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earth2studio-create-prognostic

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

Install / Use

npx skills add NVIDIA/skills --skill earth2studio-create-prognostic

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

94/100

Supported Platforms

Universal

Our assessment of earth2studio-create-prognostic

earth2studio-create-prognostic scores 94/100 on our quality scale, 438th of 3,356 Development & Engineering skills we index (top 14%).

Its SKILL.md is 10.0 KB long, well organised into 28 sections with 10 code examples: a thorough specification that gives an agent plenty to work with.

With 3,421 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 5 days ago, so earth2studio-create-prognostic is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-29. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

earth2studio-create-prognostic compared with similar skills

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Frequently asked questions

How do I install earth2studio-create-prognostic?
Run npx skills add NVIDIA/skills --skill earth2studio-create-prognostic. The install tabs above show the steps for each supported agent.
Which AI agents does earth2studio-create-prognostic work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is earth2studio-create-prognostic safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is earth2studio-create-prognostic still maintained?
The repository was last updated 5 days ago, so earth2studio-create-prognostic is actively maintained.

name: earth2studio-create-prognostic version: 0.16.0 license: Apache-2.0 metadata: author: NVIDIA Earth-2 Team agent-skills@nvidia.com tags: [earth2studio, prognostic-model, python] description: > Create Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation. argument-hint: URL or local path to reference inference script (optional)

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:

  1. __init__ 2. input_coords 3. output_coords (@batch_coords)
  2. load_default_package 5. load_model 6. to (optional)
  3. Private methods 8. __call__ (@batch_func) 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 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/

Related Skills

View on GitHub
GitHub Stars3.4k
CategoryDevelopment
Updated5d ago
Forks412

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions