Scaffold Reverso MVP pipeline structure

Set up the project skeleton per PLAN.md §4:
- src/ package: identifiers, disease, drugs, scoring, provenance
  with pydantic schemas and confidence-tier logic (working);
  data-pull/compute functions stubbed per their build week
- 5 starter notebooks (01-05) with PLAN-referenced steps
- tests/test_scoring.py: tier-assignment tests pass; scoring
  reference test xfail until Week 3
- docs/: recovery_test_report, data_sources, known_limitations skeletons
- pyproject.toml (requires-python >=3.11,<3.14), .gitignore, README
- data/ tree preserved via .gitkeep; raw/processed/results gitignored

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

tests/test_scoring.py

@@ -0,0 +1,65 @@
+"""Tests for the matching engine and provenance logic.
+
+The headline test (PLAN.md §6, Week 3 task 4) verifies connectivity scoring against a known
+reference within tolerance; it is marked xfail until the scorer is implemented in Week 3.
+
+The tier-assignment tests run today — they pin the rules from PLAN.md §3 so the most
+commercially important design decision can't silently drift.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from src.provenance import ConfidenceTier, assign_tier
+
+
+class TestAssignTier:
+    """Tier rules from PLAN.md §3."""
+
+    def test_measured_large_n_peer_reviewed_multi_source_is_tier_a(self):
+        assert (
+            assign_tier(
+                is_measured=True,
+                n_per_group=27,
+                peer_reviewed=True,
+                single_source=False,
+            )
+            == ConfidenceTier.A
+        )
+
+    def test_inferred_is_always_tier_c(self):
+        assert (
+            assign_tier(
+                is_measured=False,
+                n_per_group=1000,
+                peer_reviewed=True,
+                single_source=False,
+            )
+            == ConfidenceTier.C
+        )
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            dict(is_measured=True, n_per_group=6, peer_reviewed=True, single_source=False),
+            dict(is_measured=True, n_per_group=27, peer_reviewed=False, single_source=False),
+            dict(is_measured=True, n_per_group=27, peer_reviewed=True, single_source=True),
+            dict(is_measured=True, n_per_group=None, peer_reviewed=True, single_source=False),
+        ],
+    )
+    def test_measured_but_weak_evidence_is_tier_b(self, kwargs):
+        assert assign_tier(**kwargs) == ConfidenceTier.B
+
+
+@pytest.mark.xfail(reason="Connectivity scoring implemented in Week 3 (notebook 04).", strict=True)
+def test_connectivity_score_matches_reference():
+    """Verify connectivity scoring against a CMap/cmapPy reference within tolerance.
+
+    PLAN.md §6, Week 3 task 4. Replace this body with a known reference example
+    (disease up/down sets + drug signature -> expected score) once the scorer exists.
+    """
+    from src.scoring import connectivity_score
+
+    score = connectivity_score(up_genes=[], down_genes=[], drug_signature=None)  # noqa
+    assert score == pytest.approx(0.0, abs=1e-6)