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chore: import upstream snapshot with attribution
2026-07-13 12:02:32 +08:00

72 lines
2.2 KiB
Python

"""Skills views - wraps SDK types with helper methods"""
from typing import Any
from browser_use_sdk import ParameterSchema, SkillResponse
from pydantic import BaseModel, ConfigDict, Field
class MissingCookieException(Exception):
"""Raised when a required cookie is missing for skill execution
Attributes:
cookie_name: The name of the missing cookie parameter
cookie_description: Description of how to obtain the cookie
"""
def __init__(self, cookie_name: str, cookie_description: str):
self.cookie_name = cookie_name
self.cookie_description = cookie_description
super().__init__(f"Missing required cookie '{cookie_name}': {cookie_description}")
class Skill(BaseModel):
"""Skill model with helper methods for LLM integration
This wraps the SDK SkillResponse with additional helper properties
for converting schemas to Pydantic models.
"""
model_config = ConfigDict(extra='forbid', validate_assignment=True)
id: str
title: str
description: str
parameters: list[ParameterSchema]
output_schema: dict[str, Any] = Field(default_factory=dict)
@staticmethod
def from_skill_response(response: SkillResponse) -> 'Skill':
"""Create a Skill from SDK SkillResponse"""
return Skill(
id=str(response.id),
title=response.title,
description=response.description,
parameters=response.parameters,
output_schema=response.output_schema,
)
def parameters_pydantic(self, exclude_cookies: bool = False) -> type[BaseModel]:
"""Convert parameter schemas to a pydantic model for structured output
exclude_cookies is very useful when dealing with LLMs that are not aware of cookies.
"""
from browser_use.skills.utils import convert_parameters_to_pydantic
parameters = list[ParameterSchema](self.parameters)
if exclude_cookies:
parameters = [param for param in parameters if param.type != 'cookie']
return convert_parameters_to_pydantic(parameters, model_name=f'{self.title}Parameters')
@property
def output_type_pydantic(self) -> type[BaseModel] | None:
"""Convert output schema to a pydantic model for structured output"""
if not self.output_schema:
return None
from browser_use.skills.utils import convert_json_schema_to_pydantic
return convert_json_schema_to_pydantic(self.output_schema, model_name=f'{self.title}Output')