菜单
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 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).
| name | dft-cp2k |
| description | 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. |
| compatibility | Requires a runnable CP2K environment and suitable basis/pseudopotential data for target elements. |
| license | GPL-3.0-only |
| metadata | {"author":"qqgu","version":"0.1.0","repository":"https://github.com/cp2k/cp2k"} |
Use this skill as the top-level CP2K orchestration layer.
This skill routes requests to one task-specific CP2K subskill path:
dft-cp2k/staticdft-cp2k/relaxdft-cp2k/mddft-cp2k/electronicThis router skill should:
This router skill should not:
The user must provide enough starting context:
static / relax / mdelectronicIf prerequisites are missing, stop and ask for them.
dft-cp2k/static.dft-cp2k/relax.dft-cp2k/md.dft-cp2k/electronic.dpdisp-submitCP2K is a common DFT backend for generating NEP training labels, especially for large systems where linear-scaling methods are advantageous.
dft-cp2k/static for single-point energy+force+stress.
Recommended: EPS_SCF 1e-7 or tighter.GPUMD/tools/Format_Conversion/cp2k2xyz or dpdata-cli:
uvx dpdata cp2k_output -i cp2k/output -O train.xyz -o extxyz
Provide: