feat(sleep): optimizer/target model split, transfer experiment, LLM miner

Three additions driven by the goal of price-aware, model-flexible sleep:

1. DualBackend + build_backend(): route attempt->TARGET model and
   reflect/judge->OPTIMIZER model (SkillOpt's target-vs-optimizer split).
   gbrain runner gains --optimizer-backend/-model + --target-backend/-model.

2. run_transfer.py: sleep-scenario cross-model transfer. Optimize a skill on a
   SOURCE model (e.g. cheap haiku), freeze it, evaluate held-out on a TARGET
   model (e.g. expensive sonnet) with no further optimization — plus a direct
   reference. Mirrors the SkillOpt paper's transfer table; quantifies the
   "optimize cheap overnight, deploy anywhere" value prop.

3. llm_miner.py: turn real harvested transcripts into TaskRecords WITH checkable
   rule/rubric judges, wired into the cycle for non-mock backends, so real-data
   lift becomes measurable (heuristic miner remains the no-API fallback).
   Fixed a str.format brace bug the new unit test caught.

19 tests pass.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>

tests/test_sleep_engine.py

@@ -177,6 +177,42 @@ class TestGbrainLoader(unittest.TestCase):
         self.assertEqual(score_rule_judge(ho.judge, skill)[0], 0.0)
 
 
+class TestLlmMiner(unittest.TestCase):
+    def test_miner_emits_checkable_tasks(self):
+        # a stub backend whose _call returns canned miner JSON => deterministic
+        from skillopt.sleep.backend import Backend
+        from skillopt.sleep.llm_miner import make_llm_miner
+
+        class StubBackend(Backend):
+            name = "stub"
+            def _call(self, prompt, *, max_tokens=1024):
+                return ('[{"intent":"write a research brief",'
+                        '"checks":[{"op":"section_present","arg":"Key Risks"}],'
+                        '"rubric":"has a risks section","satisfied":false}]')
+
+        digest = SessionDigest(session_id="s1", project="/p",
+                               user_prompts=["write a brief on X"],
+                               assistant_finals=["a brief"], n_user_turns=1)
+        miner = make_llm_miner(StubBackend())
+        tasks = miner([digest])
+        self.assertEqual(len(tasks), 1)
+        self.assertEqual(tasks[0].reference_kind, "rule")
+        self.assertEqual(tasks[0].judge["checks"][0]["op"], "section_present")
+
+    def test_miner_drops_uncheckable(self):
+        from skillopt.sleep.backend import Backend
+        from skillopt.sleep.llm_miner import make_llm_miner
+
+        class EmptyBackend(Backend):
+            name = "stub"
+            def _call(self, prompt, *, max_tokens=1024):
+                return "[]"
+
+        digest = SessionDigest(session_id="s1", project="/p",
+                               user_prompts=["chat"], n_user_turns=1)
+        self.assertEqual(make_llm_miner(EmptyBackend())([digest]), [])
+
+
 class TestFullCycleAndAdopt(unittest.TestCase):
     def test_cycle_stage_then_adopt_with_backup(self):
         with tempfile.TemporaryDirectory() as proj, tempfile.TemporaryDirectory() as home: