update description

Author: gopidesupavan

providers/dq/docs/assets.rst

@@ -63,11 +63,4 @@ Put the gate first in a Dag scheduled by the asset, and chain everything else af
         gate >> process_orders()
 
 ``min_score`` must be between ``0`` and ``1``; the check considers only the *most recent*
-triggering event for the asset when a run was triggered by several. A missing summary or a
-score below the threshold produces a warning-level log entry and a skipped run rather than a
-failed one -- a bad batch of upstream data is an expected, recoverable condition, not a bug in
-the consumer Dag.
-
-The two Dags are wired together only through the asset -- the producer above and the consumer
-here have no direct dependency on each other; either can be redeployed, renamed, or replaced
-independently as long as the asset name and its ``airflow.dq`` config stay the same.
+triggering event for the asset when a run was triggered by several.

providers/dq/src/airflow/providers/dq/rules/rule.py

@@ -47,8 +47,6 @@ class Condition(BaseModel):
     ``greater_than``, ``less_than``, ``geq_to``, ``leq_to``, plus a percentage
     ``tolerance`` that widens ``equal_to`` into a range.
 
-    Invalid input (e.g. no comparison set, or an unknown key) raises pydantic's own
-    :class:`~pydantic.ValidationError` -- there's no provider-specific wrapper around it.
     """
 
     model_config = ConfigDict(frozen=True, extra="forbid")
@@ -181,8 +179,6 @@ def _validate(self) -> DQRule:
     @property
     def rule_uid(self) -> str:
         """Stable identity across runs: survives severity/dimension tweaks and Dag refactors."""
-        # _validate() always fills in condition before returning; cast tells the type
-        # checker what the validator already guarantees at runtime.
         condition = cast("Condition", self.condition)
         identity = {
             "name": self.previous_name or self.name,

providers/dq/src/airflow/providers/dq/skills/dq-rule-authoring/references/ruleset.schema.json

@@ -2,7 +2,7 @@
   "$defs": {
     "Condition": {
       "additionalProperties": false,
-      "description": "Pass/fail condition evaluated against a rule's observed value.\n\nUses the same grammar as the ``common.sql`` check operators: ``equal_to``,\n``greater_than``, ``less_than``, ``geq_to``, ``leq_to``, plus a percentage\n``tolerance`` that widens ``equal_to`` into a range.\n\nInvalid input (e.g. no comparison set, or an unknown key) raises pydantic's own\n:class:`~pydantic.ValidationError` -- there's no provider-specific wrapper around it.",
+      "description": "Pass/fail condition evaluated against a rule's observed value.\n\nUses the same grammar as the ``common.sql`` check operators: ``equal_to``,\n``greater_than``, ``less_than``, ``geq_to``, ``leq_to``, plus a percentage\n``tolerance`` that widens ``equal_to`` into a range.",
       "properties": {
         "equal_to": {
           "anyOf": [