docs: add precipitation reviewer path

projects/precipitation-anomaly-diagnostics/docs/reviewer-path.md

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+# Reviewer path
+
+This reviewer path is a lightweight guide for reading the compact precipitation diagnostics mini-lab as a public scientific-computing workflow. It is intended to make the project easy to evaluate without presenting it as the repository's flagship release surface.
+
+## 30-second orientation
+
+Read the project README first. Confirm this is a sanitized scientific-computing mini-lab, not a course archive and not an operational forecast system.
+
+Check the first screen for the project role:
+
+- compact reviewer-facing precipitation diagnostics mini-lab
+- supporting scientific-data project inside `scientific-computing-toolkit`
+- not part of the `sbom-diff-and-risk` release surface
+- not a separate meteorology portfolio
+
+## 5-minute workflow review
+
+Inspect:
+
+- [`docs/methodology.md`](methodology.md)
+- [`docs/inference-framework.md`](inference-framework.md)
+- [`reports/example-report.md`](../reports/example-report.md)
+- [`examples/sample_metadata.json`](../examples/sample_metadata.json)
+
+This pass should answer whether the workflow is reproducible, scientifically bounded, and clear enough for a reviewer to follow from input assumptions to derived outputs.
+
+## 15-minute reproducibility review
+
+Run:
+
+```bash
+python -m pip install -e .[dev]
+python -m pytest
+python scripts/run_preprocessing.py --help
+python scripts/run_eof_analysis.py --help
+python scripts/run_composite_analysis.py --help
+```
+
+This review does not require raw climate datasets. The help commands verify that the public scripts expose their configuration surface without relying on local machine paths or restricted data files.
+
+## Boundaries
+
+Read:
+
+- [`docs/data-policy.md`](data-policy.md)
+- [`PUBLICATION_BOUNDARIES.md`](../PUBLICATION_BOUNDARIES.md)
+
+The project should be read as a reproducible spatiotemporal diagnostics module: it demonstrates data-policy discipline, analysis structure, and reviewer-friendly interpretation while keeping raw datasets, institutional course artifacts, personal identifiers, and local machine details out of the public repository.