contractor-matching-ai
AI-powered contractor matching and selection for construction projects. Analyze contractor capabilities, past performance, certifications, and project requirements to recommend optimal matches.
npx skills add majiayu000/claude-skill-registry --skill contractor-matching-ai --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
AI-powered contractor matching for construction projects. It analyzes project requirements against contractor capabilities, track historical performance, and generate recommendations based on multiple criteria including technical capabilities, past performance, certifications, and region.
How it works
- Defines data structures for Contractor and ProjectRequirement, including fields like regions, specializations, certifications, performance_score, safety_score, capacity_available, and pricing.
- Implements a match_contractors(project, contractors, top_n) function that:
- Filters contractors by region, required work types, and certificate requirements.
- Computes a score per contractor based on project priority (quality, cost, safety, or speed) using formulas that combine performance, bid variance, and capacity.
- Returns the top_n contractors with their scores and matching metrics.
- Provides a Comprehensive Matching System with:
- ContractorProfile data model including performance, safety, capacity, certifications, and financials.
- AI Matching Engine with classes ProjectRequirements and ContractorMatchingEngine.
- Eligibility filtering in _filter_eligible considering region, work categories, project value range, certifications, licenses, capacity, and availability.
- Score calculation in _calculate_match_score using breakdowns for performance, safety, price, capacity, experience, and financials, then weighted total.
- Price score via _calculate_price_score, experience via _calculate_experience_score, and financial via _calculate_financial_score.
When to use it
Use when you need to identify high-fit contractors for a construction project based on region, required capabilities, certifications, project value, and priority (performance, price, capacity, etc.).
What it can touch
- Data structures: ContractorProfile, ProjectRequirements, ContractorMatchingEngine, ProjectRequirement, Contractor, and related fields.
- It uses project.region, contractor.operating_regions, contractor.specializations, contractor.certifications, contractor.licenses, contractor.current_workload_pct, contractor.earliest_availability, contractor.max_project_value/min_project_value, and historical_bid_data for scoring.
Caveats
- Relies on the presence and validity of contractor records (certifications, licenses, references, financials) to compute scores.
- If historical_bid_data is missing, price scoring defaults to 50.0.
- Scope and formulas are defined in the code; outcomes are not guaranteed and depend on input data quality and completeness.
# AI Contractor Matching ## Overview This skill implements AI-powered contractor matching for construction projects. Analyze project requirements against contractor capabilities, track historical performance, and generate recommendations based on multiple criteria. **Matching Criteria:** - Technical capabilities & expertise - Past performance scores - Certifications & licenses - Geographic availability - Capacity & current workload - Pricing competitiveness - Safety records ## Quick Start ```python from dataclasses import dataclass, field from typing import List, Dict, Optional from datetime import date import numpy as np @dataclass class Contractor: contractor_id: str name: str specializations: List[str] certifications: List[str] performance_score: float # 0-100 safety_score: float # 0-100 regions: List[str] capacity_available: float # 0-100 percentage avg_bid_variance: float # % above/below average @dataclass class ProjectRequirement: project_id: str work_types: List[str] required_certs: List[str] region: str estimated_value: float priority: str # cost, quality, speed, safety def match_contractors(project: ProjectRequirement, contractors: List[Contractor], top_n: int = 5) -> List
- Overview
- Quick Start
- Comprehensive Matching System
- Contractor Profile Management
- AI Matching Engine
- Bid Analysis and Prediction
- Contractor Recommendation Report
- Quick Reference
- Resources
- Next Steps
What does the contractor-matching-ai skill do?
AI-powered contractor matching and selection for construction projects. Analyze contractor capabilities, past performance, certifications, and project requirements to recommend optimal matches.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill contractor-matching-ai --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.