A comprehensive builder for generating end-to-end AI forecasting systems and PyTorch architectures for subseasonal-to-seasonal weather predictions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install s2s-forecasting-expert
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 s2s-forecasting-expert 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 S2S Model Builder?
The S2S Model Builder is a powerful addition to Openclaw Skills designed to streamline the creation of AI-driven climate and weather forecasting models. It serves as a specialized developer assistant that generates high-fidelity PyTorch code for state-of-the-art architectures, including FuXi-style transformers and FengWu-style Earth system models. By automating the boilerplate of complex spatiotemporal transformers and training loops, this tool allows researchers to rapidly prototype and deploy sophisticated S2S systems.
This skill focuses on bridging the gap between meteorological theory and deep learning implementation. Using Openclaw Skills, developers can instantly produce data preprocessing pipelines for ERA5-style datasets, implement probabilistic training using CRPS loss, and configure distributed training for global grid data. It ensures that all generated code is transparent, secure, and ready for local execution within a professional data science environment.
S2S Model Builder Use Cases
- Designing 3D spatiotemporal transformers for global grid weather attention models.
- Generating CRPS-based probabilistic training loops for ensemble forecasting.
- Creating ERA5-style data preprocessing pipelines for multi-variable inputs like Z500 and T2M.
- Implementing lead-time conditioned decoders for long-range forecast horizons.
- Building distributed training configurations for high-resolution global climate datasets.
How S2S Model Builder Works
- The user provides a technical prompt regarding an S2S forecasting requirement, such as building a FuXi-style model.
- The skill analyzes the request and references established AI weather forecasting paradigms and PyTorch best practices.
- It generates complete, runnable Python scripts including model architecture, loss functions, and evaluation metrics.
- The user integrates the generated code into their local workflow for model training and inference using their own datasets.
S2S Model Builder Setup
To utilize this skill, ensure your environment is configured for deep learning and climate data processing. No external API keys are required as all code is generated locally via Openclaw Skills.
# Install core dependencies for generated S2S models
pip install torch torchvision xarray dask netCDF4 numpy
S2S Model Builder Data Schema & Taxonomy
The skill generates structured code that organizes climate data and model outputs according to standard meteorology formats:
| Component | Organization Method | Details |
|---|---|---|
| Architectures | Modular PyTorch Classes | Includes Global Grid Attention and Spatiotemporal Transformers |
| Preprocessing | Xarray/Dask Pipelines | Handles ERA5-style variables (Z500, T2M, SST, Winds) |
| Training | Distributed Configs | Multi-GPU and FSDP-ready training loops |
| Metrics | Diagnostic Modules | ACC, RMSE, and CRPS spread-skill diagnostics |
S2S Model Builder Advanced Features
- Support for ensemble neural forecasting with multi-lead-time forecasting heads.
- Implementation of Gaussian and ensemble-based Continuous Ranked Probability Score (CRPS) loss.
- Advanced memory-optimized forward passes for handling high-resolution global grids.
- Built-in reliability calibration utilities and spread-skill diagnostic tools.
- Compatibility with AIFS-inspired probabilistic models and quantile regression heads.
SKILL.md
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