jinzhezenggroup/computational-chemistry-agent-skills
39 skills
$npx skills add jinzhezenggroup/computational-chemistry-agent-skillsSkillInstalls
agent-taskboard-manifestIt 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 expliciantechamberA 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 typingaseUnified 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 calculatoase-calculatorsRoute ASE calculator-backend requests to adapter subskills based on backend choice. Use when ASE workflows need backend-specific calculator setup (for example GPAW or MACE) while keeping workflow logiase-workflowsRoute ASE atomistic workflow requests to task-specific subskills based on user intent. Use when the user asks for ASE-based static, relaxation, MD, or NEB workflows and you must apply consistent workfbandPrepare GPAW band-structure workflow scripts from existing ground-state context and user-specified k-path settings. Use when the user requests electronic band-structure calculations with explicit prercreate-skillCreate new Agent Skills following the agentskills.io specification. Use when the user wants to create, scaffold, or design a new skill for AI agents. Handles SKILL.md generation, directory structure sdeepmd-finetune-dpa3Fine-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 moddeepmd-python-inferenceRun 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 Pythdeepmd-trainTrain 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, dft-abinitRoute 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-anadft-cp2kRoute 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-analysidft-gpawRoute 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 prepadft-qeGenerate 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, badft-siestaRoute 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-anadft-vaspRoute 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 prepadftbplusRoute 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-strucdosPrepare GPAW DOS workflow scripts from existing ground-state context and user-specified DOS settings. Use when the user requests total/projected DOS setup with explicit prerequisite checks against pridpdata-cliA 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 youdpgen-simplifyPrepare, 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 `dpelectronicPrepare ABINIT electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.gjf-fluxAssemble 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 gpawConfigure ASE GPAW calculator adapter settings for ASE workflows. Use when ASE workflow tasks require GPAW backend setup including mode, k-point, convergence, and restart policies.lammps-deepmdA 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 executionlammps-reaxffRun 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_maceConfigure ASE MACE calculator adapter settings for ASE workflows. Use when ASE workflow tasks require MACE backend setup including model path/version, device/precision, stress availability, and inferemdPrepare ASE molecular-dynamics workflow tasks with backend-agnostic controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, thermostat, and output policies.nebPrepare ASE NEB workflow tasks with backend-agnostic controls. Use when the user needs reaction-path optimization between initial/final states with explicit image construction, spring settings, and coopenbabelA 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 computationpackmol-generate-mixtureA 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 neephonopyGeneral 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 properpymatgen-structureStructure 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/dopingrdkit-confA 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-erdkit-reprA standardized CLI wrapper for RDKit molecular featurization workflows that handles physicochemical descriptor computation (outputs .csv) and molecular fingerprint extraction (outputs .npy or .csv), wreacnetgeneratorRun 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 drelaxPrepare ASE geometry-optimization workflow tasks with backend-agnostic controls. Use when the user needs structural relaxation while selecting optimizer, convergence target, constraints, and output trrun-gaussActs 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 (staticPrepare ASE static (single-point) workflow tasks with backend-agnostic workflow controls. Use when the user needs one-shot energy/force/stress evaluation through an ASE calculator adapter.unimolA standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDK