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Provide data-driven decision support for construction. Analyze multiple factors and recommend optimal project decisions.
Passed 20 of 20 test cases with the skill, and 8 of 20 without it.
and 17 more in the full report
13 fixed9 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
For identifying weakness callouts in the 'Paving Segment 3' analysis, analysts flagged any score below 0.50. Under the decision framework rules, what maximum normalized score threshold determines if a bottom criterion is listed as a weakness?
| Checked | Without skill | With skill |
|---|---|---|
| The normalized score threshold for a weakness is 0.40 | Missed | Passed |
The instructions your model reads when the skill loads.
Construction decision-making challenges:
Multi-criteria decision support system for construction projects with weighted scoring, risk analysis, and scenario comparison.
import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import date, datetime
from enum import Enum
import math
class DecisionType(Enum):
VENDOR_SELECTION = "vendor_selection"
METHOD_SELECTION = "method_selection"
SCHEDULE_OPTION = "schedule_option"
DESIGN_ALTERNATIVE = "design_alternative"
RISK_RESPONSE = "risk_response"
RESOURCE_ALLOCATION = "resource_allocation"
class CriterionType(Enum):
COST = "cost"
TIME = "time"
QUALITY = "quality"
SAFETY = "safety"
RISK = "risk"
test cases passed, vs 8 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.