refactor(schemas): rename Agent* schemas and WS frame types to Scout*

Rename all Pydantic models referring to the scout subsystem:
AgentConfig → ScoutConfig, ContentTypeConfig → ScoutContentTypeConfig,
AgentCatalogItem → ScoutCatalogItem, AgentCreationCheckRequest/Response →
ScoutCreationCheckRequest/Response, AgentTriggerRequest → ScoutTriggerRequest,
AgentRunLogResponse → ScoutRunLogResponse.

LLM-helper agent schemas in app/agents/* are untouched.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/api/routes/scout_setup.py

@@ -1,4 +1,4 @@
-"""Chatbot Journey — WS-based guided conversation to build an AgentConfig.
+"""Chatbot Journey — WS-based guided conversation to build an ScoutConfig.
 
 The journey is driven entirely through WebSocket frames (no REST endpoints).
 The device WS handler dispatches ``journey_start`` and ``journey_message``
@@ -13,7 +13,7 @@ Journey flow:
   3. FE sends ``journey_message`` frames for each user reply.
   4. Server appends the user message, calls the LLM (which may read files
      via tools), and sends back a ``journey_reply``.
-  5. After 3-5 turns the LLM wraps up by emitting an ``AgentConfig`` JSON
+  5. After 3-5 turns the LLM wraps up by emitting an ``ScoutConfig`` JSON
      block delimited by ``AGENT_CONFIG_START`` / ``AGENT_CONFIG_END``.
   6. Server parses and validates the JSON with Pydantic, sends
      ``journey_reply`` with ``done=True`` and the serialised config.
@@ -34,7 +34,7 @@ from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, Tool
 from app.agents.filesystem_agent import make_directory_tools
 from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
 from app.core.llm import get_agent_llm, model_for_agent
-from app.schemas import AgentConfig
+from app.schemas import ScoutConfig
 
 logger = logging.getLogger(__name__)
 
@@ -42,7 +42,7 @@ logger = logging.getLogger(__name__)
 
 _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
 
-# Sentinel strings used to delimit the LLM-produced AgentConfig JSON.
+# Sentinel strings used to delimit the LLM-produced ScoutConfig JSON.
 _CONFIG_START = "AGENT_CONFIG_START"
 _CONFIG_END = "AGENT_CONFIG_END"
 
@@ -92,7 +92,7 @@ def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
 _JOURNEY_SYSTEM_PROMPT = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
 Your job is to understand what files the user has in their directory and produce a
-structured AgentConfig JSON that the extraction agent will use as its instruction set.
+structured ScoutConfig JSON that the extraction agent will use as its instruction set.
 
 You have access to file-system tools to explore the user's directory:
 - list_directory: see folder structure and file names
@@ -122,7 +122,7 @@ Cover these topics based on what you discovered:
 4. Date extraction (e.g. "by Friday" → dueDate)
 5. Exclusion rules (e.g. skip newsletters, skip files with no project match)
 
-### Step 4 — Produce the AgentConfig JSON
+### Step 4 — Produce the ScoutConfig JSON
 Once you are ≥ 90% confident, output the final config between these exact markers
 (each on its own line):
 
@@ -168,7 +168,7 @@ def _build_system_prompt(
 ) -> tuple[str, Any]:
     """Return ``(compiled_system_prompt, langfuse_prompt_obj_or_None)``."""
     existing_section = (
-        "\nThe user already has the following AgentConfig — refine it based on their answers:\n"
+        "\nThe user already has the following ScoutConfig — refine it based on their answers:\n"
         f"```json\n{existing_config}\n```\n"
         if existing_config
         else ""
@@ -189,11 +189,11 @@ def _build_system_prompt(
     return compiled, prompt_obj
 
 
-# ── AgentConfig extraction ────────────────────────────────────────────────
+# ── ScoutConfig extraction ────────────────────────────────────────────────
 
 
 def _extract_agent_config(text: str) -> str | None:
-    """Return validated AgentConfig JSON string from between markers, or None.
+    """Return validated ScoutConfig JSON string from between markers, or None.
 
     Parses the JSON with Pydantic to ensure it conforms to the schema before
     returning.  Returns None if markers are absent or JSON is invalid.
@@ -206,10 +206,10 @@ def _extract_agent_config(text: str) -> str | None:
     if not raw:
         return None
     try:
-        parsed = AgentConfig.model_validate_json(raw)
+        parsed = ScoutConfig.model_validate_json(raw)
         return parsed.model_dump_json()
     except Exception as exc:
-        logger.warning("agent_setup: failed to parse AgentConfig JSON: %s", exc)
+        logger.warning("agent_setup: failed to parse ScoutConfig JSON: %s", exc)
         return None
 
 
@@ -475,7 +475,7 @@ async def handle_journey_message(
         if turns >= _MAX_TURNS:
             nudge_content = (
                 "[System: You have enough information. Please generate the final "
-                f"AgentConfig JSON now, wrapped in {_CONFIG_START} / {_CONFIG_END} markers.]"
+                f"ScoutConfig JSON now, wrapped in {_CONFIG_START} / {_CONFIG_END} markers.]"
             )
             session.history.append({"role": "user", "content": nudge_content})