fix: tool registry emits Anthropic schema, question lifecycle cleanup

- Tool.to_schema() emits input_schema (Anthropic Messages API) instead
  of OpenAI-style parameters
- QuestionManager: remove questions from _pending on answer and timeout,
  reject double answers, add list_pending()
- core/skills/__init__.py so skills are importable as a package
- revert optional-import fallbacks in logger/message_bus: nats-py and
  loguru are hard requirements, a broken install should fail loudly
- tests for wait_for_answer happy path and timeout cleanup

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012AJLYcbTHCe45fyFnMfEin

core/interactive_question.py

@@ -110,24 +110,33 @@ class QuestionManager:
             raise TimeoutError(f"Question {question.question_id} timed out waiting for human input")
         finally:
             self._futures.pop(question.question_id, None)
+            self._pending.pop(question.question_id, None)
 
     def answer_question(self, question_id: str, answer: Any) -> bool:
-        """Called when human operator submits an answer from the UI."""
+        """Called when human operator submits an answer from the UI.
+
+        Returns False if the question is unknown or was already answered."""
         question = self._pending.get(question_id)
         if not question:
             logger.warning(f"No pending question found for id {question_id}")
             return False
+        if question.answered:
+            logger.warning(f"Question {question_id} already answered; ignoring")
+            return False
 
         question.answered = True
         question.answer = answer
 
         fut = self._futures.get(question_id)
         if fut and not fut.done():
-            fut.set_result(answer)
-            return True
-
+            fut.set_result(answer)  # wait_for_answer() removes it from _pending
+        else:
+            self._pending.pop(question_id, None)  # nobody waiting; don't leak
         return True
 
+    def list_pending(self) -> List[Question]:
+        return [q for q in self._pending.values() if not q.answered]
+
     def get_pending(self, question_id: str) -> Optional[Question]:
         return self._pending.get(question_id)
 

core/logger.py

@@ -1,33 +1,27 @@
-"""Structured logging for LogiFlow AI — loguru setup with standard logging fallback."""
+"""Structured logging for LogiFlow AI — single loguru setup imported by all modules."""
 import sys
 import os
-import logging
 from pathlib import Path
+from loguru import logger
 
-try:
-    from loguru import logger
+Path("logs").mkdir(exist_ok=True)
 
-    Path("logs").mkdir(exist_ok=True)
+logger.remove()
 
-    logger.remove()
+logger.add(
+    sys.stderr,
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
+    level=os.getenv("LOG_LEVEL", "INFO"),
+    colorize=False,
+)
 
-    logger.add(
-        sys.stderr,
-        format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
-        level=os.getenv("LOG_LEVEL", "INFO"),
-        colorize=False,
-    )
-
-    logger.add(
-        "logs/logiflow_{time:YYYY-MM-DD}.log",
-        rotation="100 MB",
-        retention="30 days",
-        compression="gz",
-        level="DEBUG",
-        format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
-    )
-except ImportError:
-    logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)-8s | %(message)s")
-    logger = logging.getLogger("logiflow")
+logger.add(
+    "logs/logiflow_{time:YYYY-MM-DD}.log",
+    rotation="100 MB",
+    retention="30 days",
+    compression="gz",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
+)
 
 __all__ = ["logger"]

core/message_bus.py

@@ -8,11 +8,8 @@ from collections import defaultdict
 from dataclasses import dataclass
 from enum import Enum
 
-try:
-    import nats
-    import nats.js.errors
-except ImportError:
-    nats = None
+import nats
+import nats.js.errors
 
 from core.types import AgentMessage, MessageType
 from core.logger import logger
@@ -59,9 +56,6 @@ class MessageBus:
 
     async def connect(self):
         """Connect to NATS and create the logistics JetStream stream."""
-        if nats is None:
-            logger.warning("nats-py not installed; message bus running in local in-memory mode")
-            return
         self._nc = await nats.connect(
             servers=[f"nats://{NATS_HOST}:{NATS_PORT}"],
             user=NATS_USER,

core/tool_registry.py

@@ -33,7 +33,7 @@ class Tool:
     metadata: Dict[str, Any] = field(default_factory=dict)
 
     def to_schema(self) -> Dict[str, Any]:
-        """Generate OpenAI/Claude compatible JSON Schema for tool calling."""
+        """Generate an Anthropic Messages API tool definition (name / description / input_schema)."""
         properties = {}
         required = []
 
@@ -56,7 +56,7 @@ class Tool:
         return {
             "name": self.name,
             "description": self.description,
-            "parameters": {
+            "input_schema": {
                 "type": "object",
                 "properties": properties,
                 "required": required,