A specialized technical suite for generating conservative CP2K input files and cross-converting them between major quantum chemistry codes.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cp2k-crosscode-input-studio
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 cp2k-crosscode-input-studio 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 CP2K Cross-Code Input Studio?
The CP2K Cross-Code Input Studio is an advanced utility designed to bridge the gap between natural language research requests and runnable computational chemistry inputs. It focuses on producing practical, high-quality CP2K drafts that prioritize explicit assumptions and conservative defaults over false certainty. By utilizing a normalized job specification, it ensures that materials simulation setups are consistent, transparent, and ready for expert review.
Beyond basic generation, this Openclaw Skills powered tool excels at cross-code translation. It can derive functional input drafts for Gaussian, VASP, ORCA, and Quantum ESPRESSO based on a core CP2K configuration. This makes it an indispensable asset for researchers working in multi-code environments who need to maintain workflow parity across different simulation engines.
CP2K Cross-Code Input Studio Use Cases
- Creating single-point energy and geometry optimization drafts from molecular XYZ files.
- Setting up periodic cell optimization for materials science research.
- Generating short test Molecular Dynamics (MD) inputs with safe, conservative defaults.
- Converting existing CP2K input files into VASP, ORCA, or Quantum ESPRESSO formats.
- Automating the normalization of raw research requests into structured JSON job specifications.
How CP2K Cross-Code Input Studio Works
- The skill analyzes the user's request and any provided structure files against internal design rules and schemas.
- It normalizes the request into a standard JSON contract to ensure all physical parameters are logically mapped.
- It references specific modules for task routing, basis/potential choices (KIND), and k-point settings based on system periodicity.
- Local Python scripts are triggered to deterministically render the CP2K input draft and any requested cross-code translations.
- A comprehensive package is generated, including the normalized specification, the runnable input file, and a detailed technical report.
CP2K Cross-Code Input Studio Setup
To integrate this capability into your workflow, ensure you have the Openclaw Skills environment configured and follow these steps:
# Navigate to your agent's skill directory
cd skills/cp2k-crosscode-input-studio
# Ensure Python dependencies for the bundled scripts are met
pip install -r requirements.txt
# The skill is now ready to process .inp and .xyz files via your AI agent.
CP2K Cross-Code Input Studio Data Schema & Taxonomy
The skill generates a consistent three-file bundle to maintain a clear audit trail of the simulation setup:
| File | Description |
|---|---|
normalized.json |
The intermediate job specification containing the interpreted physics and settings. |
job.inp |
The primary CP2K input file generated using conservative defaults. |
report.md |
A technical summary highlighting periodicity assumptions, applied defaults, and warnings. |
[code]_draft.inp |
(Optional) Translated input files for Gaussian, VASP, ORCA, or Quantum ESPRESSO. |
CP2K Cross-Code Input Studio Advanced Features
- Multi-code translation support for transitioning between CP2K, VASP, ORCA, and Quantum ESPRESSO.
- Bundled Python pipeline (
generate_cp2k_bundle.py) for one-shot automation of the entire drafting process. - Heuristic k-point generation and conservative periodic-material drafting for complex crystal systems.
- Ambiguity policy enforcement that flags under-specified requests (like uncertain spin states) for manual review rather than guessing.
SKILL.md
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