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Create Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation.
$ npx -y skills add NVIDIA/skills --skill earth2studio-create-prognostic --agent claude-codeHow it fires
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/earth2studio-create-prognosticContext preview
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Create Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation.
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)
**Do these steps IN ORDER. Do not skip any step.**
> **⚠️ 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.
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).
| 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.
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 |
---
If `$ARGUMENTS` provided, use it. Otherwise ask: > Please provide a reference inference script URL/path.
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.
Edit `pyproject.toml`: add the model extra alphabetically, even if it is empty, and update the `all` aggregate.
**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) 4. `load_default_package` 5. `load_model` 6. `to` (optional) 7. Private methods 8. `__call__` (@batch_func) 9. `_default_generator` 10. `create_iterator`
**input_coords rules:**
**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.
**`__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.
**`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()`.
**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_<na
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