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BytesAgain › Skills › S2S Forecasting Expert (FuXi, FengWu, AIFS)
🦀 ClawHub

S2S Forecasting Expert (FuXi, FengWu, AIFS)

by @manmeet3591

End-to-end builder for AI-based Subseasonal-to-Seasonal (S2S) forecasting systems. Generates runnable PyTorch code for FuXi-style, FengWu-style, and AIFS-ins...

Versionv1.0.1
Downloads945
#clawhub
TERMINAL
clawhub install s2s-forecasting-expert

📖 About This Skill

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