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-prognosticInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
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.
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 foundOur 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
All 4 of these similar skills score higher than earth2studio-create-prognostic; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| earth2studio-create-prognostic (this skill)by NVIDIA | 94 | 3.4k | 5d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.0k | 13d ago | CLAUDE.md |
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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.
Skill content
View source on GitHubname: 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>.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/
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
