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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.

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$ npx -y skills add NVIDIA/skills --skill earth2studio-create-prognostic --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/earth2studio-create-prognostic

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

earth2studio-create-prognostic.SKILL.md
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](#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) 4. `load_default_package` 5. `load_model` 6. `to` (optional) 7. Private methods 8. `__call__` (@batch_func) 9. `_default_generator` 10. `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_<na
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