feat: BMAD-Agenten, Kern-Workflow & lauffähiger Photo-to-Listing-Prototyp

- src/models.py: typisierte Verträge (dataclasses, Stdlib-only)
- src/llm/claude_client.py: Adapter um 'claude -p' mit Mock-Fallback
- src/agents/: BaseAgent + Vision, Market, Listing, Chat + Orchestrator
- src/workflow.py: photo_to_listing() Fassade
- spike/prototype.py + concept_spike.py: lauffähige End-to-End-Demo
- tests/: 28 unittest-Tests (Mock-Pfad, offline deterministisch)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/test_price_research.py

@@ -0,0 +1,39 @@
+"""Tests für den Preis-Recherche-Agenten (Mock-Pfad)."""
+
+import unittest
+
+from src.agents import PriceResearchAgent
+from src.llm import ClaudeClient
+from src.models import ItemAnalysis, PriceSuggestion
+
+
+def _item(category="Kopfhörer", score=1.0):
+    return ItemAnalysis(
+        title_guess="Test", category=category,
+        condition="Neu", condition_score=score, brand="Marke",
+    )
+
+
+class PriceResearchTest(unittest.TestCase):
+    def setUp(self):
+        self.agent = PriceResearchAgent(ClaudeClient(mode="mock"))
+
+    def test_returns_price_suggestion(self):
+        price = self.agent.run(_item())
+        self.assertIsInstance(price, PriceSuggestion)
+        self.assertEqual(price.currency, "EUR")
+        self.assertLess(price.price_min, price.price_max)
+
+    def test_condition_scales_price(self):
+        good = self.agent.run(_item(score=1.0)).suggested_price
+        worn = self.agent.run(_item(score=0.0)).suggested_price
+        self.assertGreater(good, worn)
+
+    def test_category_affects_base_price(self):
+        headphones = self.agent.run(_item(category="Kopfhörer")).suggested_price
+        unknown = self.agent.run(_item(category="Irgendwas")).suggested_price
+        self.assertGreater(headphones, unknown)
+
+
+if __name__ == "__main__":
+    unittest.main()