65 skills published by jinzhezenggroup across 1 repository. Together they weigh 114 889 tokens — that is what loading all of them at once would cost you in context.
65 skills 114 889 tokens total
> 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 needs to be carefully planned before execution USE WHEN the user provides a text-based plan and wants it to be made more detailed and formalized according to this specification. USE WHEN the user asks to summarize ongoing or completed work into a reusable workflow manifest. USE WHEN the user specifies the location of an existing agent workflow and wants it loaded and executed according to the specification.
> 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 structure that can be processed (e.g., pdb, mol2, ac, gout). Typical use cases include parameterizing ligands or modified residues (assigning atom/bond types, generating or reading partial charges), converting structures from upstream tools into mol2/prepi formats, and preparing topology-ready inputs for downstream tools such as LEaP. DO NOT USE for standard residues, metal complexes, inorganic systems, or when no valid molecular structure is available (e.g., only SMILES).
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.
Route 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 logic backend-agnostic.
Route 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 workflow controls independent of calculator backend.
Prepare 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 prerequisite checks and path-definition handling.
Prepare VASP band-structure workflow inputs from existing SCF context and user-specified band-path settings. Use when the user requests electronic band-structure calculations and needs explicit prerequisite checks, line-mode KPOINTS path setup, and stage-specific INCAR preparation.
Create 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 setup, and validation.
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 and multi-task fine-tuning workflows.
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 labeled data.
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 configuration is needed.
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.
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.
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.
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.
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. This skill prepares the QE task only; use a separate submission skill such as dpdisp-submit to submit the generated task.
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.
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.
Prepare VASP DOS workflow inputs from existing SCF artifacts and user-specified DOS settings. Use when the user requests total/projected DOS setup and needs INCAR/KPOINTS preparation with explicit prerequisite checks against prior SCF runs.
Prepare 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 prior converged calculations.
> 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/npy, deepmd/hdf5) for DeePMD-kit.
Use dpdata Python Driver plugins to label systems (energies/forces/virials) via System.predict(), list available drivers, and build Driver objects (ase/deepmd/gaussian/sqm/hybrid). Use when working with dpdata Python API (not CLI) and you need driver-based energy/force prediction, plugin registration keys, or examples of using dpdata with ASE calculators or DeePMD models.
Minimize geometries with dpdata minimizer plugins via System.minimize(), including how minimizers relate to drivers (ASEMinimizer needs a dpdata Driver) and how to list supported minimizers (ase/sqm). Use when doing geometry optimization/minimization through dpdata Python API.
> Run Shell commands as computational jobs, on local machines or HPC clusters, through Shell, Slurm, PBS, LSF, Bohrium, etc. USE WHEN the user needs to submit batch jobs to a cluster, run commands on a remote server, execute tasks via job schedulers (Slurm, PBS, LSF), or safely run long-term/background shell commands that require state tracking and auto-recovery.
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.
Prepare CP2K electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
Prepare SIESTA electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
Prepare DFTB+ electronic-analysis task inputs based on prior ground-state context. Use when the user requests band/DOS-style analyses and needs prerequisite-aware setup.
Prepare ABINIT electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
> 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.
Configure 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.
> 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`).
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` template.
Configure 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 inference controls.
Prepare 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.
Prepare CP2K molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, and thermostat/barostat settings.
Prepare SIESTA molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, and thermostat controls.
Prepare ABINIT molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories with explicit ensemble, timestep, and thermostat controls.
Prepare DFTB+ molecular-dynamics task inputs from a user-provided structure and MD controls. Use when the user needs finite-temperature trajectories using DFTB+.
Prepare 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 convergence controls.
> 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 extract geometries from simulation logs to build new inputs.
> 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 chemistry workflows.
> 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.
> 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.
> 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) molecular geometries from SMILES datasets (.csv/.smi) for downstream tasks such as docking, visualization, or 3D-descriptor computation.
> 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 presets.
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 if available or via `uvx --from reacnetgenerator ...`. Writes outputs into `out/<input_basename>/` with logs and a summary.
Prepare CP2K geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled optimization with explicit optimizer and convergence controls.
Prepare DFTB+ geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled structural optimization.
Prepare ABINIT geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled structural optimization with explicit force/stress convergence controls.
Prepare GPAW geometry-relaxation task inputs/scripts from a user-provided structure and essential optimization settings. Use when the user needs structure optimization with explicit optimizer and force-convergence policies.
Prepare ASE geometry-optimization workflow tasks with backend-agnostic controls. Use when the user needs structural relaxation while selecting optimizer, convergence target, constraints, and output trajectory policy independently of calculator backend.
Prepare SIESTA geometry-relaxation task inputs from a user-provided structure and optimization settings. Use when the user needs ion-only or cell-coupled structural optimization with explicit force/stress controls.
Prepare VASP geometry-relaxation input tasks from a user-provided structure and essential DFT settings. Use when the user needs ionic or cell-coupled relaxation and requires explicit ISIF-driven relaxation intent mapping, INCAR generation, and POTCAR mapping instructions.
> 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.
> USE WHEN requesting core chemical structural data (SMILES, formula, mass, 2D images) via IUPAC, common, or multilingual names. You MUST actively retrieve the data using this skill; DO NOT hallucinate or generate structures yourself. DO NOT USE WHEN asking for physical properties (melting point, solubility), safety/toxicity data (MSDS), or synthesis pathways.
Prepare DFTB+ single-point (static) task inputs from a user-provided structure and essential SCC/settings choices. Use when the user needs total-energy/electronic single-point evaluation.
Prepare ABINIT single-point (static) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit ABINIT cutoff, k-point, and SCF controls.
Prepare CP2K single-point (static) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit CP2K basis/potential and SCF controls.
Prepare VASP static SCF input tasks from a user-provided structure and essential DFT settings. Use when the user needs single-point electronic structure/total-energy calculations with INCAR generation, KSPACING-based k-point policy (or explicit KPOINTS on request), and POTCAR mapping instructions.
Prepare 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.
Prepare GPAW static SCF task inputs/scripts from a user-provided structure and essential DFT settings. Use when the user needs single-point total-energy/electronic calculations with explicit GPAW calculator settings and reproducible run script layout.
Prepare SIESTA single-point (static) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit SIESTA mesh, basis, and SCF controls.
> 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 framework.
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.