| name | wastewise-regulatory |
| description | WasteWise Complete Analysis with automated regulatory compliance research and LLM Judge validation. Includes all standard analysis features PLUS automated ordinance research, compliance checklists, and quality-scored evaluation. Use when you need both waste analysis AND regulatory compliance documentation. |
WasteWise Regulatory - Complete Analysis with Compliance Research
What This Skill Does
Enhanced version of WasteWise Complete Analysis with:
Core Validation Framework
- ✅ Contract tabs are generated when contracts are provided
- ✅ Contract clauses are properly extracted and categorized
- ✅ Optimization recommendations meet strict criteria
- ✅ All formulas are correctly calculated
- ✅ Data completeness across all sheets
- ✅ Professional formatting standards
- ✅ Cross-sheet data consistency
NEW: Regulatory Compliance Research
- ✅ Automated research of local waste/recycling/organics ordinances
- ✅ Extraction of mandatory requirements and thresholds
- ✅ Documentation of penalties and enforcement
- ✅ Licensed hauler identification
- ✅ Compliance checklist generation
- ✅ Confidence scoring for research quality
This skill will NOT produce output until ALL validation checks pass, including regulatory compliance research validation.
Pre-Flight Validation Checklist
Before generating the final workbook, this skill runs a mandatory validation suite:
1. Contract Validation
☐ If contract file detected → CONTRACT_TERMS sheet MUST be created
☐ Extract 7 clause types: Term & Renewal, Rate Increases, Termination, Liability,
Service Level, Force Majeure, Indemnification
☐ Calendar reminders calculated for critical dates
☐ Verbatim clause text extracted (not paraphrased)
☐ Risk severity assigned (high/medium/low)
2. Optimization Validation
☐ Compactor optimization: Only if avg < 6 tons/haul AND 14-day max interval
☐ Contamination reduction: Only if charges > 3-5% of spend
☐ Bulk subscription: Only if avg > $500/month
☐ Per-compactor pricing validated (not per-property)
☐ ROI calculations include all costs (install + monitoring)
☐ 14-day constraint enforced in recommendations
3. Formula Validation
☐ Yards per door: Correct formula for equipment type
- Compactors: (Total Tons × 14.49) / Units
- Dumpsters: (Qty × Size × Freq × 4.33) / Units
☐ Cost per door: Total Monthly Cost / Units
☐ Capacity utilization: (Tons Per Haul / Target Tons) × 100%
☐ Days between pickups: 30 / (Hauls Per Month)
4. Sheet Structure Validation
☐ SUMMARY_FULL: 2026 savings one-liner at top
☐ EXPENSE_ANALYSIS: Month-by-month COLUMN format
☐ OPTIMIZATION: All 3 opportunities with calculation breakdowns
☐ QUALITY_CHECK: Confidence scores and validation metrics
☐ DOCUMENTATION_NOTES: Vendor contacts, formulas, glossary
☐ HAUL_LOG: Created only if compactor present
☐ CONTRACT_TERMS: Created only if contract provided
☐ REGULATORY_COMPLIANCE: Created with location-based research
5. Data Completeness Validation
☐ Property name extracted
☐ Unit count specified
☐ All invoice dates parsed
☐ Tonnage data present (if compactor)
☐ Service types identified
☐ Account numbers captured
☐ Vendor name extracted
☐ Location data extracted (city, county, state, zip)
6. Cross-Validation
☐ SUMMARY totals match EXPENSE_ANALYSIS
☐ HAUL_LOG tonnage matches OPTIMIZATION calculations
☐ CONTRACT_TERMS dates align with calendar reminders
☐ Cost per door consistent across all sheets
☐ REGULATORY_COMPLIANCE location matches property data
7. NEW: Regulatory Compliance Validation
☐ Location data successfully extracted from property documents
☐ Minimum 3 official government sources (.gov) consulted
☐ Waste, recycling, AND organics requirements researched
☐ Mandatory vs voluntary status explicitly documented
☐ Capacity requirements are numerical (not "adequate" or "sufficient")
☐ Service frequencies are specific (not "regular" or "as needed")
☐ Penalty amounts documented with $ figures
☐ At least 3-5 licensed haulers identified with full contact info
☐ Ordinance citations include chapter/section numbers
☐ Property size thresholds verified (4+, 5+, 8+, 10+ units)
☐ Recent regulatory changes (2023-2025) checked
☐ Confidence score assigned (HIGH/MEDIUM/LOW)
Regulatory Compliance Research Protocol
Phase 1: Extract Location Data
From uploaded documentation, identify:
- City name
- County (if available)
- State
- Zip code
- Property type and size (unit count)
- Building stories/height (for threshold determination)
Phase 2: Conduct Regulatory Research
Search Pattern Sequence:
"[City Name] [State]" waste recycling ordinance"[City Name]" universal recycling ordinance"[City Name]" mandatory composting multifamily"[City Name]" solid waste code"[County Name] County" waste management requirements"[State]" recycling law commercial properties
Priority Sources:
- Municipal solid waste/sanitation department websites (.gov)
- City/county ordinance databases (Municode, American Legal)
- State environmental agency waste division pages
- Regional waste authority sites
Phase 3: Extract Regulatory Requirements
For each waste stream (Trash, Recycling, Composting/Organics), document:
TRASH/WASTE COLLECTION
- Municipal service availability for property size
- Private licensed hauler requirement (mandatory/optional)
- Minimum service frequency requirements
- Container size/type specifications
- Placement restrictions
- Licensed hauler directory URL
RECYCLING REQUIREMENTS
- Mandatory vs voluntary status
- Property size threshold for mandate
- Minimum capacity requirement (% of waste, gallons per unit, or total volume)
- Accepted materials list
- Service frequency minimum
- Container specifications (size, type, color)
- Placement requirements
- Signage requirements (language, symbols, content)
- Co-location rules
COMPOSTING/ORGANICS REQUIREMENTS
- Mandatory vs voluntary status
- Effective date (especially 2023-2025 mandates)
- Property size threshold
- Minimum capacity requirement
- Service frequency minimum
- Accepted materials (food scraps, food-soiled paper, yard waste, BPI-certified)
- Container specifications
- Resident education requirements
Phase 4: Document Penalties & Enforcement
Extract:
- Violation classification (misdemeanor, civil, criminal)
- Fine structure (per offense, per day, maximum)
- Enforcement agency name and contact
- Warning vs citation procedures
- Repeat violation escalation
Phase 5: Identify Licensed Haulers
Compile minimum 3-5 haulers with:
- Company name
- Phone number
- Website URL
- Service capabilities (waste, recycling, composting, compactor hauls)
- Official hauler directory URL
Phase 6: Generate Compliance Checklist
Create property-specific checklist showing:
- ✅ Requirements currently met
- ⚠️ Requirements needing attention
- ❌ Requirements not met
- 📅 Upcoming compliance deadlines
Confidence Scoring System
After completing regulatory research, assign confidence level:
HIGH CONFIDENCE
- All waste streams documented with official ordinance citations
- Penalty amounts and enforcement agency confirmed
- Licensed hauler directory found and verified
- Reporting requirements fully documented
- All sources from official .gov domains
- Capacity requirements are specific numerical values
- Recent regulatory changes confirmed
MEDIUM CONFIDENCE
- Core requirements found but some details missing
- Ordinance cited but specific sections unclear
- Hauler list incomplete (fewer than 3 haulers)
- Some sources from non-official websites
- Mixed specificity in requirements
LOW CONFIDENCE - FLAG FOR HUMAN REVIEW
- Limited official information available
- Conflicting sources found
- Recent changes not confirmed
- No licensed hauler directory located
- Key requirements vague or missing
- Property size threshold unclear
- REQUIRES MANUAL VERIFICATION
Enhanced Validation Implementation
Regulatory Compliance Validator Class
class RegulatoryComplianceValidator:
"""Validates regulatory compliance research quality"""
def __init__(self):
self.validation_results = {
'location_extraction': {},
'source_quality': {},
'requirement_specificity': {},
'completeness': {},
'confidence_assessment': {}
}
self.errors = []
self.warnings = []
self.confidence_score = None
def validate_regulatory_research(self, regulatory_data: Dict,
property_info: Dict) -> Tuple[bool, str]:
"""
Validate regulatory compliance research quality
Returns: (passed: bool, confidence_level: str)
"""
# 1. Validate location extraction
location_valid = self.validate_location_data(
regulatory_data.get('location', {}),
property_info
)
# 2. Validate source quality
sources_valid = self.validate_sources(
regulatory_data.get('sources', [])
)
# 3. Validate requirement specificity
specificity_valid = self.validate_requirement_specificity(
regulatory_data.get('requirements', {})
)
# 4. Validate completeness
completeness_valid = self.validate_completeness(
regulatory_data
)
# 5. Assess confidence level
confidence_level = self.assess_confidence(
location_valid, sources_valid, specificity_valid, completeness_valid
)
# Determine if research passed minimum standards
passed = confidence_level in ['HIGH', 'MEDIUM']
if confidence_level == 'LOW':
self.errors.append(
"❌ REGULATORY RESEARCH CONFIDENCE TOO LOW: "
"Research quality insufficient for automated compliance assessment. "
"HUMAN REVIEW REQUIRED."
)
return passed, confidence_level
def validate_location_data(self, location: Dict, property_info: Dict) -> bool:
"""Validate location extraction quality"""
required_fields = ['city', 'state']
optional_fields = ['county', 'zip_code']
missing_required = [f for f in required_fields if not location.get(f)]
if missing_required:
self.errors.append(
f"❌ LOCATION DATA INCOMPLETE: Missing required fields: "
f"{', '.join(missing_required)}"
)
return False
# Check for property size data
if not property_info.get('unit_count'):
self.warnings.append(
"⚠️ Unit count not specified - may affect threshold applicability"
)
self.validation_results['location_extraction'] = {
'status': 'PASSED',
'city': location.get('city'),
'state': location.get('state'),
'county': location.get('county'),
'unit_count': property_info.get('unit_count')
}
return True
def validate_sources(self, sources: List[Dict]) -> bool:
"""Validate research source quality"""
if len(sources) < 3:
self.errors.append(
f"❌ INSUFFICIENT SOURCES: Only {len(sources)} sources consulted. "
f"Minimum 3 required."
)
return False
# Check for .gov sources
gov_sources = [s for s in sources if '.gov' in s.get('url', '')]
if len(gov_sources) == 0:
self.warnings.append(
"⚠️ No official .gov sources found - relying on secondary sources"
)
self.validation_results['source_quality'] = {
'status': 'PASSED' if len(gov_sources) >= 1 else 'WARNING',
'total_sources': len(sources),
'gov_sources': len(gov_sources),
'source_list': [s.get('name', 'Unknown') for s in sources]
}
return True
def validate_requirement_specificity(self, requirements: Dict) -> bool:
"""Validate that requirements are specific and measurable"""
vague_terms = ['adequate', 'sufficient', 'appropriate', 'regular', 'as needed']
specificity_issues = []
# Check recycling requirements
recycling = requirements.get('recycling', {})
capacity = recycling.get('capacity_requirement', '')
if any(term in str(capacity).lower() for term in vague_terms):
specificity_issues.append("Recycling capacity uses vague terms")
if recycling.get('service_frequency', '') in ['regular', 'as needed', '']:
specificity_issues.append("Recycling frequency not specific")
# Check composting requirements
composting = requirements.get('composting', {})
if composting.get('mandatory'):
comp_capacity = composting.get('capacity_requirement', '')
if any(term in str(comp_capacity).lower() for term in vague_terms):
specificity_issues.append("Composting capacity uses vague terms")
# Check penalties
penalties = requirements.get('penalties', {})
if not penalties.get('fine_per_offense') or '$' not in str(penalties.get('fine_per_offense')):
specificity_issues.append("Penalty amounts not specified with $ values")
if specificity_issues:
for issue in specificity_issues:
self.warnings.append(f"⚠️ SPECIFICITY: {issue}")
self.validation_results['requirement_specificity'] = {
'status': 'PASSED' if len(specificity_issues) == 0 else 'WARNING',
'issues_found': len(specificity_issues),
'issues': specificity_issues
}
return len(specificity_issues) == 0
def validate_completeness(self, regulatory_data: Dict) -> bool:
"""Validate completeness of regulatory research"""
requirements = regulatory_data.get('requirements', {})
# Check all three waste streams are addressed
waste_streams = ['waste', 'recycling', 'composting']
missing_streams = []
for stream in waste_streams:
if stream not in requirements or not requirements[stream]:
missing_streams.append(stream)
if missing_streams:
self.warnings.append(
f"⚠️ INCOMPLETE RESEARCH: Missing waste streams: {', '.join(missing_streams)}"
)
# Check for licensed haulers
haulers = regulatory_data.get('licensed_haulers', [])
if len(haulers) < 3:
self.warnings.append(
f"⚠️ INSUFFICIENT HAULERS: Only {len(haulers)} licensed haulers found. "
f"Minimum 3 recommended."
)
# Check for ordinance citations
ordinances = regulatory_data.get('ordinances', [])
if len(ordinances) == 0:
self.errors.append(
"❌ NO ORDINANCE CITATIONS: No ordinances referenced in research"
)
return False
self.validation_results['completeness'] = {
'status': 'PASSED' if len(missing_streams) == 0 else 'WARNING',
'waste_streams_covered': len(waste_streams) - len(missing_streams),
'haulers_found': len(haulers),
'ordinances_cited': len(ordinances)
}
return len(missing_streams) <= 1 # Allow 1 missing stream
def assess_confidence(self, location_valid: bool, sources_valid: bool,
specificity_valid: bool, completeness_valid: bool) -> str:
"""
Assess overall confidence level for regulatory research
Returns: 'HIGH', 'MEDIUM', or 'LOW'
"""
# Count validations passed
validations_passed = sum([
location_valid,
sources_valid,
specificity_valid,
completeness_valid
])
# Get detailed metrics
source_quality = self.validation_results.get('source_quality', {})
requirement_specificity = self.validation_results.get('requirement_specificity', {})
completeness = self.validation_results.get('completeness', {})
gov_sources = source_quality.get('gov_sources', 0)
specificity_issues = requirement_specificity.get('issues_found', 0)
haulers_found = completeness.get('haulers_found', 0)
# HIGH CONFIDENCE criteria
if (validations_passed == 4 and
gov_sources >= 2 and
specificity_issues == 0 and
haulers_found >= 3):
confidence = 'HIGH'
# MEDIUM CONFIDENCE criteria
elif (validations_passed >= 3 and
gov_sources >= 1 and
specificity_issues <= 2):
confidence = 'MEDIUM'
# LOW CONFIDENCE
else:
confidence = 'LOW'
self.confidence_score = confidence
self.validation_results['confidence_assessment'] = {
'level': confidence,
'validations_passed': f"{validations_passed}/4",
'gov_sources': gov_sources,
'specificity_issues': specificity_issues,
'haulers_found': haulers_found
}
return confidence
Integration with Main Validator
class WasteWiseValidator:
"""Comprehensive validation framework for WasteWise Analysis"""
def __init__(self):
self.validation_results = {
'contract_validation': {},
'optimization_validation': {},
'formula_validation': {},
'sheet_structure_validation': {},
'data_completeness_validation': {},
'cross_validation': {},
'regulatory_compliance_validation': {} # NEW
}
self.errors = []
self.warnings = []
self.regulatory_validator = RegulatoryComplianceValidator() # NEW
def validate_all(self, invoice_data: List[Dict], contract_data: Dict,
property_info: Dict, optimization_results: Dict,
regulatory_data: Dict) -> Tuple[bool, Dict]: # NEW parameter
"""
Run all validation checks including regulatory compliance
Returns: (passed: bool, validation_report: dict)
"""
# 1-6. Original validations (contract, optimization, formula, etc.)
contract_valid = self.validate_contract(contract_data, invoice_data)
optimization_valid = self.validate_optimizations(optimization_results, invoice_data)
formula_valid = self.validate_formulas(invoice_data, property_info)
structure_valid = self.validate_sheet_structure(
invoice_data, contract_data, optimization_results, regulatory_data # NEW
)
completeness_valid = self.validate_data_completeness(
invoice_data, property_info
)
cross_valid = self.validate_cross_references(
invoice_data, optimization_results, contract_data
)
# 7. NEW: Regulatory compliance validation
regulatory_valid, confidence_level = self.regulatory_validator.validate_regulatory_research(
regulatory_data, property_info
)
# Store regulatory validation results
self.validation_results['regulatory_compliance_validation'] = {
'status': 'PASSED' if regulatory_valid else 'FAILED',
'confidence_level': confidence_level,
'details': self.regulatory_validator.validation_results
}
# Merge errors and warnings from regulatory validator
self.errors.extend(self.regulatory_validator.errors)
self.warnings.extend(self.regulatory_validator.warnings)
all_passed = all([
contract_valid,
optimization_valid,
formula_valid,
structure_valid,
completeness_valid,
cross_valid,
regulatory_valid # NEW
])
return all_passed, self.generate_validation_report()
REGULATORY_COMPLIANCE Sheet Structure
Section 1: Jurisdiction Overview
SECTION 1: REGULATORY COMPLIANCE - [City, State]
Governing Ordinances:
- [City Code Chapter X-Y: Full Name]
- [County Code Section X: Full Name]
- [State Law/Statute: Full Name]
Property Classification: [Based on unit count/size threshold]
Regulatory Summary: [1-2 sentences on key mandates]
Section 2: Waste Collection Requirements
Municipal Service: Available / Not Available
Private Hauler Requirement: ✅ MANDATORY / ⚠️ OPTIONAL
Key Requirements:
- Licensed hauler required: Yes/No
- Minimum service frequency: [X times per week]
- Container requirements: [Size, type specifications]
- Placement restrictions: [Details]
Licensed Hauler Directory: [URL]
Section 3: Recycling Requirements
MANDATORY STATUS: ✅ MANDATORY / ⚠️ VOLUNTARY
Capacity Requirements:
- Minimum capacity: [Specific measurement]
- Based on: [Formula or standard]
Service Requirements:
- Minimum frequency: [Specific]
- Accepted materials: [List]
Container Specifications:
- Size/type: [Details]
- Color requirements: [If any]
- Placement: [Requirements]
Signage Requirements:
- Languages: [Required languages]
- Required symbols: [Details]
- Content: [Required information]
Compliance Checklist:
✅ [Specific requirement with measurable standard]
✅ [Specific requirement with measurable standard]
⚠️ [Requirement needing attention]
Section 4: Composting/Organics Requirements
⚠️ IMPORTANT: [Note if recent requirement]
MANDATORY STATUS: ✅ MANDATORY / ⚠️ VOLUNTARY
Effective Date: [If applicable]
Capacity Requirements:
- Minimum capacity: [Specific measurement]
- Formula: [If provided]
Service Requirements:
- Minimum frequency: [Specific]
- Accepted materials:
✓ Food scraps
✓ Food-soiled paper
✓ Yard waste [if applicable]
✓ BPI-certified compostables [if accepted]
Container Specifications:
- Size: [Details]
- Type: [Details]
- Features: [Locking lids, color, etc.]
Resident Education:
- Required materials: [Details]
- Language requirements: [Details]
Compliance Checklist:
✅ [Specific requirement]
⚠️ [Requirement needing attention]
❌ [Requirement not met]
Section 5: Penalties & Enforcement
Violation Type: [Classification]
Fine Structure:
- Per offense: Up to $[amount]
- Per day: Up to $[amount] per day
- Maximum: $[amount] total
Enforcement:
- Agency: [Name]
- Contact: [Phone and email]
Example Violations:
- [List common violations]
Section 6: Licensed Haulers
FULL-SERVICE PROVIDERS:
1. [Company Name]
Phone: [Number]
Website: [URL]
Services: Waste, recycling, composting, compactor hauls
2. [Company Name]
Phone: [Number]
Website: [URL]
Services: [List]
[Additional haulers...]
Official Hauler Directory: [URL]
Section 7: Regulatory Contacts
PRIMARY AGENCY:
Agency: [Full name]
Phone: [Number]
Email: [Address]
Website: [URL]
COMPLIANCE QUESTIONS:
Contact: [Name or department]
Phone: [Number]
Email: [Address]
Section 8: Research Confidence Assessment
RESEARCH QUALITY ASSESSMENT
Confidence Level: [HIGH / MEDIUM / LOW]
Quality Metrics:
- Government sources consulted: [Number]
- Official ordinances cited: [Number]
- Licensed haulers identified: [Number]
- Specificity issues: [Number]
[If LOW confidence:]
⚠️ HUMAN REVIEW REQUIRED
This research requires manual verification due to:
- [List specific concerns]
- [Conflicting information found]
- [Recent regulatory changes unconfirmed]
Complete Workflow with Regulatory Compliance
Step 1: Initial Data Processing
- Process uploaded invoices
- Extract contract (if provided)
- NEW: Extract location data from property documents
- Identify property characteristics (units, type, building height)
Step 2: Regulatory Research Phase
- Execute search pattern sequence
- Consult minimum 3 official sources
- Extract waste, recycling, and organics requirements
- Document penalties and licensed haulers
- Generate compliance checklists
- Assign confidence score
Step 3: Comprehensive Validation
- Run contract validation
- Run optimization validation
- Run formula validation
- Run sheet structure validation
- Run data completeness validation
- Run cross-validation
- NEW: Run regulatory compliance validation
- Assess overall confidence level
Step 4: Validation Gate
- HIGH/MEDIUM Confidence: Proceed to output
- LOW Confidence: HALT and flag for human review
Step 5: Generate Output
- Create all standard sheets (SUMMARY, EXPENSE_ANALYSIS, OPTIMIZATION, etc.)
- NEW: Create REGULATORY_COMPLIANCE sheet
- Create QUALITY_CHECK sheet with regulatory confidence score
- Generate executive summary with compliance status
Enhanced Validation Report Example
🔐 STEP 3: Validation Gate - Running All Checks...
------------------------------------------------------------
📊 VALIDATION RESULTS:
✅ Contract Validation: PASSED
✅ Optimization Validation: PASSED
✅ Formula Validation: PASSED
✅ Sheet Structure Validation: PASSED
✅ Data Completeness Validation: PASSED
✅ Cross Validation: PASSED
✅ Regulatory Compliance Validation: PASSED (CONFIDENCE: HIGH)
🏛️ REGULATORY RESEARCH SUMMARY:
Location: Austin, Texas
Sources Consulted: 5 (.gov: 3, Other: 2)
Ordinances Cited: 3
Licensed Haulers Found: 6
Confidence Level: HIGH
Key Findings:
✅ Universal Recycling Ordinance applies (8+ units)
✅ Mandatory composting effective January 2024
✅ Minimum 1:1 recycling:waste capacity ratio required
⚠️ Annual reporting required by February 1, 2026
⚠️ WARNINGS:
⚠️ Property may need additional compost capacity
⚠️ Annual reporting deadline in 85 days
============================================================
VALIDATION SUMMARY:
Total Checks: 7
Passed: 7
Failed: 0
Warnings: 2
Regulatory Confidence: HIGH
============================================================
✅ ALL VALIDATIONS PASSED - Proceeding to output generation
Low Confidence Example - Human Review Required
🔐 STEP 3: Validation Gate - Running All Checks...
------------------------------------------------------------
📊 VALIDATION RESULTS:
✅ Contract Validation: PASSED
✅ Optimization Validation: PASSED
✅ Formula Validation: PASSED
✅ Sheet Structure Validation: PASSED
✅ Data Completeness Validation: PASSED
✅ Cross Validation: PASSED
❌ Regulatory Compliance Validation: FAILED (CONFIDENCE: LOW)
🏛️ REGULATORY RESEARCH SUMMARY:
Location: [City], [State]
Sources Consulted: 2 (.gov: 0, Other: 2)
Ordinances Cited: 0
Licensed Haulers Found: 1
Confidence Level: LOW
Issues Identified:
❌ No official .gov sources found
❌ No ordinance citations located
❌ Insufficient hauler information
⚠️ Conflicting information on composting mandate
⚠️ Recent ordinance changes (2024) not confirmed
🚨 HUMAN REVIEW REQUIRED 🚨
The regulatory compliance research did not meet minimum quality
standards for automated assessment. Manual verification needed for:
1. Composting mandate status (conflicting sources)
2. Property size threshold applicability
3. Enforcement penalties (not documented)
4. Licensed hauler requirements
Recommend: Contact [City] Solid Waste Department directly
Phone: [If available]
Website: [If available]
============================================================
VALIDATION SUMMARY:
Total Checks: 7
Passed: 6
Failed: 1
Warnings: 4
Regulatory Confidence: LOW - MANUAL REVIEW REQUIRED
============================================================
❌ VALIDATION FAILED - Output generation halted
Please complete manual regulatory verification before proceeding.
Required Libraries
- anthropic - Claude API for document processing and web research
- pandas - Data manipulation and analysis
- openpyxl - Excel workbook generation with formatting
- python-dateutil - Date parsing and calendar calculations
- typing - Type hints for validation functions
- requests - Web research for regulatory compliance
- beautifulsoup4 - HTML parsing for ordinance extraction
Example Usage
User prompt: "I uploaded 6 months of invoices, the waste service contract, and property documents for The Club at Millenia (560 units, Austin TX). Run the validated analysis with regulatory compliance research."
Claude will:
- ✅ Process all invoices and extract contract
- ✅ Extract location data (Austin, Travis County, Texas)
- ✅ Research Austin waste/recycling/composting ordinances
- ✅ Consult official sources (austintexas.gov, Travis County)
- ✅ Extract Universal Recycling Ordinance requirements
- ✅ Document composting mandate (effective 2024)
- ✅ Identify 5+ licensed haulers
- ✅ Run comprehensive validation suite (7 categories, 40+ checks)
- ✅ Assign confidence level (HIGH/MEDIUM/LOW)
- ✅ HALT if LOW confidence or any validation fails
- ✅ Generate REGULATORY_COMPLIANCE sheet
- ✅ Generate CONTRACT_TERMS sheet
- ✅ Create HAUL_LOG if compactor detected
- ✅ Validate all formulas and calculations
- ✅ Cross-reference data across sheets
- ✅ Generate validated Excel workbook with quality report
Output files:
TheClubAtMillenia_WasteAnalysis_Validated.xlsx- Complete workbook with all sheets including regulatory compliance- Executive summary with validation status and regulatory confidence level
Key Principles
- Validation-First - No output until ALL checks pass (including regulatory)
- Research Quality - Minimum 3 sources, preference for .gov domains
- Specificity Required - Vague terms trigger warnings or failures
- Confidence Transparency - Clear scoring of research quality
- Human-in-the-Loop - LOW confidence requires manual review
- Contract-Aware - Mandatory CONTRACT_TERMS if contract provided
- Compliance-Focused - Property-specific regulatory checklists
- Formula Accuracy - Validates every calculation
- Cross-Referenced - Ensures data consistency across sheets
- Quality Assurance - Built-in QUALITY_CHECK sheet with regulatory confidence
This enhanced validated edition provides enterprise-grade quality control for waste management analysis with comprehensive regulatory compliance research.