earth2studio-create-prognostic

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](#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

Never read evals/targets/ — grader references only.

Reference Files

Load on demand during the matching step:

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:

1. init 2. inputcoords 3. outputcoords (@batch_coords)
2. loaddefaultpackage 5. load_model 6. to (optional)
3. Private methods 8. call (@batchfunc) 9. default_generator
4. 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 inputcoords or outputcoords
• Map variables to E2STUDIO_VOCAB (282 entries in earth2studio/lexicon/base.py)

outputcoords: Use handshakedim/handshakecoords for input validation, then increment leadtime. 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: @batchfunc decorated, shape (batch, time, leadtime, var, lat, lon). Reshape to model format → call model → reshape back.

createiterator: MUST yield initial condition first (step 0). Use fronthook/rear_hook for perturbation injection.

Step 6 — Implement Model Loading

loaddefaultpackage: Lock HuggingFace URLs: hf://org/repo@commit

loadmodel: Use package.resolve(), maplocation="cpu", eval() mode, decorate with @checkoptionaldependencies().

Step 7 — Write Tests

File: test/models/px/test_<name>.py

Required tests:

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

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 fronthook()/rearhook() in defaultgenerator
• 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/