Earth2studio Create Prognostic

NVIDIAOfficialCreated by NVIDIA Earth-2 Team

Skill
Workflow
Software development
Apache-2.0
Medium risk
Has scripts

From the author

Create Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation.

— the description by NVIDIA Earth-2 Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured, step-by-step procedure for implementing prognostic (time-stepping) model wrappers within the Earth2Studio ecosystem. It guides developers through dependency management, model file creation using required inheritance patterns, coordinate system implementation, forward pass logic, and rigorous testing. The workflow ensures consistency across model integrations by enforcing specific method order, SPDX headers, and documentation standards, while providing templates for pull requests and validation comparisons against vanilla implementations.

What it does

Generates prognostic model wrappers for Earth2Studio.
Manages Python dependencies and model-specific extras in pyproject.toml.
Enforces standard inheritance and method structures for model classes.
Provides templates for coordinate systems and iterative forecasting logic.
Facilitates automated testing and validation against reference implementations.
Standardizes documentation and changelog updates for new model integrations.

When to use it

When integrating a new third-party ML weather model into Earth2Studio.
When you need to ensure a model wrapper adheres to the framework's architectural standards.
When you need to validate a new model wrapper against a vanilla reference implementation.
When preparing a pull request for a new prognostic model contribution.

Advantages

Ensures architectural consistency across all prognostic models.
Reduces boilerplate through provided templates and skeletons.
Standardizes testing and validation procedures.
Simplifies dependency management and documentation requirements.

Usage examples

“Create IdentityModel - returns input unchanged, 6h step, 181x360, vars: t2m, u10m, v10m, msl”: The agent generates the identity.py file with the required triple inheritance, implements the coordinate system, creates the corresponding test file, and runs the full verification suite.
“Add Pangu-Weather wrapper from https://github.com/198808xc/Pangu-Weather”: The agent fetches the inference script, sets up the model wrapper with appropriate dependencies, implements the forward pass and iterator, and executes the test suite.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/earth2studio-create-prognostic/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip earth2studio-create-prognostic.zip -d .agents/skills

Documentation by NVIDIA Earth-2 Team

Quick 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>.py with triple inheritance
[ ] Create test/models/px/test_<name>.py with 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 run for 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_coords 3. output_coords (@batch_coords)
load_default_package 5. load_model 6. 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 at np.timedelta64(0, "h")
lat: 90 to -90 (north to south); this is the public Earth2Studio convention even if the source model uses the opposite order
lon: 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_coords or output_coords
Map variables to E2STUDIO_VOCAB (282 entries in earth2studio/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 both pip install earth2studio[model-name] and uv add earth2studio --extra model-name instructions.
Update CHANGELOG.md under ### 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.md
references/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 python for ALL Python commands
Use loguru.logger, never print()
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/

Files

ATTRIBUTION.mdAdded by AutoGineer961 B
BENCHMARK.md4.3 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md10.0 KiB
evals/config.yml1.1 KiB
evals/environment/Dockerfile1.4 KiB
evals/environment/setup/bootstrap.shScript1.2 KiB
evals/evals.json2.4 KiB
evals/targets/eval_1_target.pyScript6.6 KiB
evals/targets/eval_2_target.pyScript8.3 KiB
evals/targets/test/eval_1_test_target.pyScript5.6 KiB
evals/targets/test/eval_2_test_target.pyScript6.4 KiB
references/method-templates.pyScript12.6 KiB
references/pr-body-template.md2.4 KiB
references/pr-comment-template.md2.2 KiB
references/skeleton-template.pyScript15.2 KiB
references/testing-guide.pyScript13.7 KiB
references/validation-guide.md4.8 KiB
skill-card.md2.4 KiB
skill.oms.sig7.1 KiB

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Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs an API key, Needs Python

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Attribution and licence

Created by

NVIDIA Earth-2 Team

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/earth2studio-create-prognostic · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by NVIDIA Earth-2 Team or NVIDIA.

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