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Get Started Free →Find substitute materials using CWICR data. Identify equivalent alternatives based on function, cost, and availability.
.claude/skills/datadrivenconstruction-cwicr-material-substitution/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 100% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 185% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 181% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 224% | 0% |
Material substitution challenges:
Systematic material substitution using CWICR data to find functionally equivalent alternatives with cost and performance analysis.
pythonimport 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
python# Load CWICR data cwicr = pd.read_parquet("TR_workitems_costs_resources_DDC_CWICR.parquet") # Initialize substitution finder sub_finder = CWICRMaterialSubstitution(cwicr) # Find substitutes substitutes = sub_finder.find_substitutes("CONC-C30-001") for sub in substitutes[:5]: print(f"{sub.substitute_code}: ${sub.cost_difference:+.2f} ({sub.cost_difference_pct:+.1f}%)")
pythonsavings = sub_finder.find_cost_saving_alternatives("STEEL-S355", min_savings_pct=10) for s in savings: print(f"{s.substitute_code}: Save ${abs(s.cost_difference):.2f}/unit")
pythonmaterials = [ {'code': 'CONC-001', 'quantity': 200}, {'code': 'STEEL-002', 'quantity': 5000}, {'code': 'BRICK-003', 'quantity': 10000} ] batch = sub_finder.batch_substitution(materials) print(f"Potential Savings: ${batch['total_potential_savings']:,.2f}")
pythonconcrete_options = sub_finder.find_by_group('concrete') for opt in concrete_options[:5]: print(f"{opt['code']}: ${opt['cost']:.2f}/{opt['unit']}")
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 20,343 | 13,474 | -34% | 1 | 1 | 0% | 3,188 | 6,362 | +100% | 0 | 0 | — |
case-02 | fail→pass | 11,610 | 8,209 | -29% | 1 | 1 | 0% | 1,753 | 5,000 | +185% | 0 | 0 | — |
case-21 | pass→pass | 14,959 | 15,950 | +7% | 1 | 1 | 0% | 2,200 | 6,134 | +179% | 0 | 0 | — |
case-03 | pass→pass | 10,645 | 7,277 | -32% | 1 | 1 | 0% | 1,694 | 4,596 | +171% | 0 | 0 | — |
case-04 | fail→pass | 11,299 | 4,341 | -62% | 1 | 1 | 0% | 1,545 | 4,337 | +181% | 0 | 0 | — |
case-05 | fail→pass | 15,212 | 6,070 | -60% | 1 | 1 | 0% | 2,262 | 4,451 | +97% | 0 | 0 | — |
case-06 | fail→pass | 8,419 | 2,961 | -65% | 1 | 1 | 0% | 1,257 | 4,067 | +224% | 0 | 0 | — |
case-07 | pass→pass | 12,290 | 11,941 | -3% | 1 | 1 | 0% | 1,982 | 5,492 | +177% | 0 | 0 | — |
case-08 | pass→pass | 9,234 | 4,517 | -51% | 1 | 1 | 0% | 1,425 | 4,194 | +194% | 0 | 0 | — |
case-09 | pass→pass | 7,584 | 3,917 | -48% | 1 | 1 | 0% | 1,022 | 4,066 | +298% | 0 | 0 | — |
case-10 | fail→fail | 15,115 | 18,011 | +19% | 1 | 1 | 0% | 2,459 | 6,865 | +179% | 0 | 0 | — |
case-11 | fail→pass | 14,337 | 3,667 | -74% | 1 | 1 | 0% | 2,057 | 4,183 | +103% | 0 | 0 | — |
case-22 | pass→pass | 17,599 | 24,856 | +41% | 1 | 1 | 0% | 2,875 | 6,895 | +140% | 0 | 0 | — |
case-12 | pass→pass | 4,490 | 5,753 | +28% | 1 | 1 | 0% | 712 | 4,672 | +556% | 0 | 0 | — |
case-13 | fail→pass | 13,443 | 4,698 | -65% | 1 | 1 | 0% | 2,233 | 4,352 | +95% | 0 | 0 | — |
case-14 | pass→pass | 5,620 | 5,305 | -6% | 1 | 1 | 0% | 754 | 4,333 | +475% | 0 | 0 | — |
case-15 | pass→pass | 7,155 | 8,305 | +16% | 1 | 1 | 0% | 1,143 | 4,843 | +324% | 0 | 0 | — |
case-16 | pass→pass | 32,561 | 4,951 | -85% | 1 | 1 | 0% | 1,569 | 4,195 | +167% | 0 | 0 | — |
case-17 | fail→pass | 9,463 | 3,978 | -58% | 1 | 1 | 0% | 1,314 | 4,069 | +210% | 0 | 0 | — |
case-18 | fail→pass | 8,468 | 2,782 | -67% | 1 | 1 | 0% | 1,296 | 3,956 | +205% | 0 | 0 | — |
case-19 | pass→pass | 6,712 | 4,354 | -35% | 1 | 1 | 0% | 979 | 4,175 | +326% | 0 | 0 | — |
case-20 | fail→pass | 9,862 | 3,526 | -64% | 1 | 1 | 0% | 1,552 | 4,006 | +158% | 0 | 0 | — |
case-23 | pass→pass | 23,407 | 23,286 | -1% | 1 | 1 | 0% | 4,201 | 8,723 | +108% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +43 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/22/2026 | +41% |
Other measured skills in the registry, with their headline benchmark lift.