A Hermes Agent plugin for building ML-ready weather training datasets. Powered by wxtrain, an all-Rust end-to-end pipeline.
Ask Hermes to build training datasets from operational weather models:
- "Plan a severe weather dataset for a Swin transformer" → 25-channel training spec with export format, loss function, and model recipe
- "Fetch HRRR CAPE data" → downloads via byte-range .idx subsetting (~500KB instead of 125MB)
- "Build training arrays from this GRIB file" → NPY arrays + preview PNGs + manifests
- "What's the theta-e at 30C, 20C dewpoint, 850mb?" → instant thermodynamic calculation
| Tool | Description |
|---|---|
wxt_models |
List supported weather models and sources |
wxt_fetch |
Download GRIB fields via byte-range .idx |
wxt_scan |
List all messages in a GRIB file |
wxt_decode |
Decode a GRIB message — stats, grid dimensions, variable info |
wxt_calc |
Thermodynamic calculations (theta, theta_e, RH) |
wxt_render |
Render a GRIB field as PNG |
wxt_plan |
Plan a training dataset for an ML architecture |
wxt_build |
Build training arrays from GRIB files |
wxt_plan (architecture + task → channel spec + export format)
↓
wxt_fetch (NOAA/ECMWF → byte-range GRIB download)
↓
wxt_build (decode → compute derived fields → export NPY/Parquet/WebDataset)
wxt_plan knows how to prepare data for different ML architectures:
| Architecture | Channels | Format | Loss |
|---|---|---|---|
| Swin Transformer | 25 (surface + pressure + severe) | WebDataset (96 shards) | smooth_l1 |
| Diffusion | 13 (surface + pressure) | WebDataset | noise_prediction_mse |
| Classical ML | 22 (surface + severe + tabular) | Parquet | MSE/BCE |
| Graph Network | 13 (surface + pressure) | WebDataset | smooth_l1 |
| Profile | Fields |
|---|---|
surface_core |
t2m, d2m, u10, v10, mslp |
pressure_core |
z500, t850, u850, v850, vort500, div500, theta850, tadv850 |
severe_diagnostics |
sbcape, sbcin, mlcape, mlcin, mucape, mucin, srh01, srh03, shear06, stp, scp, pwat |
radar_core |
reflectivity, velocity, spectrum_width |
thermodynamic_profiles |
theta_e, wet_bulb, lcl_height, lfc_height, dcape |
tabular_stats |
channel_min/mean/max, valid_hour_sin/cos |
| Model | Source | Resolution | Auth |
|---|---|---|---|
| HRRR | NOAA | 3km CONUS | None |
| GFS | NOAA | 0.25° global | None |
| NAM | NOAA | 12km CONUS | None |
| RAP | NOAA | 13km CONUS | None |
| ECMWF IFS | Open Data | 0.25° global | None |
| ERA5 | CDS API | 0.25° reanalysis | CDS key |
100% Rust core. No Python, no C, no eccodes, no Fortran.
wxtrain binary (22,488 lines of Rust)
├── wx-fetch — download planning, byte-range fetch, CDS auth
├── wx-grib — native GRIB1/2 decode (JPEG2000, CCSDS/AEC)
├── wx-calc — 100+ met calculations (MetPy parity verified)
├── wx-radar — NEXRAD ingest
├── wx-render — PNG rendering
├── wx-train — dataset planning & assembly
├── wx-export — NPY, Parquet, WebDataset, Zarr
└── wx-types — shared domain model
# 1. Build wxtrain
git clone https://github.com/FahrenheitResearch/wxtrain
cd wxtrain && cargo build --release
# 2. Copy plugin to Hermes
cp -r wxtrain-plugin ~/.hermes/plugins/wxtrain
# 3. (Optional) set binary path
export WXTRAIN_PATH=/path/to/wxtrain/target/release/wxtrain| Format | Use Case |
|---|---|
| NPY | Quick prototyping, single arrays |
| Parquet | Tabular ML (XGBoost, LightGBM) |
| WebDataset | Distributed training (PyTorch) |
| Zarr | Cloud-native, chunked arrays |
This plugin pairs with the Hermes Weather Plugin — use the weather plugin to explore and visualize data, then use wxtrain to build training datasets from the same models.
- wxtrain engine: Built with Codex
- Meteorological calculations: Verified against MetPy test suites
- Plugin platform: Hermes Agent by Nous Research
