo~1.6k tokens
    Auto-check passed

Categories

Questions about Computational Chemistry Guide

What does Computational Chemistry Guide do?

DFT, molecular simulation, and reaction prediction tools for chemists. Computational Chemistry Guide is an agent skill from wentorai/research-plugins.

When should I use Computational Chemistry Guide?

Computational Chemistry Guide fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Computational Chemistry Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill computational-chemistry-guide -a claude-code`. Or copy the skill folder (skills/domains/chemistry/computational-chemistry-guide in wentorai/research-plugins) into .claude/skills/computational-chemistry-guide in your project. Claude Code loads it when a task matches its description.

How do I install Computational Chemistry Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill computational-chemistry-guide -a codex`. Or copy the skill folder (skills/domains/chemistry/computational-chemistry-guide in wentorai/research-plugins) into .agents/skills/computational-chemistry-guide in your project. Codex loads it when a task matches its description.

Can I use Computational Chemistry Guide 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 wentorai/research-plugins --skill computational-chemistry-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/computational-chemistry-guide, .gemini/skills/computational-chemistry-guide, .github/skills/computational-chemistry-guide and .opencode/skills/computational-chemistry-guide in your project.

What does Computational Chemistry Guide need to run?

SKILL.md names no scripts, command-line tools or credentials: Computational Chemistry Guide is instructions for the agent only. Our summary lists: Python 3.

Does Computational Chemistry Guide access the network?

SKILL.md names 5 domains. As links in the text: faccts.de, docs.openmm.org, rdkit.org, psicode.org and onlinelibrary.wiley.com. This is read from the text; nothing was executed.

Is Computational Chemistry Guide 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. Review the folder before installing.

What licence does Computational Chemistry Guide use?

Computational Chemistry Guide 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 Computational Chemistry Guide use?

About 2.3k tokens (SKILL.md is roughly 9.1k 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 Computational Chemistry Guide?

Skills that share tags, products or a category with Computational Chemistry Guide: Molecode (AtomFlow-AI/MoleCode, 306 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Computational Chemistry Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

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