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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%).
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33 changed files with 80 additions and 568 deletions
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"""Three registries — ``capabilities/``, ``actions/``, ``triggers/`` — populated at import time."""
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"""Capability, action, and trigger registries — populated at process startup."""
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from __future__ import annotations
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"""Action registry: ``types.py`` (dataclass), ``store.py`` (dict + register fn)."""
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"""Action registry."""
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from __future__ import annotations
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@ -1,4 +1,4 @@
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"""Action registry: in-memory dict + ``register_action`` API."""
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"""In-memory action registry. Populated once at process startup."""
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from __future__ import annotations
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@ -8,26 +8,16 @@ _REGISTRY: dict[str, ActionDefinition] = {}
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def register_action(action: ActionDefinition) -> None:
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"""Add an action to the in-memory registry.
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Raises ``ValueError`` on duplicate ``type`` — registration runs
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once per process, so a duplicate is always a bug.
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"""
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"""Register an action. Raises on duplicate type."""
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if action.type in _REGISTRY:
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raise ValueError(
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f"Action already registered: {action.type!r}"
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)
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raise ValueError(f"Action already registered: {action.type!r}")
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_REGISTRY[action.type] = action
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def get_action(action_type: str) -> ActionDefinition | None:
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"""Look up one action by type. Returns ``None`` on miss."""
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return _REGISTRY.get(action_type)
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def all_actions() -> dict[str, ActionDefinition]:
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"""Snapshot of the registry as a defensive copy."""
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"""Defensive snapshot of the registry."""
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return dict(_REGISTRY)
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"""``ActionDefinition`` dataclass — the v1-minimum action shape."""
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"""``ActionDefinition`` dataclass and handler signature."""
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from __future__ import annotations
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@ -7,36 +7,10 @@ from dataclasses import dataclass
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from typing import Any
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ActionHandler = Callable[[dict[str, Any]], Awaitable[Any]]
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"""The signature every action handler must satisfy.
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Identical in shape to ``CapabilityHandler`` — both receive a
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caller-validated input dict and return an arbitrary output. The
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distinction is purely architectural: capabilities are the low-level
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"what SurfSense can do" surface, actions are the user-facing
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building blocks composed into a plan.
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"""
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@dataclass(frozen=True, slots=True)
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class ActionDefinition:
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"""A user-facing step type the plan editor can compose.
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v1 trims the dataclass to the five fields necessary for
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registry dispatch and form rendering. The full design (§4)
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includes ``output_contract``, ``uses_capabilities``, and
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``produces_artifacts``; all three are deferred until a consumer
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feature requires them:
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- ``output_contract`` — the loose ``agent_task`` action declares
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its output shape per-step via ``config.output_schema``, so the
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action-level contract is not needed in v1.
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- ``uses_capabilities`` — would let the NL generator do static
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analysis of which capabilities each action invokes; deferred
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because v1 ships a single (``agent_task``) action.
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- ``produces_artifacts`` — deferred alongside the artifact
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pipeline (see §13 decision 26).
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"""
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type: str
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name: str
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description: str
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"""Capability registry: ``types.py`` (dataclass), ``store.py`` (dict + register fn)."""
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"""Capability registry."""
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from __future__ import annotations
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"""Capability registry: in-memory dict + ``register_capability`` API."""
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"""In-memory capability registry. Populated once at process startup."""
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from __future__ import annotations
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@ -8,33 +8,16 @@ _REGISTRY: dict[str, Capability] = {}
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def register_capability(capability: Capability) -> None:
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"""Add a capability to the in-memory registry.
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Raises ``ValueError`` on duplicate ``id`` — registration is
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idempotent only at the module level (a module's
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``register_capability`` call runs once per process), so a
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duplicate is always a bug.
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"""
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"""Register a capability. Raises on duplicate id."""
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if capability.id in _REGISTRY:
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raise ValueError(
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f"Capability already registered: {capability.id!r}"
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)
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raise ValueError(f"Capability already registered: {capability.id!r}")
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_REGISTRY[capability.id] = capability
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def get_capability(capability_id: str) -> Capability | None:
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"""Look up one capability by id. Returns ``None`` on miss."""
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return _REGISTRY.get(capability_id)
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def all_capabilities() -> dict[str, Capability]:
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"""Snapshot of the registry as a defensive copy.
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Returned dict is safe to iterate while other code calls
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``register_capability`` (which v1 never does post-startup, but
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the contract holds anyway).
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"""
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"""Defensive snapshot of the registry."""
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return dict(_REGISTRY)
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@ -1,4 +1,4 @@
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"""``Capability`` dataclass — the v1-minimum five-field shape."""
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"""``Capability`` dataclass and handler signature. Locked at five fields for v1."""
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from __future__ import annotations
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@ -7,32 +7,10 @@ from dataclasses import dataclass
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from typing import Any
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CapabilityHandler = Callable[[dict[str, Any]], Awaitable[Any]]
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"""The signature every capability handler must satisfy.
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The handler is a closure that already holds whatever runtime context
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it needs (DB session, search-space scope, logger, etc.). The
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registry only passes through the caller's input dict — the same dict
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that was validated against ``input_schema``.
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"""
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@dataclass(frozen=True, slots=True)
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class Capability:
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"""The unit of "what SurfSense can do," consumed by every layer.
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v1 keeps the dataclass to exactly five fields. Earlier drafts
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considered ``name``, ``required_credentials``, ``side_effects``,
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``expected_duration_seconds``, and ``cost_estimate``; every one
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of those has been removed until a concrete consumer feature
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requires it (see ``automation-design-plan.md`` §3, decision v1).
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The handler is a ready-to-call function. It does not receive a
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context argument — context is bound at registration time by the
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factory that builds the closure (so a capability returned to an
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agent's tool list looks identical to one returned to an
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automation's action runtime).
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"""
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id: str
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description: str
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input_schema: dict[str, Any]
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"""Trigger registry: ``types.py`` (dataclass), ``store.py`` (dict + register fn)."""
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"""Trigger registry."""
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from __future__ import annotations
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"""Trigger registry: in-memory dict + ``register_trigger`` API."""
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"""In-memory trigger registry. Populated once at process startup."""
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from __future__ import annotations
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@ -8,26 +8,16 @@ _REGISTRY: dict[str, TriggerDefinition] = {}
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def register_trigger(trigger: TriggerDefinition) -> None:
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"""Add a trigger to the in-memory registry.
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Raises ``ValueError`` on duplicate ``type`` — registration runs
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once per process, so a duplicate is always a bug.
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"""
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"""Register a trigger. Raises on duplicate type."""
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if trigger.type in _REGISTRY:
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raise ValueError(
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f"Trigger already registered: {trigger.type!r}"
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)
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raise ValueError(f"Trigger already registered: {trigger.type!r}")
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_REGISTRY[trigger.type] = trigger
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def get_trigger(trigger_type: str) -> TriggerDefinition | None:
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"""Look up one trigger by type. Returns ``None`` on miss."""
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return _REGISTRY.get(trigger_type)
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def all_triggers() -> dict[str, TriggerDefinition]:
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"""Snapshot of the registry as a defensive copy."""
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"""Defensive snapshot of the registry."""
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return dict(_REGISTRY)
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"""``TriggerDefinition`` dataclass — declarative trigger metadata, no handler."""
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"""``TriggerDefinition`` dataclass. Declarative; firing is the dispatcher's job."""
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from __future__ import annotations
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@ -8,27 +8,6 @@ from typing import Any
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@dataclass(frozen=True, slots=True)
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class TriggerDefinition:
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"""A trigger type the dispatcher knows how to fire.
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Triggers are purely declarative: the dispatcher (a single
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process-wide component, not a per-type handler) reads the
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``automation_triggers`` table and decides when each row should
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fire. The trigger's job here is to declare its input/output
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contract:
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- ``config_schema``: JSON Schema for the persisted
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``AutomationTrigger.config`` — used by the form editor and
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validated on save.
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- ``payload_schema``: JSON Schema for the payload the dispatcher
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will deliver to the executor at fire time (e.g., a schedule
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trigger emits ``fired_at`` / ``scheduled_for`` /
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``last_fired_at``).
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No ``handler`` field — firing is a dispatcher responsibility,
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not a per-trigger one. This keeps the dispatcher single and
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leaves trigger types as pure metadata.
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"""
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type: str
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description: str
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config_schema: dict[str, Any]
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