General
PromptBeginner5 minmarkdown
def score_timing(prospect: dict) -> tuple[float
str]:
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General
PromptBeginner5 minmarkdown
return 5.0
SMB opportunity
0
General
PromptBeginner5 minmarkdown
return 7.0
Mid-market opportunity
0
General
PromptBeginner5 minmarkdown
return 10.0
Enterprise-scale opportunity
0
General
PromptBeginner5 minmarkdown
revenue_estimate = size_indicators.get(revenue
0)
0
General
PromptBeginner5 minmarkdown
employee_count = size_indicators.get(employees
0)
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General
PromptBeginner5 minmarkdown
size_indicators = prospect.get(size_indicators
{})
0
General
PromptBeginner5 minmarkdown
def score_deal_size(prospect: dict) -> tuple[float
str]:
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General
PromptBeginner5 minmarkdown
- weights: {deal_size
timing
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General
PromptBeginner5 minmarkdown
education=[]
# Would be extracted from profile
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General
PromptBeginner5 minmarkdown
print(json.dumps(result
indent=2))
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General
PromptBeginner5 minmarkdown
) -> dict[str
Any]:
0
Data
PromptBeginner5 minmarkdown
def get_recent_activity(profile_data: dict
days: int = 30) -> list[dict]:
0
Coding & Debugging
PromptBeginner5 minmarkdown
In production
would use LinkedIn API or Sales Navigator.
0
Data
PromptBeginner5 minmarkdown
def get_mutual_connections(profile_data: dict
user_network: list = None) -> list[str]:
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Data
PromptBeginner5 minmarkdown
from dataclasses import dataclass
asdict
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General
PromptBeginner5 minmarkdown
print(json.dumps(result
indent=2))
0
General
PromptBeginner5 minmarkdown
def search_linkedin_profile(name: str = None
company: str = None
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General
PromptBeginner5 minmarkdown
sources_checked: [company_info
funding
0
General
PromptBeginner5 minmarkdown
competitors=[]
# Would be enriched from industry analysis
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General
PromptBeginner5 minmarkdown
funding=funding_info.get(total_funding
Unknown)
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General
PromptBeginner5 minmarkdown
basic_info = search_company_info(company_name
domain)
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General
PromptBeginner5 minmarkdown
company_domain = basic_info.get(domain
domain)
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General
PromptBeginner5 minmarkdown
dict with company profile including industry
size
0