Skill: manmeet3591/s2s-forecasting-expert (version 1.0.1)

--- 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

path: README.md
content: # 🌎 S2S Model Builder

An end-to-end builder skill for AI-based Subseasonal-to-Seasonal (S2S) forecasting systems.

Generates runnable PyTorch code for:

- FuXi-style transformers
- FengWu-style Earth models
- AIFS-inspired probabilistic systems
- CRPS-based training
- Multi-lead-time forecasting

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## What This Skill Does

- Scaffolds full model architectures
- Generates training loops
- Implements CRPS
- Creates evaluation pipelines
- Designs distributed training setups

This skill generates transparent, local code only. No external APIs are called.

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## Intended Users

- Climate AI researchers
- Earth system ML engineers
- Research labs building S2S systems
- Advanced ML practitioners

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## Installation

Upload:

SKILL.md  
README.md  

to ClawHub via Publish Skill.

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## Version

1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)