computational-chemistry-agent-skills

In the author's own words, from each skill's SKILL.md — showing the first 30 of 68 files.

• agent-taskboard-manifest

  It is a specification for semantic workflows used by agents to plan, generate, formalize, summarize, and execute complex tasks, projects, experiments,and research efforts for agents, requiring explicit structure, lazy loading,scoped context, evidence-grounded routing, and human review at critical checkpoints. USE WHEN the user asks for a complex task, project, experiment, or research effort that…

  agent-workflow/agent-taskboard-manifest/SKILL.md on GitHub (opens in a new tab)

• phonopy

  General phonon-workflow skill built around phonopy, independent of force backend. USE WHEN you need to prepare finite-displacement phonon calculations, build force constants, and analyze phonon properties (band structure, DOS, thermal quantities) while obtaining forces from different engines such as VASP, Quantum ESPRESSO, or ML force fields.

  analysis/phonopy/SKILL.md on GitHub (opens in a new tab)

• reacnetgenerator

  Run ReacNetGenerator on reactive MD trajectories to generate reaction networks and reports. Use when the user wants to analyze LAMMPS dump/xyz/bond trajectories with ReacNetGenerator. Handles LAMMPS dump quirks like x/y/z vs xs/ys/zs by converting to x/y/z (orthorhombic + triclinic supported via reacnet-md-tools). Can infer atomname order from a LAMMPS data file. Runs via local reacnetgenerator i…

  analysis/reacnetgenerator/SKILL.md on GitHub (opens in a new tab)

• ase

  Unified ASE router skill with a tree of subskills for static/relax/MD/NEB workflows and backend adapters (GPAW, MACE). Use when you need backend-agnostic workflow orchestration while keeping calculator-specific setup isolated in adapter subskills, with reproducible task preparation as output.

  atomistic-workflows/ase/SKILL.md on GitHub (opens in a new tab)

• dpdata-cli

  A command-line utility for converting and manipulating over 50 atomic simulation data formats, including outputs from DFT and MD software (VASP, LAMMPS, Gaussian, QE, CP2K, ABACUS, etc.). USE WHEN you need to convert structural or trajectory files between different computational chemistry formats, or when parsing raw simulation outputs into structured training datasets (e.g., deepmd/raw, deepmd/n…

  data-processing/dpdata-cli/SKILL.md on GitHub (opens in a new tab)

• openbabel

  A versatile CLI tool for converting molecular file formats, generating 3D atomic coordinates from SMILES, rendering 2D chemical structure images, and preparing or extracting structures for computational workflows. USE WHEN you need to convert between chemical file formats (e.g., xyz, pdb, mol, smi, gjf), generate 3D structures from SMILES using `--gen3d`, render molecule images (PNG/SVG), or extr…

  data-processing/openbabel/SKILL.md on GitHub (opens in a new tab)

• packmol-generate-mixture

  A tool for generating initial packed molecular configurations (XYZ format) from single-molecule structures by calculating box dimensions, writing input scripts, and executing Packmol. USE WHEN you need to randomly pack a specific number of molecules into a simulation box (defined by target density or fixed lengths) to create starting geometries for molecular dynamics or related computational chem…

  data-processing/packmol-generate-mixture/SKILL.md on GitHub (opens in a new tab)

• pymatgen-structure

  Structure manipulation and crystal analysis workflows based on pymatgen. USE WHEN you need to read/write common atomistic formats (CIF, POSCAR, XYZ), build supercells, perform site substitution/doping, inspect symmetry (space group), or compute local structure descriptors for materials tasks.

  data-processing/pymatgen-structure/SKILL.md on GitHub (opens in a new tab)

• deepmd-finetune-dpa3

  Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. Use when the user wants to adapt a pre-trained DPA3 model to a new downstream dataset. Supports fine-tuning from a self-trained DPA3 model (.pt checkpoint), from a multi-task pre-trained model, or from a built-in pretrained model downloaded via `dp pretrained download` (e.g., DPA-3.1-3M, DPA-3.2-5M, DPA-3.3-1M). Covers single-task an…

  machine-learning-potentials/deepmd-finetune-dpa3/SKILL.md on GitHub (opens in a new tab)

• deepmd-python-inference

  Run Python inference with DeePMD-kit models using the DeepPot API. Use when the user wants to load a trained/frozen DeePMD model (.pth or .pb) or a built-in pretrained model (e.g., DPA-3.2-5M) in Python, predict energy/force/virial for atomic configurations, evaluate descriptors, or calculate model deviation between multiple models. Also covers using `dp test` CLI for batch evaluation against lab…

  machine-learning-potentials/deepmd-python-inference/SKILL.md on GitHub (opens in a new tab)

• deepmd-train

  Train DeePMD-kit models with progressive disclosure. Use when the user wants to train a DeePMD-kit potential, prepare an input.json, choose between model families such as se_e2_a/DeepPot-SE and DPA3, run `dp train`, monitor learning curves, freeze checkpoints, or test trained models. Start with model selection and read only the selected model reference under `models/` when model-specific configur…

  machine-learning-potentials/deepmd-train/SKILL.md on GitHub (opens in a new tab)

• dpgen-simplify

  Prepare, explain, validate, and run DP-GEN simplify workflows for reducing repeated or redundant DeepMD datasets. Use when the user wants to generate or modify `param.json` and `machine.json`, run `dpgen simplify param.json machine.json`, organize repeated simplify experiments, or inspect simplify outputs.

  machine-learning-potentials/dpgen-simplify/SKILL.md on GitHub (opens in a new tab)

• rdkit-conf

  A standardized CLI wrapper for RDKit 3D/2D conformer generation that samples multiple conformers per molecule (ETKDGv3, default 10), optimizes each with a force field (MMFF94s/UFF), keeps the lowest-energy conformer, automatically falls back to 2D layout on total embedding failure with a printed warning, and writes results to SDF or XYZ format. USE WHEN you need to generate 3D (or 2D fallback) mo…

  molecular-conformer/rdkit-conf/SKILL.md on GitHub (opens in a new tab)

• antechamber

  A command-line tool in AmberTools for preparing small molecules or non-standard residues within GAFF/AMBER-compatible chemical space for molecular mechanics simulations, by automating atom/bond typing, charge generation or import, and force-field–compatible input generation. USE WHEN you are working in AMBER, dealing with molecules not covered by standard force fields, and already have a structur…

  molecular-dynamics/antechamber/SKILL.md on GitHub (opens in a new tab)

• lammps-deepmd

  A tool and knowledge base for running molecular dynamics (MD) simulations in LAMMPS with the DeePMD-kit plugin. It handles input script preparation, ensemble selection (NVE/NVT/NPT), and job execution via `uv` or offline binaries. USE WHEN you need to set up, write, explain, or execute a LAMMPS molecular dynamics simulation using a DeePMD machine learning potential (e.g., `graph.pb`).

  molecular-dynamics/lammps-deepmd/SKILL.md on GitHub (opens in a new tab)

• lammps-reaxff

  Run reactive molecular dynamics simulations in LAMMPS with the ReaxFF potential, including preparing input scripts (pair_style reaxff + fix qeq/reaxff), mapping LAMMPS atom types to elements via pair_coeff, choosing ensembles (NVE/NVT/NPT), and adding common ReaxFF diagnostics such as species analysis. Use when the user wants LAMMPS+ReaxFF workflows or needs a working, annotated `input.lammps` te…

  molecular-dynamics/lammps-reaxff/SKILL.md on GitHub (opens in a new tab)

• rdkit-repr

  A standardized CLI wrapper for RDKit molecular featurization workflows that handles physicochemical descriptor computation (outputs .csv) and molecular fingerprint extraction (outputs .npy or .csv), with built-in SMILES validation. USE WHEN you need to compute RDKit molecular descriptors or fingerprints from SMILES datasets (.csv/.smi), or when you want to list all available descriptor names and…

  molecular-representation/rdkit-repr/SKILL.md on GitHub (opens in a new tab)

• unimol

  A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol fr…

  molecular-representation/unimol/SKILL.md on GitHub (opens in a new tab)

• dft-abinit

  Route ABINIT requests to task-specific subskills based on user intent. Use when the user asks for ABINIT workflows and you must decide between static, relaxation, molecular dynamics, or electronic-analysis preparation. This orchestration skill dispatches to the correct ABINIT subskill and enforces consistent handoff to submission skills.

  quantum-chemistry/dft-abinit/SKILL.md on GitHub (opens in a new tab)

• dft-cp2k

  Route CP2K requests to task-specific subskills based on user intent. Use when the user asks for CP2K workflows and you must decide between static, relaxation, molecular dynamics, or electronic-analysis preparation. This orchestration skill dispatches to the correct CP2K subskill and enforces consistent handoff to submission skills.

  quantum-chemistry/dft-cp2k/SKILL.md on GitHub (opens in a new tab)

• dft-gpaw

  Route GPAW DFT requests to task-specific subskills based on user intent. Use when the user asks for GPAW workflows and you must decide between static SCF, relaxation, DOS, or band-structure task preparation. This orchestration skill dispatches to the correct GPAW subskill and enforces consistent handoff to submission skills.

  quantum-chemistry/dft-gpaw/SKILL.md on GitHub (opens in a new tab)

• dft-qe

  Generate Quantum ESPRESSO DFT input tasks from a user-provided structure plus user-specified DFT settings. Use when the user wants to prepare QE calculations such as SCF, NSCF, relax, vc-relax, MD, bands, DOS, or phonons starting from a structure file or coordinates together with pseudopotentials, functional choice, cutoffs, k-point settings, smearing, spin/charge, and convergence parameters. Thi…

  quantum-chemistry/dft-qe/SKILL.md on GitHub (opens in a new tab)

• dft-siesta

  Route SIESTA requests to task-specific subskills based on user intent. Use when the user asks for SIESTA workflows and you must decide between static, relaxation, molecular dynamics, or electronic-analysis preparation. This orchestration skill dispatches to the correct SIESTA subskill and enforces consistent handoff to submission skills.

  quantum-chemistry/dft-siesta/SKILL.md on GitHub (opens in a new tab)

• dft-vasp

  Route VASP DFT requests to task-specific subskills based on user intent. Use when the user asks for VASP workflows and you must decide between static SCF, relaxation, DOS, or band-structure task preparation. This orchestration skill does not own detailed input generation logic; it dispatches to the correct VASP subskill and enforces consistent handoff to submission skills.

  quantum-chemistry/dft-vasp/SKILL.md on GitHub (opens in a new tab)

• dftbplus

  Route DFTB+ requests to task-specific subskills based on user intent. Use when the user asks for DFTB+ workflows and you must decide between static, relaxation, molecular dynamics, or electronic-structure post-ground-state preparation. This orchestration skill dispatches to the correct subskill and enforces consistent handoff to submission skills.

  quantum-chemistry/dftbplus/SKILL.md on GitHub (opens in a new tab)

• gjf-flux

  Assemble and extract Gaussian .gjf input file sections (directives, route, title, molecule blocks, appendices) and build single- or multi-step Link1 jobs from modular component files. USE WHEN needed for generating, refactoring, templating, or scripting Gaussian job files.

  quantum-chemistry/gjf-flux/SKILL.md on GitHub (opens in a new tab)

• run-gauss

  Acts as a knowledge base providing environment checklists, directory/scratch management, and bash command templates. USE WHEN you need to guide the execution of Gaussian computational chemistry jobs (.gjf) on local or remote/HPC environments.

  quantum-chemistry/run-gauss/SKILL.md on GitHub (opens in a new tab)

• xtb

  Prepare and explain xTB semiempirical quantum-chemistry workflows for single-point energy, forces, charges, dipole, geometry optimization, and molecular dynamics. Use when the user asks for xTB calculations directly, or wants to use xTB through Python/ASE/dpdata bridges while keeping xTB as the primary method rather than as an ASE-only backend.

  quantum-chemistry/xtb/SKILL.md on GitHub (opens in a new tab)

• xtbloom-integrate-ase-dpdata

  Integrate xTBloom with ASE or dpdata for molecular energy, force, charge, labeling, relaxation, optimizer, or dynamics workflows. Use when an AI coding agent needs to attach the xTBloom ASE calculator, configure dpdata's xtbloom driver or batch minimizer, preserve the adapters' eV/angstrom conventions, choose reproducible versus warm-started execution, or diagnose unsupported periodic inputs.

  quantum-chemistry/xtbloom-integrate-ase-dpdata/SKILL.md on GitHub (opens in a new tab)

• xtbloom-run-python-inference

  Write, review, and run high-level xTBloom Python GFN2-xTB inference with `Calculator`, `Structure`, and `BatchCalculator`, including single systems, repeated geometry updates, heterogeneous ragged batches, backend selection, units, finite-temperature meaning, and peer-local failure handling. Use for ordinary NumPy-based energy, force, and charge workflows; use a dedicated integration skill instea…

  quantum-chemistry/xtbloom-run-python-inference/SKILL.md on GitHub (opens in a new tab)

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