AI-powered contractor matching and selection for construction projects. Analyze contractor capabilities, past performance, certifications, and project requirements to recommend optimal matches.
SkillScore100Maintained
Automatic
◆Procurement pack
Last verified 8/22/2026
What it changes
Passed 22 of 22 test cases with the skill, and 5 of 22 without it.
Without skill23%
With skill100%
Checks it passed that the model alone missed
Computes final safety score of 41
Computes total financial score of 87
Sets default price priority weight to 0.20
and 29 more in the full report
17 fixed5 passed either way0 still failing0 broke
Pass-rate lift
+77 pp
Tokens
+311%
Agent turns
0%
Response time
-45%
Model gemini-3.6-flashMeasured 8/22/2026v122 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 17
Prompt
A contractor bid evaluation yields a combined evaluation score of 58.0 and a proposal price deviation of +25% above the average bid. What recommendation label is assigned?
What was checked, without and with the skill
Checked
Without skill
With skill
Assigns Price Concerns status label when price deviation exceeds 20 percent
Missed
Passed
case-17 · gemini-3.6-flash · 8/22/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md735 linessingle file
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
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]