Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cwicr-unit-convertergit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
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[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-unit-converter)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-unit-converter"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-unit-converter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-unit-converter"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-unit-converter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.04784 |
| Opus 5.5 | $0.00014 | $0.01914 |
| Sonnet 5.5 | $0.00007 | $0.00957 |
| Haiku 4.5 | $0.00003 | $0.00478 |
Grade A, and why
cwicr-unit-converter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- cwicr-unit-converter — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CWICR Unit Converter
Business Case
Problem Statement
Construction data comes in various unit systems:
- Metric vs Imperial measurements
- Different unit conventions by trade
- BIM quantities need normalization
- Regional standards differ
Solution
Comprehensive unit conversion for construction quantities, normalizing data for CWICR integration and analysis.
Business Value
- Accuracy - Eliminate unit conversion errors
- Consistency - Standardize across projects
- Integration - BIM to cost data alignment
- Global - Support international projects
Technical Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple, Union
from dataclasses import dataclass
from enum import Enum
class UnitCategory(Enum):
"""Categories of measurement units."""
LENGTH = "length"
AREA = "area"
VOLUME = "volume"
WEIGHT = "weight"
TIME = "time"
QUANTITY = "quantity"
class UnitSystem(Enum):
"""Unit systems."""
METRIC = "metric"
IMPERIAL = "imperial"
MIXED = "mixed"
@dataclass
class UnitConversion:
"""Unit conversion result."""
original_value: float
original_unit: str
converted_value: float
target_unit: str
conversion_factor: float
category: UnitCategory
# Conversion factors to base units
# Base units: meter (length), m² (area), m³ (volume), kg (weight), hour (time)
CONVERSIONS = {
# Length to meters
'm': {'factor': 1.0, 'category': UnitCategory.LENGTH, 'base': 'm'},
'meter': {'factor': 1.0, 'category': UnitCategory.LENGTH, 'base': 'm'},
'meters': {'factor': 1.0, 'category': UnitCategory.LENGTH, 'base': 'm'},
'cm': {'factor': 0.01, 'category': UnitCategory.LENGTH, 'base': 'm'},
'mm': {'factor': 0.001, 'category': UnitCategory.LENGTH, 'base': 'm'},
'km': {'factor': 1000.0, 'category': UnitCategory.LENGTH, 'base': 'm'},
'ft': {'factor': 0.3048, 'category': UnitCategory.LENGTH, 'base': 'm'},
'feet': {'factor': 0.3048, 'category': UnitCategory.LENGTH, 'base': 'm'},
'foot': {'factor': 0.3048, 'category': UnitCategory.LENGTH, 'base': 'm'},
'in': {'factor': 0.0254, 'category': UnitCategory.LENGTH, 'base': 'm'},
'inch': {'factor': 0.0254, 'category': UnitCategory.LENGTH, 'base': 'm'},
'inches': {'factor': 0.0254, 'category': UnitCategory.LENGTH, 'base': 'm'},
'yd': {'factor': 0.9144, 'category': UnitCategory.LENGTH, 'base': 'm'},
'yard': {'factor': 0.9144, 'category': UnitCategory.LENGTH, 'base': 'm'},
'yards': {'factor': 0.9144, 'category': UnitCategory.LENGTH, 'base': 'm'},
'mi': {'factor': 1609.344, 'category': UnitCategory.LENGTH, 'base': 'm'},
'mile': {'factor': 1609.344, 'category': UnitCategory.LENGTH, 'base': 'm'},
'lf': {'factor': 0.3048, 'category': UnitCategory.LENGTH, 'base': 'm'}, # Linear foot
# Area to m²
'm2': {'factor': 1.0, 'category': UnitCategory.AREA, 'base': 'm2'},
'm²': {'factor': 1.0, 'category': UnitCategory.AREA, 'base': 'm2'},
'sqm': {'factor': 1.0, 'category': UnitCategory.AREA, 'base': 'm2'},
'cm2': {'factor': 0.0001, 'category': UnitCategory.AREA, 'base': 'm2'},
'mm2': {'factor': 0.000001, 'category': UnitCategory.AREA, 'base': 'm2'},
'ha': {'factor': 10000.0, 'category': UnitCategory.AREA, 'base': 'm2'},
'hectare': {'factor': 10000.0, 'category': UnitCategory.AREA, 'base': 'm2'},
'ft2': {'factor': 0.092903, 'category': UnitCategory.AREA, 'base': 'm2'},
'sf': {'factor': 0.092903, 'category': UnitCategory.AREA, 'base': 'm2'},
'sqft': {'factor': 0.092903, 'category': UnitCategory.AREA, 'base': 'm2'},
'yd2': {'factor': 0.836127, 'category': UnitCategory.AREA, 'base': 'm2'},
'sy': {'factor': 0.836127, 'category': UnitCategory.AREA, 'base': 'm2'}, # Square yard
'acre': {'factor': 4046.86, 'category': UnitCategory.AREA, 'base': 'm2'},
# Volume to m³
'm3': {'factor': 1.0, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'm³': {'factor': 1.0, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'cbm': {'factor': 1.0, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'l': {'factor': 0.001, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'liter': {'factor': 0.001, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'litre': {'factor': 0.001, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'ml': {'factor': 0.000001, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'ft3': {'factor': 0.0283168, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'cf': {'factor': 0.0283168, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'cuft': {'factor': 0.0283168, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'yd3': {'factor': 0.764555, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'cy': {'factor': 0.764555, 'category': UnitCategory.VOLUME, 'base': 'm3'}, # Cubic yard
'cuyd': {'factor': 0.764555, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'gal': {'factor': 0.00378541, 'category': UnitCategory.VOLUME, 'base': 'm3'},
'gallon': {'factor': 0.00378541, 'category': UnitCategory.VOLUME, 'base': 'm3'},
# Weight to kg
'kg': {'factor': 1.0, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'kilogram': {'factor': 1.0, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'g': {'factor': 0.001, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'gram': {'factor': 0.001, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'mg': {'factor': 0.000001, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
't': {'factor': 1000.0, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'ton': {'factor': 1000.0, 'category': UnitCategory.WEIGHT, 'base': 'kg'}, # Metric ton
'tonne': {'factor': 1000.0, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'mt': {'factor': 1000.0, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'lb': {'factor': 0.453592, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'lbs': {'factor': 0.453592, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'pound': {'factor': 0.453592, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'oz': {'factor': 0.0283495, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'ounce': {'factor': 0.0283495, 'category': UnitCategory.WEIGHT, 'base': 'kg'},
'st': {'factor': 907.185, 'category': UnitCategory.WEIGHT, 'base': 'kg'}, # Short ton (US)
# Time to hours
'hr': {'factor': 1.0, 'category': UnitCategory.TIME, 'base': 'hr'},
'hour': {'factor': 1.0, 'category': UnitCategory.TIME, 'base': 'hr'},
'hours': {'factor': 1.0, 'category': UnitCategory.TIME, 'base': 'hr'},
'h': {'factor': 1.0, 'category': UnitCategory.TIME, 'base': 'hr'},
'min': {'factor': 1/60, 'category': UnitCategory.TIME, 'base': 'hr'},
'minute': {'factor': 1/60, 'category': UnitCategory.TIME, 'base': 'hr'},
'day': {'factor': 8.0, 'category': UnitCategory.TIME, 'base': 'hr'}, # 8-hour workday
'days': {'factor': 8.0, 'category': UnitCategory.TIME, 'base': 'hr'},
'week': {'factor': 40.0, 'category': UnitCategory.TIME, 'base': 'hr'}, # 40-hour week
# Quantity (no conversion, just counting)
'ea': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'each': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'pc': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'pcs': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'piece': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'pieces': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'no': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'nr': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'set': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'lot': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'},
'ls': {'factor': 1.0, 'category': UnitCategory.QUANTITY, 'base': 'ea'}, # Lump sum
}
class CWICRUnitConverter:
"""Convert between construction units."""
def __init__(self):
self.conversions = CONVERSIONS
def normalize_unit(self, unit: str) -> str:
"""Normalize unit string for lookup."""
return str(unit).lower().strip().replace(' ', '').replace('.', '')
def get_unit_info(self, unit: str) -> Optional[Dict[str, Any]]:
"""Get conversion info for unit."""
normalized = self.normalize_unit(unit)
return self.conversions.get(normalized)
def convert(self,
value: float,
from_unit: str,
to_unit: str) -> UnitConversion:
"""Convert value between units."""
from_info = self.get_unit_info(from_unit)
to_info = self.get_unit_info(to_unit)
if not from_info:
raise ValueError(f"Unknown source unit: {from_unit}")
if not to_info:
raise ValueError(f"Unknown target unit: {to_unit}")
if from_info['category'] != to_info['category']:
raise ValueError(
f"Cannot convert between {from_info['category'].value} and {to_info['category'].value}"
)
# Convert: source -> base -> target
base_value = value * from_info['factor']
converted_value = base_value / to_info['factor']
conversion_factor = from_info['factor'] / to_info['factor']
return UnitConversion(
original_value=value,
original_unit=from_unit,
converted_value=round(converted_value, 6),
target_unit=to_unit,
conversion_factor=conversion_factor,
category=from_info['category']
)
def to_metric(self, value: float, from_unit: str) -> UnitConversion:
"""Convert to standard metric unit."""
info = self.get_unit_info(from_unit)
if not info:
raise ValueError(f"Unknown unit: {from_unit}")
base_unit = info['base']
return self.convert(value, from_unit, base_unit)
def to_imperial(self, value: float, from_unit: str) -> UnitConversion:
"""Convert to common imperial unit."""
info = self.get_unit_info(from_unit)
if not info:
raise ValueError(f"Unknown unit: {from_unit}")
imperial_map = {
'm': 'ft',
'm2': 'sf',
'm3': 'cy',
'kg': 'lb',
'hr': 'hr'
}
base = info['base']
imperial_unit = imperial_map.get(base, base)
return self.convert(value, from_unit, imperial_unit)
def convert_dataframe(self,
df: pd.DataFrame,
value_column: str,
unit_column: str,
target_unit: str,
output_column: str = None) -> pd.DataFrame:
"""Convert units in DataFrame column."""
result = df.copy()
if output_column is None:
output_column = f"{value_column}_converted"
converted_values = []
for _, row in df.iterrows():
try:
conversion = self.convert(
row[value_column],
row[unit_column],
target_unit
)
converted_values.append(conversion.converted_value)
except ValueError:
converted_values.append(None)
result[output_column] = converted_values
result[f'{output_column}_unit'] = target_unit
return result
def normalize_units(self,
df: pd.DataFrame,
value_column: str,
unit_column: str) -> pd.DataFrame:
"""Normalize all units to base metric units."""
result = df.copy()
normalized_values = []
normalized_units = []
for _, row in df.iterrows():
try:
conversion = self.to_metric(row[value_column], row[unit_column])
normalized_values.append(conversion.converted_value)
normalized_units.append(conversion.target_unit)
except ValueError:
normalized_values.append(row[value_column])
normalized_units.append(row[unit_column])
result[f'{value_column}_normalized'] = normalized_values
result[f'{unit_column}_normalized'] = normalized_units
return result
class ConstructionUnitHelper:
"""Helper for construction-specific unit operations."""
def __init__(self):
self.converter = CWICRUnitConverter()
def calculate_area(self,
length: float, length_unit: str,
width: float, width_unit: str,
result_unit: str = 'm2') -> float:
"""Calculate area from length and width."""
# Convert both to meters
length_m = self.converter.convert(length, length_unit, 'm').converted_value
width_m = self.converter.convert(width, width_unit, 'm').converted_value
# Calculate area in m²
area_m2 = length_m * width_m
# Convert to requested unit
return self.converter.convert(area_m2, 'm2', result_unit).converted_value
def calculate_volume(self,
length: float, length_unit: str,
width: float, width_unit: str,
height: float, height_unit: str,
result_unit: str = 'm3') -> float:
"""Calculate volume from dimensions."""
# Convert all to meters
length_m = self.converter.convert(length, length_unit, 'm').converted_value
width_m = self.converter.convert(width, width_unit, 'm').converted_value
height_m = self.converter.convert(height, height_unit, 'm').converted_value
# Calculate volume in m³
volume_m3 = length_m * width_m * height_m
# Convert to requested unit
return self.converter.convert(volume_m3, 'm3', result_unit).converted_value
def concrete_volume(self,
length_ft: float,
width_ft: float,
thickness_in: float) -> Dict[str, float]:
"""Calculate concrete volume (common US method)."""
# Convert to meters
length_m = self.converter.convert(length_ft, 'ft', 'm').converted_value
width_m = self.converter.convert(width_ft, 'ft', 'm').converted_value
thickness_m = self.converter.convert(thickness_in, 'in', 'm').converted_value
volume_m3 = length_m * width_m * thickness_m
volume_cy = self.converter.convert(volume_m3, 'm3', 'cy').converted_value
return {
'm3': round(volume_m3, 3),
'cy': round(volume_cy, 2)
}
def rebar_weight(self,
length: float, length_unit: str,
bar_size: str) -> Dict[str, float]:
"""Calculate rebar weight from length and bar size."""
# Rebar weight per meter (kg/m) - US bar sizes
rebar_weights = {
'#3': 0.561, '#4': 0.996, '#5': 1.556,
'#6': 2.24, '#7': 3.049, '#8': 3.982,
'#9': 5.06, '#10': 6.41, '#11': 7.91
}
weight_per_m = rebar_weights.get(bar_size, 1.0)
length_m = self.converter.convert(length, length_unit, 'm').converted_value
weight_kg = length_m * weight_per_m
weight_lb = self.converter.convert(weight_kg, 'kg', 'lb').converted_value
return {
'kg': round(weight_kg, 2),
'lb': round(weight_lb, 2),
'ton': round(weight_kg / 1000, 4)
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 440 lines · 34 tokens per session scan A d4e1a5c62fbc
cwicr-unit-converter is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (373 stars, last pushed 8d ago), licensed MIT. It adds 34 tokens to every session and 4,784 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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