
Earth2Studio Prognostic
OfficialFreeCreate wrappers for prognostic weather models in Earth2Studio.
Free · Opens the source repo
What Earth2Studio Prognostic does
The Earth2Studio Prognostic skill is designed for developers and data scientists who need to create model wrappers for prognostic weather forecasting models within the Earth2Studio environment. This skill facilitates the integration of various third-party machine learning models that predict future states based on initial conditions, allowing users to leverage advanced weather prediction capabilities in their applications. By following a structured workflow, users can efficiently implement the necessary components to connect their models with Earth2Studio.
To get started, users are guided through a series of steps that include creating model files, implementing coordinate systems, and defining the forward pass for model execution. The skill emphasizes the importance of using the uv run command to ensure all dependencies are correctly managed, which is crucial for successful model operation. Reference files are provided to assist in the setup and implementation process, ensuring that users have the necessary templates and guidelines at their disposal.
This skill is particularly suited for those working in meteorology, climate science, or any field that requires time-stepping forecasts. By enabling the creation of prognostic model wrappers, it streamlines the development process and enhances the ability to produce accurate weather forecasts. Users can also benefit from the detailed testing framework included in the skill, which ensures that the implemented models perform as expected.
Overall, the Earth2Studio Prognostic skill is a valuable tool for developers looking to integrate sophisticated weather prediction models into their workflows, providing a clear pathway for implementation and testing.
When to use it
Use this skill when you need to implement prognostic models in Earth2Studio for time-stepping forecasts.
When not to use it
This skill is not suitable for diagnostic models or for tasks outside of creating prognostic wrappers.
What you can build with it
Integrating a New Weather Model
You need to wrap a new third-party weather model for use in Earth2Studio, following the structured steps provided by this skill.
Testing Model Performance
After implementing a prognostic model, you can utilize the testing framework included to ensure your model performs as expected.
Collaborating on Weather Forecasting Projects
When working in a team, this skill provides a standardized approach to creating and integrating models, facilitating collaboration.
How to install Earth2Studio Prognostic
View source1. Install with the skills CLI
npx skills add nvidia/skills/earth2studio-create-prognostic --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by nvidiaQuick Start Checklist
Do these steps IN ORDER. Do not skip any step.
- Read this SKILL.md completely first
- Get reference script (Step 0)
- Create
earth2studio/models/px/<name>.pywith triple inheritance - Create
test/models/px/test_<name>.pywith mock tests - Run:
uv run pytest test/models/px/test_<name>.py -v - Add/update model extra, install docs, API docs, and changelog (Steps 1-2, 9)
- Run:
make format && make lint
⚠️ CRITICAL: Always use
uv runfor Python commands:
- ✅
uv run pytest .../uv run python ...- ❌
pytest .../python ...(missing dependencies)Stuck or wrong output: Do not keep retrying the same fix. Follow Self-Improvement to patch this skill before continuing.
Purpose
Implement a prognostic model wrapper connecting third-party ML weather models to Earth2Studio. Prognostic models time-integrate forward—given initial state, they predict future states by stepping through time (e.g., 6-hour increments).
Workspace
| Context | Location |
|---|---|
| Harbor eval | Write to /workspace/output/earth2studio/models/px/... |
Harbor + --copy-repo | Full checkout at /workspace/repo |
| Local clone | Directory with pyproject.toml |
Never read evals/targets/ — grader references only.
Reference Files
Load on demand during the matching step:
| File | Content | Load at |
|---|---|---|
references/skeleton-template.py | Full model skeleton with FILL comments | Steps 3–6 |
references/method-templates.py | Canonical method implementations | Steps 4–6 |
references/testing-guide.py | Test skeleton and mock patterns | Step 7 |
references/validation-guide.md | Comparison scripts, PR, code review | Steps 10–11 |
Workflow Steps
Step 0 — Get Reference Script
If $ARGUMENTS provided, use it. Otherwise ask:
Please provide a reference inference script URL/path.
Step 1 — Analyze & Propose Dependencies
Analyze: packages, architecture, I/O shapes, time step, resolution, checkpoint.
Propose pyproject.toml group (alphabetical, add to all). Every
prognostic model must have an optional dependency extra, even when no packages
are required:
model-name = ["package1>=version", "package2"]
# or, when no additional packages are required:
model-name = []
[CONFIRM] Present dependencies and ask user to approve.
Step 2 — Add Dependencies
Edit pyproject.toml: add the model extra alphabetically, even if it is
empty, and update the all aggregate.
Step 3 — Create Model File
File: earth2studio/models/px/<lowercase>.py
Required inheritance (all three):
class ModelName(torch.nn.Module, AutoModelMixin, PrognosticMixin):
Required imports:
import numpy as np
import torch
from earth2studio.models.auto import AutoModelMixin, Package
from earth2studio.models.batch import batch_coords, batch_func
from earth2studio.models.px.base import PrognosticMixin
from earth2studio.models.utils import create_coords_from_lat_lon, handshake_dim
from earth2studio.lexicon import E2STUDIO_VOCAB
from earth2studio.utils import check_optional_dependencies
from loguru import logger
SPDX header (required at top of every .py file):
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: Apache-2.0
Canonical method order:
__init__2.input_coords3.output_coords(@batch_coords)load_default_package5.load_model6.to(optional)- Private methods 8.
__call__(@batch_func) 9._default_generator create_iterator
Step 4 — Implement Coordinates
input_coords rules:
batch:np.empty(0)time:np.empty(0)(dynamic)lead_time: starts atnp.timedelta64(0, "h")lat: 90 to -90 (north to south); this is the public Earth2Studio convention even if the source model uses the opposite orderlon: 0 to 360- If a checkpoint/model core expects south-to-north latitude, flip tensors internally before/after the core model; do not expose flipped latitude in
input_coordsoroutput_coords - Map variables to
E2STUDIO_VOCAB(282 entries inearth2studio/lexicon/base.py)
output_coords: Use handshake_dim/handshake_coords for input validation, then increment lead_time. Prefer a shared coordinate-check helper and call it from output_coords, __call__, and iterator setup before model execution.
Step 5 — Implement Forward Pass
__call__: @batch_func decorated, shape (batch, time, lead_time, var, lat, lon).
Reshape to model format → call model → reshape back.
create_iterator: MUST yield initial condition first (step 0).
Use front_hook/rear_hook for perturbation injection.
Step 6 — Implement Model Loading
load_default_package: Lock HuggingFace URLs: hf://org/repo@commit
load_model: Use package.resolve(), map_location="cpu", eval() mode,
decorate with @check_optional_dependencies().
Step 7 — Write Tests
File: test/models/px/test_<name>.py
Required tests:
| Function | Purpose |
|---|---|
test_<model>_call | Single forward pass (parametrize device/time) |
test_<model>_iter | Iterator produces sequence |
test_<model>_exceptions | Invalid coords raise errors |
test_<model>_package | Real weights (@pytest.mark.package) |
Create PhooModelName dummy matching interface for mock tests.
Run tests:
uv run pytest test/models/px/test_<name>.py -m "not package" -v
uv run pytest test/models/px/test_<name>.py::test_<model>_package --package -v
Do not omit the package test. If arbitrary random inputs are not physically valid for the real checkpoint, use a stable model-appropriate synthetic input while still loading real weights and running a forward pass.
Step 8 — Register Model (if requested)
- Add to
earth2studio/models/px/__init__.py(alphabetical) - Verify deps in pyproject.toml
Step 9 — Documentation
- Add to
docs/modules/models_px.rst(alphabetical). This is required for every new prognostic model so the API docs include the generated page. - Add to
docs/userguide/about/install.md(alphabetical tab) for the model extra, even when the extra is empty. Include model-specific notes plus bothpip install earth2studio[model-name]anduv add earth2studio --extra model-nameinstructions. - Update
CHANGELOG.mdunder### Added. This is required for every new prognostic model.
Format and lint:
make format && make lint && make license
Step 10 - Validation (if requested)
Follow references/validation-guide.md. Create uncommitted vanilla, E2S,
comparison, and sanity-check scripts; do not commit generated outputs or images.
Use PR-safe placeholders for plots so the user can upload images manually.
[CONFIRM] User must visually inspect plots before proceeding.
Step 11 - PR (if requested)
Follow references/validation-guide.md and use:
references/pr-body-template.mdreferences/pr-comment-template.md
Before creating the PR, verify pyproject.toml has the model extra, the
all extra includes it, install docs include both pip and uv commands, and
docs/modules/models_px.rst plus CHANGELOG.md are updated.
Do not include machine names, absolute paths, device inventory, or uploaded image links in PR text. Use plot placeholders instead.
Examples
Simple Identity Model
User: Create IdentityModel - returns input unchanged, 6h step, 181x360, vars: t2m, u10m, v10m, msl
Agent: [reads SKILL.md, creates identity.py with triple inheritance,
creates test_identity.py, runs pytest, runs make format && lint]
External Model (Pangu)
User: Add Pangu-Weather wrapper
GitHub: https://github.com/198808xc/Pangu-Weather
Agent: [reads SKILL.md, fetches inference.py, creates pangu.py,
creates test_pangu.py, runs pytest]
Key Patterns
Coordinate Template
@property
def input_coords(self) -> CoordSystem:
return CoordSystem({
"batch": np.empty(0),
"time": np.empty(0),
"lead_time": np.array([np.timedelta64(0, "h")]),
"variable": np.array(["t2m", "u10m", ...]),
# Public Earth2Studio convention is north-to-south latitude.
"lat": np.linspace(90, -90, 181),
"lon": np.linspace(0, 359, 360),
})
@batch_coords()
def output_coords(self, input_coords: CoordSystem) -> CoordSystem:
output = input_coords.copy()
output["lead_time"] = input_coords["lead_time"] + np.timedelta64(6, "h")
return output
Iterator Template
def create_iterator(self, x, coords):
yield x, coords # Initial condition (step 0)
while True:
x, coords = self.front_hook(x, coords)
x, coords = self(x, coords)
x, coords = self.rear_hook(x, coords)
yield x, coords
Troubleshooting
| Error | Solution |
|---|---|
OptionalDependencyFailure | uv add --optional <group> <pkg> |
| Coordinate handshake fails | Check handshake_dim indices match dim position |
| Iterator wrong shapes | Debug reshape logic with random input |
ModuleNotFoundError: pytest | Use uv run pytest not pytest |
Reminders
DO:
- Use
uv run pythonfor ALL Python commands - Use
loguru.logger, neverprint() - Inherit
torch.nn.Module + AutoModelMixin + PrognosticMixin - Yield initial condition first in
create_iterator - Use
front_hook()/rear_hook()in_default_generator - Include SPDX header in every .py file
DON'T:
- Create general base classes for reuse
- Commit API keys or comparison scripts
- Read from
evals/targets/
Frequently asked questions about Earth2Studio Prognostic
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