| name | s2s-model-builder |
| description | End-to-end builder for AI-based Subseasonal-to-Seasonal (S2S) forecasting systems. Generates runnable PyTorch code for FuXi-style, FengWu-style, and AIFS-inspired models including CRPS-based probabilistic training. |
| metadata | {"clawdbot":{"emoji":"🌎","requires":{"env":[]},"files":[]}} |
S2S Model Builder (Subseasonal-to-Seasonal Forecasting)
This skill actively helps you design, implement, and train S2S forecasting models from scratch.
It generates:
- PyTorch model architectures
- Training loops
- CRPS loss implementations
- Data preprocessing pipelines (ERA5-style)
- Evaluation scripts
- Multi-GPU training configurations
- Inference pipelines
Supported paradigms include:
- FuXi-style transformer architectures
- FengWu-style Earth system transformers
- AIFS-inspired probabilistic models
- Ensemble neural forecasting
- Multi-lead-time forecasting heads
What This Skill Can Build
1. Model Architecture Code
- 3D spatiotemporal transformers
- Global grid attention models
- Multi-variable input pipelines (Z500, T2M, winds, SST)
- Lead-time conditioned decoders
- Ensemble output heads
2. Training Infrastructure
- PyTorch training loops
- Distributed training (FSDP-ready structure)
- Mixed precision support
- Gradient accumulation
- Checkpoint saving
3. Probabilistic Forecasting
- CRPS loss (Gaussian & ensemble forms)
- Quantile regression heads
- Spread-skill diagnostics
- Reliability calibration utilities
4. Evaluation Code
- CRPS computation
- ACC metric implementation
- RMSE across forecast horizons
- Skill vs climatology baseline
5. Deployment-Ready Inference
- Batched inference scripts
- Memory-optimized forward passes
- Model export patterns
Example Prompts
- “Generate a FuXi-style transformer in PyTorch for 30-day Z500 forecasting.”
- “Build a CRPS loss function for ensemble S2S outputs.”
- “Create a full ERA5 training pipeline scaffold.”
- “Design a multi-lead-time S2S forecasting head.”
- “Implement distributed training for global 1° resolution data.”
External Endpoints
This skill does not call external APIs.
| Endpoint | Purpose | Data Sent |
|---|
| None | N/A | None |
All generated code runs locally within the user’s environment.
Security & Privacy
- No external API calls
- No automatic dataset downloads
- No remote execution
- No hidden scripts
- All code is generated transparently
Users are responsible for lawful dataset usage (e.g., ERA5 licensing).
Model Invocation Note
This skill may be automatically invoked when user queries involve:
- Building S2S models
- FuXi / FengWu / AIFS implementations
- CRPS training
- AI weather model architecture
- ERA5 training pipelines
Users may opt out by disabling the skill.
Trust Statement
By using this skill, you acknowledge it generates code for AI-based climate forecasting systems. No data is transmitted externally. All execution occurs within your own environment.
Version
v1.0.0
Last updated: Feb 16, 2026
