Route NEP requests to task-specific subskills. NEP (Neuroevolution Potential) is the native machine-learning potential family of the GPUMD ecosystem — analogous to DeePMD-kit for LAMMPS. Use when the user asks for `nep.in`, `train.xyz`, `test.xyz`, NEP training, NEP89 reuse, prediction mode, fine-tuning, dipole / polarizability auxiliary models, or automation via NepTrain / NepTrainKit.
Train a first NEP potential from labeled extxyz data. Use when the user needs `nep.in`, `train.xyz`, `test.xyz`, parameter guidance, loss.out interpretation, or deployment of the resulting `nep.txt` back into GPUMD. NEP is the native machine-learning potential for GPUMD and plays the role that DeePMD plays for LAMMPS.
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 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.
Route ABACUS requests to task-specific subskills based on user intent. Use when the user asks for any ABACUS DFT calculation and you need to determine whether the task is SCF, relaxation, MD, or electronic analysis.
Prepare ABACUS single-point (static SCF) task inputs from a user-provided structure and essential DFT settings. Use when the user needs total-energy/electronic SCF evaluation with explicit ABACUS INPUT/STRU/KPT generation, pseudopotential + orbital mapping, and basis-type selection (PW or LCAO).
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 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.