refactor: replace <SILENT_OK> with structured post-run evaluation

- Add nanobot/utils/evaluator.py: lightweight LLM tool-call to decide notify/silent after background task execution
- Remove magic token injection from heartbeat and cron prompts
- Clean session history (no more <SILENT_OK> pollution)
- Add tests for evaluator and updated heartbeat three-phase flow

tests/test_evaluator.py

@@ -0,0 +1,63 @@
+import pytest
+
+from nanobot.utils.evaluator import evaluate_response
+from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
+
+
+class DummyProvider(LLMProvider):
+    def __init__(self, responses: list[LLMResponse]):
+        super().__init__()
+        self._responses = list(responses)
+
+    async def chat(self, *args, **kwargs) -> LLMResponse:
+        if self._responses:
+            return self._responses.pop(0)
+        return LLMResponse(content="", tool_calls=[])
+
+    def get_default_model(self) -> str:
+        return "test-model"
+
+
+def _eval_tool_call(should_notify: bool, reason: str = "") -> LLMResponse:
+    return LLMResponse(
+        content="",
+        tool_calls=[
+            ToolCallRequest(
+                id="eval_1",
+                name="evaluate_notification",
+                arguments={"should_notify": should_notify, "reason": reason},
+            )
+        ],
+    )
+
+
+@pytest.mark.asyncio
+async def test_should_notify_true() -> None:
+    provider = DummyProvider([_eval_tool_call(True, "user asked to be reminded")])
+    result = await evaluate_response("Task completed with results", "check emails", provider, "m")
+    assert result is True
+
+
+@pytest.mark.asyncio
+async def test_should_notify_false() -> None:
+    provider = DummyProvider([_eval_tool_call(False, "routine check, nothing new")])
+    result = await evaluate_response("All clear, no updates", "check status", provider, "m")
+    assert result is False
+
+
+@pytest.mark.asyncio
+async def test_fallback_on_error() -> None:
+    class FailingProvider(DummyProvider):
+        async def chat(self, *args, **kwargs) -> LLMResponse:
+            raise RuntimeError("provider down")
+
+    provider = FailingProvider([])
+    result = await evaluate_response("some response", "some task", provider, "m")
+    assert result is True
+
+
+@pytest.mark.asyncio
+async def test_no_tool_call_fallback() -> None:
+    provider = DummyProvider([LLMResponse(content="I think you should notify", tool_calls=[])])
+    result = await evaluate_response("some response", "some task", provider, "m")
+    assert result is True

tests/test_heartbeat_service.py

@@ -123,6 +123,98 @@ async def test_trigger_now_returns_none_when_decision_is_skip(tmp_path) -> None:
     assert await service.trigger_now() is None
 
 
+@pytest.mark.asyncio
+async def test_tick_notifies_when_evaluator_says_yes(tmp_path, monkeypatch) -> None:
+    """Phase 1 run -> Phase 2 execute -> Phase 3 evaluate=notify -> on_notify called."""
+    (tmp_path / "HEARTBEAT.md").write_text("- [ ] check deployments", encoding="utf-8")
+
+    provider = DummyProvider([
+        LLMResponse(
+            content="",
+            tool_calls=[
+                ToolCallRequest(
+                    id="hb_1",
+                    name="heartbeat",
+                    arguments={"action": "run", "tasks": "check deployments"},
+                )
+            ],
+        ),
+    ])
+
+    executed: list[str] = []
+    notified: list[str] = []
+
+    async def _on_execute(tasks: str) -> str:
+        executed.append(tasks)
+        return "deployment failed on staging"
+
+    async def _on_notify(response: str) -> None:
+        notified.append(response)
+
+    service = HeartbeatService(
+        workspace=tmp_path,
+        provider=provider,
+        model="openai/gpt-4o-mini",
+        on_execute=_on_execute,
+        on_notify=_on_notify,
+    )
+
+    async def _eval_notify(*a, **kw):
+        return True
+
+    monkeypatch.setattr("nanobot.utils.evaluator.evaluate_response", _eval_notify)
+
+    await service._tick()
+    assert executed == ["check deployments"]
+    assert notified == ["deployment failed on staging"]
+
+
+@pytest.mark.asyncio
+async def test_tick_suppresses_when_evaluator_says_no(tmp_path, monkeypatch) -> None:
+    """Phase 1 run -> Phase 2 execute -> Phase 3 evaluate=silent -> on_notify NOT called."""
+    (tmp_path / "HEARTBEAT.md").write_text("- [ ] check status", encoding="utf-8")
+
+    provider = DummyProvider([
+        LLMResponse(
+            content="",
+            tool_calls=[
+                ToolCallRequest(
+                    id="hb_1",
+                    name="heartbeat",
+                    arguments={"action": "run", "tasks": "check status"},
+                )
+            ],
+        ),
+    ])
+
+    executed: list[str] = []
+    notified: list[str] = []
+
+    async def _on_execute(tasks: str) -> str:
+        executed.append(tasks)
+        return "everything is fine, no issues"
+
+    async def _on_notify(response: str) -> None:
+        notified.append(response)
+
+    service = HeartbeatService(
+        workspace=tmp_path,
+        provider=provider,
+        model="openai/gpt-4o-mini",
+        on_execute=_on_execute,
+        on_notify=_on_notify,
+    )
+
+    async def _eval_silent(*a, **kw):
+        return False
+
+    monkeypatch.setattr("nanobot.utils.evaluator.evaluate_response", _eval_silent)
+
+    await service._tick()
+    assert executed == ["check status"]
+    assert notified == []
+
+
 @pytest.mark.asyncio
 async def test_decide_retries_transient_error_then_succeeds(tmp_path, monkeypatch) -> None:
     provider = DummyProvider([