Clean up agent catalog and improve extraction agent prompts

- Remove unused config_schema from AgentCatalogItem (schema + route)
- Fix agent_setup system prompt: add extraction agent base behaviour
  context so journey LLM knows what is already handled and focuses on
  field mappings only; remove redundant data-types question (already
  known from user selection); derive data types list dynamically
- Rewrite processing base prompt to use actual tool names
  (list_tasks, update_task, add_task_comment, list_notes, update_note,
  list_timelines, update_timeline, list_all_projects, create_project)
  and enforce update-first strategy before falling back to creation

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

app/api/routes/agent_setup.py

@@ -89,6 +89,14 @@ Your job is to understand exactly what data the user wants to extract from their
 local directory and produce a detailed prompt_template that a separate AI will use
 as its instruction set.
 
+The extraction agent already has this base behaviour built in:
+  - Reads each file using file-system tools.
+  - Creates records (tasks, notes, timelines, projects) via CRUD tools.
+  - Sets isAiSuggested=1 and isApproved=0 on every record.
+  - Only extracts data explicitly present in the files — it never invents information.
+The user's custom prompt is appended AFTER this base behaviour, so focus on
+what to look for and how to map it — not on the general extraction mechanics.
+
 You have access to file-system tools to explore the user's directory:
 - list_directory: to see folder structure
 - read_file_content: to peek at file contents
@@ -100,10 +108,9 @@ Target data types: {data_types}
 Start by exploring the directory to understand its structure.  Then ask concise,
 focused questions one at a time.  Cover these topics (not necessarily in this order):
   1. The type and format of the source content (confirmed by your exploration).
-  2. Which data types to extract: tasks, notes, timelines, and/or projects.
-  3. How fields should be mapped (e.g. filename → task title).
-  4. Priority or status rules (e.g. "urgent" keyword → high priority).
-  5. Any special handling, date extraction, or exclusions.
+  2. How fields should be mapped (e.g. filename → task title).
+  3. Priority or status rules (e.g. "urgent" keyword → high priority).
+  4. Any special handling, date extraction, or exclusions.
 
 After 3-5 questions (when you have enough information), output the final prompt_template
 between these exact markers on their own lines:

app/api/routes/agents.py

@@ -121,24 +121,6 @@ async def get_agent_catalog(
             type="local_directory",
             name="Local Directory Monitor",
             description="Watches local directories, extracts data from files using AI",
-            config_schema={
-                "directory": {"type": "string", "required": True},
-                "what_to_extract": {
-                    "type": "array",
-                    "items": ["task", "note", "timeline", "project"],
-                    "required": True,
-                },
-                "actions_by_type": {
-                    "type": "object",
-                    "example": {
-                        "task": ["add", "update"],
-                        "note": ["add", "update"],
-                    },
-                    "required": False,
-                },
-                "batch_interval": {"type": "string", "required": True},
-                "custom_agent_prompt": {"type": "string", "required": True},
-            },
         ),
         AgentCatalogItem(
             type="gmail",