chore(automation): trim docstrings to intent only

Cut the docstrings and Field(description=...) text across the entire
automations/ tree down to single-line intent statements, matching the
multi_agent_chat conciseness style:

- Module docstrings: one line stating what the file is.
- Class docstrings: deleted when the class name + module docstring
  already cover intent; kept only where they add a constraint or
  rationale not visible in the signature.
- Pydantic Field descriptions: short noun phrases / clauses, not
  full sentences. Reasoning that belonged in the design plan moved
  out of the code.
- Enum values: per-value docstrings replaced with terse inline
  comments where the meaning isn't obvious from the name.

Behaviour is unchanged. The same 33 files, same public surface, same
imports — verified by re-running the 10-point registry smoke test and
the 8-point schema round-trip / constraint suite from commits 9 and
10.

LOC: 1180 → 691 (-42%).

surfsense_backend/app/automations/schemas/actions/__init__.py

@@ -1,4 +1,4 @@
-"""Per-action config schemas: one file per action type registered in v1."""
+"""Per-action config schemas, one per action type."""
 
 from __future__ import annotations
 

surfsense_backend/app/automations/schemas/actions/agent_task.py

@@ -8,59 +8,20 @@ from pydantic import BaseModel, ConfigDict, Field
 
 
 class AgentTaskActionConfig(BaseModel):
-    """Config for an ``agent_task`` plan step.
-
-    Validated against ``PlanStep.config`` whenever the step's
-    ``action`` is ``agent_task``. The step instructs the LangGraph
-    Deep Agent runtime to:
-
-    1. Receive ``prompt`` (with all preceding-step outputs and inputs
-       already rendered by the template engine).
-    2. Run the agent with access to *exactly* the capabilities named
-       in ``tools`` — nothing else from the registry is visible to
-       this agent invocation.
-    3. Return a JSON object matching ``output_schema`` (recommended;
-       the executor validates and re-prompts on mismatch).
-
-    ``output_schema`` is the design's "dynamic output contract" —
-    instead of locking the output shape on the ActionDefinition (as
-    tight actions do), the user declares the shape they want for this
-    specific step, and the agent has to match it.
-    """
+    """Run a LangGraph Deep Agent restricted to a scoped capability list."""
 
     model_config = ConfigDict(extra="forbid")
 
-    prompt: str = Field(
-        ...,
-        description=(
-            "The task prompt rendered through the Jinja sandbox. May "
-            "reference automation inputs and prior-step outputs."
-        ),
-        min_length=1,
-    )
+    prompt: str = Field(..., min_length=1, description="Task prompt; Jinja-rendered.")
     tools: list[str] = Field(
         default_factory=list,
-        description=(
-            "Allowlist of capability IDs the agent may call (e.g., "
-            "'search_space.query'). Empty list = no tool access; the "
-            "agent must answer from the prompt alone."
-        ),
+        description="Capability IDs the agent may call. Empty = no tool access.",
     )
     model: str | None = Field(
         default=None,
-        description=(
-            "Optional LiteLLM model identifier (e.g., "
-            "'anthropic/claude-sonnet-4-7'). Omitted means the "
-            "automation falls back to the search space's default "
-            "agent_llm_id."
-        ),
+        description="LiteLLM model id. Defaults to the search space's agent_llm_id.",
     )
     output_schema: dict[str, Any] | None = Field(
         default=None,
-        description=(
-            "Optional JSON Schema declaring the shape the agent must "
-            "return. Strongly recommended; the editor warns when "
-            "missing. Validated by the executor before binding to "
-            "``output_as``."
-        ),
+        description="JSON Schema the agent must return. Recommended.",
     )