feat: decision-log data layer for Decide mode (learning layer)

Storage + tools so Decide mode can learn instead of one-shot tie-breaking: log
the call Brian makes, resolve it later with the outcome, recall similar past calls
to ground new recommendations in his own track record.

- memory: decisions table + Decision dataclass + log/resolve/get/list/recall_decisions
  (embedding over situation+choice; embed failure never blocks a log)
- tools: log_decision / resolve_decision / recall_decisions handlers + specs
- tests: 9 covering roundtrip, resolve, open-only filter, similarity rank, tool layer

Data layer only — NOT wired into any mode's allow-list or the Decide card yet
(the prompt/taste part is left for Brian; see docs/DECISION_LOG.md). No behavior
changes until the tools are added to _DECIDE_TOOLS.

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

tests/test_decisions.py

@@ -0,0 +1,103 @@
+"""Decision log (Decide mode's learning layer): log -> resolve -> recall, + tools."""
+from __future__ import annotations
+
+import importlib
+
+import pytest
+
+
+@pytest.fixture
+def mem(tmp_path, monkeypatch):
+    monkeypatch.setenv("LYRA_DB_PATH", str(tmp_path / "test.db"))
+    from lyra import llm
+    # Deterministic, content-dependent embeddings so recall ordering is meaningful:
+    # "cleveland"/"tournament" cluster on axis 0, "stocks"/"money" on axis 1.
+    def fake_embed(texts):
+        out = []
+        for t in texts:
+            t = t.lower()
+            poker = sum(w in t for w in ("tournament", "cleveland", "poker", "buy-in"))
+            money = sum(w in t for w in ("stocks", "money", "invest", "sell"))
+            out.append([float(poker), float(money), 0.1])
+        return out
+    monkeypatch.setattr(llm, "embed", fake_embed)
+    import lyra.memory as memory
+    importlib.reload(memory)
+    return memory
+
+
+def test_log_and_get_roundtrip(mem):
+    did = mem.log_decision(
+        situation="Play the Cleveland turbo tournament tomorrow?",
+        choice="Yes, but only the noon flight",
+        options="skip it / noon flight / both flights",
+        rationale="20-min levels suit my aggression; one flight caps the variance",
+        confidence=4, tags="poker,tournament",
+    )
+    d = mem.get_decision(did)
+    assert d.situation.startswith("Play the Cleveland")
+    assert d.choice == "Yes, but only the noon flight"
+    assert d.confidence == 4 and d.tags == "poker,tournament"
+    assert not d.resolved and d.outcome is None
+
+
+def test_resolve_closes_the_loop(mem):
+    did = mem.log_decision(situation="Sell the stocks now?", choice="Hold")
+    assert mem.resolve_decision(did, "Recovered 12% the next week", outcome_rating=1)
+    d = mem.get_decision(did)
+    assert d.resolved and d.outcome_rating == 1
+    assert "Recovered" in d.outcome and d.resolved_at is not None
+
+
+def test_resolve_unknown_id_is_false(mem):
+    assert mem.resolve_decision(999, "n/a") is False
+
+
+def test_list_open_only_filters_resolved(mem):
+    a = mem.log_decision(situation="A?", choice="x")
+    mem.log_decision(situation="B?", choice="y")
+    mem.resolve_decision(a, "done", 0)
+    assert {d.situation for d in mem.list_decisions(open_only=True)} == {"B?"}
+    assert len(mem.list_decisions()) == 2
+
+
+def test_recall_ranks_by_similarity(mem):
+    mem.log_decision(situation="Which Cleveland tournament flight?", choice="noon")
+    mem.log_decision(situation="Should I sell the stocks?", choice="hold")
+    hits = mem.recall_decisions("another poker tournament buy-in", k=2)
+    assert hits[0].situation.startswith("Which Cleveland")   # poker cluster ranks first
+    assert hits[0].score >= hits[1].score
+
+
+# --- tool layer ---------------------------------------------------------------
+
+def test_log_decision_tool_persists(mem):
+    from lyra import tools
+    out = tools.dispatch("log_decision",
+                         {"situation": "Move the MI50 to auto clocks?", "choice": "yes",
+                          "confidence": "3", "tags": "build"})
+    assert "#1" in out
+    d = mem.get_decision(1)
+    assert d.choice == "yes" and d.confidence == 3 and d.tags == "build"
+
+
+def test_log_decision_tool_requires_both_fields(mem):
+    from lyra import tools
+    assert "Need both" in tools.dispatch("log_decision", {"situation": "just this"})
+
+
+def test_resolve_decision_tool(mem):
+    from lyra import tools
+    did = mem.log_decision(situation="X?", choice="y")
+    out = tools.dispatch("resolve_decision",
+                         {"decision_id": did, "outcome": "worked out", "rating": "1"})
+    assert f"#{did}" in out
+    assert mem.get_decision(did).outcome_rating == 1
+
+
+def test_recall_decisions_tool_surfaces_outcomes(mem):
+    from lyra import tools
+    did = mem.log_decision(situation="Cleveland tournament again?", choice="play")
+    mem.resolve_decision(did, "min-cashed", outcome_rating=0)
+    out = tools.dispatch("recall_decisions", {"query": "poker tournament tomorrow"})
+    assert "Cleveland" in out and "mixed" in out