subcontractor-prequalification
Prequalify subcontractors based on safety, financial, and performance criteria.
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction /tmp/subcontractor-prequalification && cp -r /tmp/subcontractor-prequalification/1_DDC_Toolkit/Procurement/subcontractor-prequalification ~/.claude/skills/subcontractor-prequalificationSKILL.md
# Subcontractor Prequalification
## Technical Implementation
```python
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)
```
## Quick Start
```python
prequal = SubcontractorPrequalification("Office Tower")
app = prequal.add_application("ABC Electric", "Electrical", "info@abc.com",
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")
```
## Resources
- **DDC Book**: Chapter 3.4 - ProcurementGenerate automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.
Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.
Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
Generate comprehensive BIM model validation reports. Check data quality, completeness, and compliance with standards.
Calculate CO2 emissions and carbon footprint from BIM model data. Analyze embodied carbon by material, element, and building system.
Extract quantities from IFC/Revit models for quantity takeoff. Uses DDC converters to get element counts, areas, volumes, lengths with grouping and reporting.