A high-performance computing skill for managing, debugging, and automating VASP first-principles simulation workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install vasp-skills
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install vasp-skills using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is HPC VASP?
HPC VASP is a specialized module designed to treat the Vienna Ab initio Simulation Package (VASP) as a structured, staged workflow. It provides AI agents and researchers with a coherent framework for handling the standard four-file input set: INCAR, POSCAR, KPOINTS, and POTCAR. By leveraging established protocols within Openclaw Skills, this tool ensures that computational material science tasks remain rigorous and reproducible.
The skill moves beyond simple file generation by providing deep context on electronic controls, k-point density, and smearing settings. It acts as a technical bridge between raw DFT theory and practical cluster execution, ensuring that users maintain strict discipline over species ordering and pseudopotential selection across complex simulation stages.
HPC VASP Use Cases
- Building standardized input sets for SCF, ionic relaxation, and static calculations.
- Troubleshooting VASP convergence issues, symmetry errors, and ionic relaxation failures.
- Automating the sequencing of DOS and band-structure calculations after ground-state verification.
- Managing resource-intensive workflows on scheduler-backed clusters using SLURM templates.
- Validating POTCAR datasets and species ordering for multi-element crystal structures.
How HPC VASP Works
- Reference Analysis: The skill initializes by consulting detailed manuals for input sets and parameter matrices to ensure theoretical alignment.
- Parameter Mapping: It maps specific simulation goals (like relaxation or bands) to specific INCAR and KPOINTS configurations.
- Constraint Validation: The system applies guardrails to prevent common errors, such as mixing incompatible POTCAR datasets or improper smearing for insulators.
- Execution Staging: It prepares the workflow for high-performance computing environments using localized cluster execution playbooks.
- Error Recovery: If a run fails, the skill utilizes an error-pattern dictionary to diagnose SCF, ionic, or handoff failure signatures and suggest fixes.
HPC VASP Setup
To integrate this skill into your agent environment, clone the repository and ensure your references directory is populated:
# Navigate to your agent skills directory
cd skills/hpc-vasp
# Ensure template assets are accessible
ls assets/templates/
# Validate documentation hooks
cat references/input-set-manual.md
HPC VASP Data Schema & Taxonomy
The skill organizes data around a structured hierarchy of inputs and outputs:
| File Type | Description | Role in Workflow |
|---|---|---|
| INCAR | Configuration Tags | Defines electronic and ionic convergence parameters. |
| POSCAR | Geometry Data | Defines the unit cell and atomic coordinates. |
| KPOINTS | Mesh Settings | Defines the Brillouin zone sampling density. |
| POTCAR | Pseudopotentials | Contains the atomic potential data for each species. |
| Output Logs | OUTCAR/OSZICAR | Used for automated error parsing and stage verification. |
| Handoff Files | CHGCAR/WAVECAR | Essential for restarting or transitioning between stages. |
HPC VASP Advanced Features
- Multi-stage handoff matrix for seamless transitions between relaxation, static, and DOS calculations.
- Automated SLURM script generation for high-performance cluster job submission.
- Dynamic INCAR tag adjustment based on metallic vs. insulating system behavior.
- Structured SCF and ionic relaxation failure signatures for rapid automated debugging.
- Integration with Openclaw Skills for standardized AI-driven research protocols.
SKILL.md
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