Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
S2S Forecasting Expert (FuXi, FengWu, AIFS)
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...
skill openclawclawhub Free
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...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- manmeet3591
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
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. EndpointPurposeData SentNoneN/ANone 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md