Add local search quality evaluation harness

Add an optional local evaluator that compares a baseline revision against a candidate checkout, computes deterministic stability metrics, and can call Gemini for judged ranking metrics when configured.

The harness isolates child runs with a temporary HOME and a node-free PATH so historical revisions cannot trigger Bird browser-cookie auth during evaluation.

Validation: uv run python -m unittest and local smoke/full deterministic eval runs.

tests/test_evaluate_search_quality.py

@@ -0,0 +1,81 @@
+"""Tests for the local search-quality evaluation harness."""
+
+import sys
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
+
+import evaluate_search_quality as evalsq
+
+
+class TestMetrics(unittest.TestCase):
+    def test_jaccard(self):
+        self.assertAlmostEqual(evalsq.jaccard({"a", "b"}, {"b", "c"}), 1 / 3)
+
+    def test_retention(self):
+        self.assertAlmostEqual(evalsq.retention({"a", "b"}, {"b", "c"}), 0.5)
+
+    def test_precision_at_k(self):
+        ranking = [
+            {"key": "a", "source": "reddit"},
+            {"key": "b", "source": "x"},
+            {"key": "c", "source": "youtube"},
+        ]
+        judgments = {"a": 3, "b": 1, "c": 2}
+        self.assertAlmostEqual(evalsq.precision_at_k(ranking, judgments, 2), 0.5)
+
+    def test_ndcg_at_k(self):
+        ranking = [
+            {"key": "a", "source": "reddit"},
+            {"key": "b", "source": "x"},
+            {"key": "c", "source": "youtube"},
+        ]
+        judgments = {"a": 3, "b": 0, "c": 2}
+        self.assertGreater(evalsq.ndcg_at_k(ranking, judgments, 3), 0.8)
+
+    def test_source_coverage_recall_uses_union_pool(self):
+        judged_pool = [
+            {"key": "a", "source": "reddit"},
+            {"key": "b", "source": "x"},
+            {"key": "c", "source": "youtube"},
+        ]
+        ranking = [
+            {"key": "a", "source": "reddit"},
+            {"key": "b", "source": "x"},
+        ]
+        judgments = {"a": 3, "b": 0, "c": 2}
+        self.assertAlmostEqual(evalsq.source_coverage_recall(ranking, judged_pool, judgments), 0.5)
+
+
+class TestRankedItems(unittest.TestCase):
+    def test_build_ranked_items_sorts_by_score(self):
+        report = {
+            "reddit": [{"id": "R1", "title": "Low", "url": "r1", "score": 20}],
+            "x": [{"id": "X1", "text": "High", "url": "x1", "score": 90}],
+            "youtube": [],
+            "tiktok": [],
+            "instagram": [],
+            "hackernews": [],
+            "bluesky": [],
+            "truthsocial": [],
+            "polymarket": [],
+            "websearch": [],
+        }
+        ranked = evalsq.build_ranked_items(report, per_source_limit=5)
+        self.assertEqual(ranked[0]["key"], "x1")
+
+
+class TestPathWithoutNode(unittest.TestCase):
+    def test_removes_node_entries(self):
+        path = "/usr/bin:/tmp/node-bin:/opt/homebrew/bin"
+        def fake_exists(path_obj):
+            return str(path_obj).endswith("/tmp/node-bin/node")
+        with patch.object(evalsq.Path, "exists", fake_exists):
+            filtered = evalsq.path_without_node(path)
+        self.assertEqual(filtered, "/usr/bin:/opt/homebrew/bin")
+
+
+if __name__ == "__main__":
+    unittest.main()