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-material-substitutiongit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-material-substitution)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-material-substitution"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-material-substitution/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-material-substitution"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-material-substitution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.03088 |
| Opus 5 | $0.00014 | $0.01544 |
| Sonnet 5 | $0.00006 | $0.00618 |
| Haiku 4.5 | $0.00003 | $0.00309 |
Grade A, and why
cwicr-material-substitution 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 12d 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-material-substitution — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CWICR Material Substitution
Business Case
Problem Statement
Material substitution challenges:
- Supply chain issues
- Cost optimization
- Specification compliance
- Equivalent performance
Solution
Systematic material substitution using CWICR data to find functionally equivalent alternatives with cost and performance analysis.
Business Value
- Supply flexibility - Alternative sources
- Cost savings - Lower-cost equivalents
- Compliance - Specification matching
- Quick decisions - Rapid alternative search
Technical Implementation
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
from difflib import SequenceMatcher
class SubstitutionType(Enum):
"""Types of substitution."""
DIRECT = "direct" # Drop-in replacement
EQUIVALENT = "equivalent" # Same function, different material
UPGRADE = "upgrade" # Better performance
DOWNGRADE = "downgrade" # Lower performance (cost saving)
class CompatibilityLevel(Enum):
"""Compatibility levels."""
EXACT = "exact" # Identical specs
HIGH = "high" # Minor differences
MEDIUM = "medium" # Requires review
LOW = "low" # Significant differences
@dataclass
class MaterialSubstitute:
"""Material substitution option."""
original_code: str
original_description: str
substitute_code: str
substitute_description: str
substitution_type: SubstitutionType
compatibility: CompatibilityLevel
original_cost: float
substitute_cost: float
cost_difference: float
cost_difference_pct: float
notes: str
# Material compatibility groups
MATERIAL_GROUPS = {
'concrete': ['cement', 'beton', 'concrete', 'C20', 'C25', 'C30', 'C35', 'C40'],
'steel': ['steel', 'rebar', 'reinforcement', 'S235', 'S275', 'S355'],
'lumber': ['wood', 'timber', 'lumber', 'plywood', 'OSB'],
'masonry': ['brick', 'block', 'CMU', 'masonry'],
'insulation': ['insulation', 'rockwool', 'glasswool', 'EPS', 'XPS', 'PIR'],
'pipe': ['pipe', 'PVC', 'HDPE', 'copper', 'steel pipe'],
'electrical': ['wire', 'cable', 'conduit'],
'finishing': ['paint', 'plaster', 'drywall', 'gypsum'],
'flooring': ['tile', 'vinyl', 'laminate', 'carpet', 'hardwood'],
'roofing': ['shingle', 'membrane', 'metal roof', 'tile roof']
}
class CWICRMaterialSubstitution:
"""Find material substitutions using CWICR data."""
def __init__(self, cwicr_data: pd.DataFrame):
self.materials = cwicr_data
self._index_data()
def _index_data(self):
"""Index material data."""
if 'work_item_code' in self.materials.columns:
self._code_index = self.materials.set_index('work_item_code')
elif 'material_code' in self.materials.columns:
self._code_index = self.materials.set_index('material_code')
else:
self._code_index = None
def _similarity(self, a: str, b: str) -> float:
"""Calculate string similarity."""
return SequenceMatcher(None, a.lower(), b.lower()).ratio()
def _get_material_group(self, description: str) -> Optional[str]:
"""Identify material group from description."""
desc_lower = description.lower()
for group, keywords in MATERIAL_GROUPS.items():
if any(kw.lower() in desc_lower for kw in keywords):
return group
return None
def _get_cost(self, code: str) -> Tuple[float, str]:
"""Get material cost."""
if self._code_index is None or code not in self._code_index.index:
return (0, 'unit')
item = self._code_index.loc[code]
cost = float(item.get('material_cost', item.get('total_cost', 0)) or 0)
unit = str(item.get('unit', 'unit'))
return (cost, unit)
def find_substitutes(self,
material_code: str,
max_results: int = 10,
max_cost_increase: float = 0.20,
include_upgrades: bool = True) -> List[MaterialSubstitute]:
"""Find substitute materials."""
if self._code_index is None or material_code not in self._code_index.index:
return []
original = self._code_index.loc[material_code]
original_desc = str(original.get('description', material_code))
original_cost, original_unit = self._get_cost(material_code)
group = self._get_material_group(original_desc)
substitutes = []
for code, row in self._code_index.iterrows():
if code == material_code:
continue
sub_desc = str(row.get('description', code))
sub_group = self._get_material_group(sub_desc)
# Check if same group or similar description
if group and sub_group == group:
similarity = 0.7
else:
similarity = self._similarity(original_desc, sub_desc)
if similarity < 0.3:
continue
sub_cost, sub_unit = self._get_cost(code)
if sub_unit != original_unit:
continue
cost_diff = sub_cost - original_cost
cost_diff_pct = (cost_diff / original_cost * 100) if original_cost > 0 else 0
# Filter by cost increase limit
if not include_upgrades and cost_diff_pct > max_cost_increase * 100:
continue
# Determine substitution type
if cost_diff_pct < -10:
sub_type = SubstitutionType.DOWNGRADE
elif cost_diff_pct > 10:
sub_type = SubstitutionType.UPGRADE
elif similarity > 0.8:
sub_type = SubstitutionType.DIRECT
else:
sub_type = SubstitutionType.EQUIVALENT
# Determine compatibility
if similarity > 0.9:
compat = CompatibilityLevel.EXACT
elif similarity > 0.7:
compat = CompatibilityLevel.HIGH
elif similarity > 0.5:
compat = CompatibilityLevel.MEDIUM
else:
compat = CompatibilityLevel.LOW
substitutes.append(MaterialSubstitute(
original_code=material_code,
original_description=original_desc,
substitute_code=code,
substitute_description=sub_desc,
substitution_type=sub_type,
compatibility=compat,
original_cost=round(original_cost, 2),
substitute_cost=round(sub_cost, 2),
cost_difference=round(cost_diff, 2),
cost_difference_pct=round(cost_diff_pct, 1),
notes=f"Similarity: {similarity:.0%}"
))
# Sort by compatibility then cost
substitutes.sort(key=lambda x: (
list(CompatibilityLevel).index(x.compatibility),
x.cost_difference
))
return substitutes[:max_results]
def find_cost_saving_alternatives(self,
material_code: str,
min_savings_pct: float = 5.0) -> List[MaterialSubstitute]:
"""Find lower-cost alternatives."""
subs = self.find_substitutes(material_code, max_results=20)
cost_saving = [
s for s in subs
if s.cost_difference_pct <= -min_savings_pct
]
return sorted(cost_saving, key=lambda x: x.cost_difference)
def find_by_group(self,
group_name: str,
max_results: int = 20) -> List[Dict[str, Any]]:
"""Find all materials in a group."""
if self._code_index is None:
return []
results = []
for code, row in self._code_index.iterrows():
desc = str(row.get('description', code))
item_group = self._get_material_group(desc)
if item_group == group_name.lower():
cost, unit = self._get_cost(code)
results.append({
'code': code,
'description': desc,
'cost': cost,
'unit': unit,
'group': item_group
})
return sorted(results, key=lambda x: x['cost'])[:max_results]
def substitution_impact(self,
original_code: str,
substitute_code: str,
quantity: float) -> Dict[str, Any]:
"""Calculate impact of substitution."""
original_cost, _ = self._get_cost(original_code)
substitute_cost, _ = self._get_cost(substitute_code)
original_total = original_cost * quantity
substitute_total = substitute_cost * quantity
impact = substitute_total - original_total
return {
'original_code': original_code,
'substitute_code': substitute_code,
'quantity': quantity,
'original_unit_cost': original_cost,
'substitute_unit_cost': substitute_cost,
'original_total': round(original_total, 2),
'substitute_total': round(substitute_total, 2),
'cost_impact': round(impact, 2),
'impact_percent': round(impact / original_total * 100, 1) if original_total > 0 else 0
}
def batch_substitution(self,
materials: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Find substitutions for multiple materials."""
results = []
total_original = 0
total_potential_savings = 0
for mat in materials:
code = mat.get('material_code', mat.get('code'))
qty = mat.get('quantity', 1)
subs = self.find_cost_saving_alternatives(code)
original_cost, _ = self._get_cost(code)
original_total = original_cost * qty
total_original += original_total
best_sub = subs[0] if subs else None
potential_savings = 0
if best_sub:
impact = self.substitution_impact(code, best_sub.substitute_code, qty)
potential_savings = abs(impact['cost_impact']) if impact['cost_impact'] < 0 else 0
total_potential_savings += potential_savings
results.append({
'code': code,
'quantity': qty,
'original_total': round(original_total, 2),
'best_substitute': best_sub.substitute_code if best_sub else None,
'potential_savings': round(potential_savings, 2),
'alternatives_count': len(subs)
})
return {
'materials': results,
'total_original_cost': round(total_original, 2),
'total_potential_savings': round(total_potential_savings, 2),
'savings_percent': round(total_potential_savings / total_original * 100, 1) if total_original > 0 else 0
}
def export_substitution_report(self,
substitutes: List[MaterialSubstitute],
output_path: str) -> str:
"""Export substitution report to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
df = pd.DataFrame([
{
'Original Code': s.original_code,
'Original Description': s.original_description,
'Substitute Code': s.substitute_code,
'Substitute Description': s.substitute_description,
'Type': s.substitution_type.value,
'Compatibility': s.compatibility.value,
'Original Cost': s.original_cost,
'Substitute Cost': s.substitute_cost,
'Cost Difference': s.cost_difference,
'Difference %': s.cost_difference_pct,
'Notes': s.notes
}
for s in substitutes
])
df.to_excel(writer, sheet_name='Substitutes', index=False)
return output_path
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.
- 12d ago First seen · 395 lines · 28 tokens per session scan A 0653be97c646
cwicr-material-substitution is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 21d ago), licensed MIT. It adds 28 tokens to every session and 3,088 once invoked, about $0.0001 per session on Opus 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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