or, when the packaged diagnostic needs no extra runtime packages:

Quick Start Checklist

Do these steps in order. Do not skip ahead. Before editing, read this SKILL.md and load the relevant reference files for the model type. After implementation, run the focused pytest command before saying tests pass. If tests cannot run, report the exact command and failure instead of claiming success.

• Read this SKILL.md completely first
• Get the reference script, repo, paper, or model documentation (Step 0)
• Classify the diagnostic as simple, AutoModel, or generative (Step 1)
• Propose dependency extras before editing dependency files (Step 1)
• Create earth2studio/models/dx/<name>.py with diagnostic-only APIs
• Create test/models/dx/test_<name>.py with mock tests
• Run: uv run pytest test/models/dx/test_<name>.py -m "not package" -v
• Add/update model extra, install docs, API docs, and changelog when required
• Run: make format && make lint && make license

Critical command rule: always use uv run for Python commands:

• Use uv run pytest ... and uv run python ...
• Do not use bare pytest or python in repo workflows

If the generated model is wrong, do not keep retrying the same fix. Follow Self-Improvement, patch this skill or its references, then continue with the corrected workflow.

Purpose

Implement a diagnostic model wrapper connecting third-party or derived ML transforms to Earth2Studio. Diagnostic models transform data at a single time point: input fields in, output fields out, no forecast integration.

Prerequisites

• Earth2Studio installed via uv with dev dependencies (uv sync --all-extras)
• Python 3.10+ environment
• Reference inference script, repo, paper, or model documentation
• Checkpoint source and license information for packaged models