feat(agents): semantic task clustering + focus-area inferred preferred_areas (#97, #113)

- New ml/agents/clustering.py: embed task content via nomic-embed-text
  (Ollama), greedy cosine clustering (threshold 0.72, max 6 clusters),
  graceful fallback to project-id grouping when Ollama is unreachable
- focus_area v2.0.0: compute() uses semantic clusters as focus areas;
  adds preferred_areas InferredParam inferred from top-2 projects by
  task_completion count
- 135 tests, all passing

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ml/agents/tests/test_clustering.py

@@ -0,0 +1,135 @@
+"""Unit tests for ml.agents.clustering (issue #97).
+
+Embedding calls are mocked so tests run without Ollama.
+"""
+from __future__ import annotations
+
+import sys, os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+
+from unittest.mock import patch
+
+from ml.agents.clustering import cluster_tasks, Cluster, _greedy_cluster, _cosine
+
+
+# ── helpers ──────────────────────────────────────────────────────────────────
+
+def _task(content: str, project_id: str | None = None, is_overdue: bool = False) -> dict:
+    t: dict = {"content": content, "is_overdue": is_overdue}
+    if project_id:
+        t["project_id"] = project_id
+    return t
+
+
+def _embed_seq(*vecs):
+    """Return a side_effect list so successive _embed calls return these vectors."""
+    return list(vecs)
+
+
+# ── Cluster dataclass ─────────────────────────────────────────────────────────
+
+class TestCluster:
+    def test_task_count(self):
+        c = Cluster(label="X", tasks=[_task("a"), _task("b")])
+        assert c.task_count == 2
+
+    def test_overdue_count(self):
+        c = Cluster(label="X", tasks=[_task("a", is_overdue=True), _task("b")])
+        assert c.overdue_count == 1
+
+
+# ── cosine similarity ─────────────────────────────────────────────────────────
+
+class TestCosine:
+    def test_identical_vectors(self):
+        v = [1.0, 0.0, 0.0]
+        assert _cosine(v, v) == 1.0
+
+    def test_orthogonal_vectors(self):
+        assert _cosine([1.0, 0.0], [0.0, 1.0]) == 0.0
+
+    def test_zero_vector(self):
+        assert _cosine([0.0, 0.0], [1.0, 0.0]) == 0.0
+
+
+# ── greedy clustering ─────────────────────────────────────────────────────────
+
+class TestGreedyClustering:
+    def _similar_vec(self, base: list[float], noise: float = 0.01) -> list[float]:
+        return [x + noise for x in base]
+
+    def test_similar_tasks_grouped(self):
+        v = [1.0, 0.0, 0.0]
+        v2 = [0.999, 0.001, 0.0]
+        items = [
+            (_task("A"), v),
+            (_task("B"), v2),
+        ]
+        clusters = _greedy_cluster(items)
+        assert len(clusters) == 1
+        assert clusters[0].task_count == 2
+
+    def test_dissimilar_tasks_separate(self):
+        v1 = [1.0, 0.0, 0.0]
+        v2 = [0.0, 1.0, 0.0]
+        items = [(_task("A"), v1), (_task("B"), v2)]
+        clusters = _greedy_cluster(items)
+        assert len(clusters) == 2
+
+    def test_label_from_first_task(self):
+        v = [1.0, 0.0]
+        clusters = _greedy_cluster([(_task("Write report"), v)])
+        assert clusters[0].label == "Write report"
+
+
+# ── cluster_tasks integration ─────────────────────────────────────────────────
+
+class TestClusterTasks:
+    def test_empty_tasks(self):
+        result = cluster_tasks([])
+        assert result == []
+
+    def test_fallback_when_ollama_unavailable(self):
+        with patch("ml.agents.clustering._embed", return_value=None):
+            tasks = [_task("A", "p1"), _task("B", "p2"), _task("C", "p1")]
+            clusters = cluster_tasks(tasks)
+        assert len(clusters) == 2
+        labels = {c.label for c in clusters}
+        assert "p1" in labels and "p2" in labels
+
+    def test_fallback_groups_by_project(self):
+        with patch("ml.agents.clustering._embed", return_value=None):
+            tasks = [_task("A", "work")] * 3 + [_task("B", "home")] * 2
+            clusters = cluster_tasks(tasks)
+        by_label = {c.label: c.task_count for c in clusters}
+        assert by_label["work"] == 3
+        assert by_label["home"] == 2
+
+    def test_tasks_without_content_go_to_other(self):
+        v = [1.0, 0.0]
+        with patch("ml.agents.clustering._embed", return_value=v):
+            tasks = [_task("Has content"), {"is_overdue": False}]
+            clusters = cluster_tasks(tasks)
+        labels = {c.label for c in clusters}
+        assert "Other tasks" in labels
+
+    def test_semantic_clustering_groups_similar(self):
+        v_work = [1.0, 0.0, 0.0]
+        v_home = [0.0, 1.0, 0.0]
+        side_effects = [v_work, v_work, v_home, v_home]
+        with patch("ml.agents.clustering._embed", side_effect=side_effects):
+            tasks = [
+                _task("Write report"),
+                _task("Review PR"),
+                _task("Buy groceries"),
+                _task("Cook dinner"),
+            ]
+            clusters = cluster_tasks(tasks)
+        assert len(clusters) == 2
+        assert all(c.task_count == 2 for c in clusters)
+
+    def test_all_tasks_no_content_fallback_by_project(self):
+        tasks = [{"project_id": "p1", "is_overdue": False},
+                 {"project_id": "p2", "is_overdue": False}]
+        clusters = cluster_tasks(tasks)
+        assert len(clusters) == 2