Prequalify subcontractors based on safety, financial, and performance criteria.
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill subcontractor-prequalification
import pandas as pd
from datetime import date
from typing import Dict, Any, List
from dataclasses import dataclass, field
from enum import Enum
class QualificationStatus(Enum):
PENDING = "pending"
QUALIFIED = "qualified"
CONDITIONALLY_QUALIFIED = "conditionally_qualified"
NOT_QUALIFIED = "not_qualified"
@dataclass
class PrequalificationCriteria:
name: str
weight: float
min_score: int
max_score: int = 10
@dataclass
class SubcontractorApplication:
app_id: str
company_name: str
trade: str
contact_email: str
years_in_business: int
annual_revenue: float
bonding_capacity: float
emr_rate: float # Experience Modification Rate
status: QualificationStatus
scores: Dict[str, int] = field(default_factory=dict)
documents_received: List[str] = field(default_factory=list)
notes: str = ""
@property
def total_score(self) -> float:
return sum(self.scores.values())
class SubcontractorPrequalification:
def __init__(self, project_name: str):
self.project_name = project_name
self.applications: Dict[str, SubcontractorApplication] = {}
self.criteria = self._default_criteria()
self._counter = 0
def _default_criteria(self) -> List[PrequalificationCriteria]:
return [
PrequalificationCriteria("Safety Record", 0.25, 6),
PrequalificationCriteria("Financial Stability", 0.20, 5),
PrequalificationCriteria("Experience", 0.20, 6),
PrequalificationCriteria("References", 0.15, 5),
PrequalificationCriteria("Capacity", 0.10, 5),
PrequalificationCriteria("Insurance/Bonding", 0.10, 7)
]
def add_application(self, company_name: str, trade: str, contact_email: str,
years_in_business: int, annual_revenue: float,
bonding_capacity: float, emr_rate: float) -> SubcontractorApplication:
self._counter += 1
app_id = f"PQ-{self._counter:03d}"
app = SubcontractorApplication(
app_id=app_id,
company_name=company_name,
trade=trade,
contact_email=contact_email,
years_in_business=years_in_business,
annual_revenue=annual_revenue,
bonding_capacity=bonding_capacity,
emr_rate=emr_rate,
status=QualificationStatus.PENDING
)
self.applications[app_id] = app
return app
def score_application(self, app_id: str, scores: Dict[str, int]):
if app_id not in self.applications:
return
app = self.applications[app_id]
app.scores = scores
self._evaluate_qualification(app)
def _evaluate_qualification(self, app: SubcontractorApplication):
passed = True
for criteria in self.criteria:
score = app.scores.get(criteria.name, 0)
if score < criteria.min_score:
passed = False
break
if passed and app.total_score >= 60:
app.status = QualificationStatus.QUALIFIED
elif app.total_score >= 50:
app.status = QualificationStatus.CONDITIONALLY_QUALIFIED
else:
app.status = QualificationStatus.NOT_QUALIFIED
def get_qualified(self, trade: str = None) -> List[SubcontractorApplication]:
qualified = [a for a in self.applications.values()
if a.status in [QualificationStatus.QUALIFIED,
QualificationStatus.CONDITIONALLY_QUALIFIED]]
if trade:
qualified = [a for a in qualified if a.trade.lower() == trade.lower()]
return qualified
def export_register(self, output_path: str):
data = [{
'ID': a.app_id,
'Company': a.company_name,
'Trade': a.trade,
'Years': a.years_in_business,
'Revenue': a.annual_revenue,
'EMR': a.emr_rate,
'Status': a.status.value,
'Score': a.total_score
} for a in self.applications.values()]
pd.DataFrame(data).to_excel(output_path, index=False)
prequal = SubcontractorPrequalification("Office Tower")
app = prequal.add_application("ABC Electric", "Electrical", "[email protected]",
years_in_business=15, annual_revenue=10000000,
bonding_capacity=5000000, emr_rate=0.85)
prequal.score_application(app.app_id, {
"Safety Record": 8, "Financial Stability": 7, "Experience": 8,
"References": 7, "Capacity": 8, "Insurance/Bonding": 9
})
qualified = prequal.get_qualified("Electrical")
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Take datadrivenconstruction/subcontractor-prequalification from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.