negotiation-expert-infrastructure

name	negotiation-expert-infrastructure
description	Use when negotiating property acquisitions for utility easements, transit corridors, land assembly, or expropriation settlements; calculating BATNA, ZOPA, or optimal settlement ranges; assessing holdout risk; or generating concession strategies for infrastructure property owners.

Negotiation Settlement Calculator - SKILL Documentation

Overview

Expert skill for calculating BATNA, ZOPA, probability-weighted expected value, and optimal settlement range for property acquisition negotiations in infrastructure projects.

Primary Use Cases:

• Utility easement acquisitions (transmission lines, pipelines, telecom)
• Transit corridor negotiations (LRT, subway, BRT)
• Land assembly for infrastructure projects
• Expropriation settlement negotiations

Key Outputs:

• BATNA (Best Alternative to Negotiated Agreement) analysis
• ZOPA (Zone of Possible Agreement) calculation
• Probability-weighted scenario analysis
• Optimal settlement range and strategy
• Holdout risk assessment
• Concession strategy recommendations

When to Use This Skill

Trigger Phrases

This skill should be invoked when users mention:

Negotiation Analysis:

• "Calculate BATNA for this acquisition"
• "What's the ZOPA for this negotiation?"
• "Should we settle or go to hearing?"
• "What's our walkaway point?"
• "Analyze settlement vs. expropriation hearing"

Settlement Strategy:

• "What should our opening offer be?"
• "Generate concession strategy"
• "What's the optimal settlement range?"
• "Calculate expected value of settlement"

Risk Assessment:

• "Assess holdout risk for this owner"
• "What's the probability of reaching settlement?"
• "Analyze negotiating power"

Document Type Detection

Auto-invoke when reading:

• *settlement*analysis*.json
• *negotiation*input*.json
• *batna*zopa*.json
• Files in ${CLAUDE_PLUGIN_ROOT}/skills/negotiation-expert-infrastructure/samples/

Core Concepts

BATNA (Best Alternative to Negotiated Agreement)

Your best option if negotiation fails - in infrastructure acquisitions, this is typically proceeding to expropriation hearing.

Components:

• Expected award (probability-weighted)
• Legal fees
• Expert witness fees
• Time costs (delay, staff resources)

Formula:

Net BATNA = Expected Award + Total Costs
Expected Award = Σ(Probability_i × Award_i)

Interpretation:

• Net BATNA is your walkaway point - don't settle for more than this
• Strong BATNA = better negotiating position
• Weak BATNA = incentive to settle

ZOPA (Zone of Possible Agreement)

The range where both parties' reservation prices overlap - where a deal is possible.

Components:

• Lower Bound: Seller's minimum (reservation price)
• Upper Bound: Buyer's maximum (BATNA or budget limit)
• Midpoint: Fair split of surplus
• Range: Width of negotiating zone

Existence:

• ZOPA Exists: Buyer Max ≥ Seller Min → Settlement possible
• No ZOPA: Seller Min > Buyer Max → Proceed to hearing

Leverage:

• Distance from your reservation price to midpoint indicates negotiating power
• Larger share of ZOPA = stronger position

Probability-Weighted Expected Value

Monte Carlo-style analysis of negotiation outcomes.

Scenarios:

1. Settle at seller's minimum (best case for buyer)
2. Settle at ZOPA midpoint (most likely)
3. Settle near buyer's maximum (if buyer has weak position)
4. Proceed to hearing (negotiation failure)

Output:

• Expected value across all scenarios
• Best case / Worst case
• Standard deviation (risk measure)

Optimal Settlement Range

Recommended negotiation strategy based on BATNA, ZOPA, and confidence level.

Components:

• Opening Offer: Start below target to leave room for concessions
• Target Settlement: Aim for this amount (typically ZOPA midpoint or better)
• Floor: Don't go below this (aggressive but defensible)
• Ceiling: Don't exceed this (approaching walkaway)
• Walkaway Point: BATNA or buyer maximum (whichever is lower)

Confidence Level:

• 0.8 (80%) = Moderate risk tolerance - wider range
• 0.9 (90%) = Lower risk tolerance - narrower range around midpoint
• 0.7 (70%) = Higher risk tolerance - aggressive range

Holdout Risk Assessment

Probability that owner will refuse reasonable offers and force expropriation.

Risk Factors (0-30 scale):

1. Motivation (0-12):

   • Financial need (inverse) - high need = low holdout
   • Emotional attachment - high attachment = high holdout
   • Business impact - critical impact = high holdout

2. Sophistication (0-10):

   • Real estate experience - high experience = higher holdout
   • Legal representation - increases holdout
   • Previous negotiations - experience increases holdout

3. Alternatives (0-8):

   • Relocation options - fewer = higher holdout
   • Financial flexibility - low = lower holdout (need money)
   • Timeline pressure - high pressure = lower holdout

Risk Levels:

• 0-9: LOW (15% holdout probability)
• 10-14: MEDIUM (30% holdout probability)
• 15-19: HIGH (50% holdout probability)
• 20+: CRITICAL (70% holdout probability)

Calculator Architecture

Modular Structure

negotiation-expert-infrastructure/
├── negotiation_settlement_calculator.py  # Main orchestrator
├── modules/
│   ├── validators.py                     # Input validation
│   ├── calculations.py                   # BATNA/ZOPA/EV calculations
│   ├── analysis.py                       # Risk & strategy analysis
│   └── output_formatters.py              # Report generation
├── samples/
│   └── sample_1_farmer_easement.json     # Sample input
├── negotiation_settlement_input_schema.json  # JSON Schema
└── SKILL.md                              # This documentation

Shared Utilities Used

From Shared_Utils/negotiation_utils.py:

• calculate_batna() - BATNA analysis
• calculate_zopa() - ZOPA calculation
• probability_weighted_ev() - Scenario analysis
• optimal_settlement_range() - Settlement recommendations
• calculate_concession_strategy() - Diminishing concessions

From Shared_Utils/risk_utils.py:

• assess_holdout_risk() - Holdout risk scoring

From Shared_Utils/report_utils.py:

• All markdown formatting functions
• Executive summary generation
• Risk assessment formatting

Calculator Usage

Command Line

# Basic usage
python negotiation_settlement_calculator.py samples/sample_1_farmer_easement.json

# With JSON output
python negotiation_settlement_calculator.py samples/sample_1_farmer_easement.json \
  --output results.json

# With markdown report
python negotiation_settlement_calculator.py samples/sample_1_farmer_easement.json \
  --report settlement_analysis.md \
  --property "125-acre farm easement"

# Verbose mode
python negotiation_settlement_calculator.py samples/sample_1_farmer_easement.json \
  --verbose

Python API

from negotiation_settlement_calculator import NegotiationSettlementCalculator

# Load input data
with open('samples/sample_1_farmer_easement.json') as f:
    input_data = json.load(f)

# Create calculator
calculator = NegotiationSettlementCalculator(input_data, verbose=True)

# Validate
if not calculator.validate():
    print("Validation failed")
    exit(1)

# Calculate
results = calculator.calculate()

# Generate report
report = calculator.generate_report("125-acre farm easement")
print(report)

# Access specific results
batna_net = results['batna']['net_batna']
zopa_exists = results['zopa']['exists']
target_settlement = results['optimal_settlement']['target']

Input Data Schema

Required Fields

{
  "buyer_max": 200000,
  "seller_min": 150000,
  "hearing_probabilities": {
    "low_award": 0.2,
    "mid_award": 0.5,
    "high_award": 0.3
  },
  "hearing_costs": {
    "low_award_amount": 140000,
    "mid_award_amount": 175000,
    "high_award_amount": 210000,
    "legal_fees": 50000,
    "expert_fees": 30000,
    "time_cost": 15000
  }
}

Optional Fields

{
  "owner_profile": {
    "motivation": {
      "financial_need": "low",
      "emotional_attachment": "high",
      "business_impact": "critical"
    },
    "sophistication": {
      "real_estate_experience": "high",
      "legal_representation": true,
      "previous_negotiations": 2
    },
    "alternatives": {
      "relocation_options": "limited",
      "financial_flexibility": "medium",
      "timeline_pressure": "low"
    }
  },
  "settlement_offer": 180000,
  "legal_costs_to_settle": 5000,
  "confidence_level": 0.8,
  "num_negotiation_rounds": 3,
  "property_description": "125-acre farm easement"
}

Sample Output

Console Summary

================================================================================
NEGOTIATION SETTLEMENT ANALYSIS
================================================================================
Property: 125-acre farm property - transmission line easement acquisition

Key Metrics:
  Buyer Maximum:         $200,000
  Seller Minimum:        $150,000
  Net BATNA (Hearing):   $270,000

ZOPA:
  Range:                 $150,000 - $200,000
  Midpoint:              $175,000

Optimal Settlement:
  Opening Offer:         $162,500
  Target:                $175,000
  Walkaway:              $200,000

Recommendation:      SETTLE
Confidence:          MEDIUM

Settlement at $175,000 saves $95,000 compared to hearing ($270,000).
ZOPA exists with range of $50,000. However, holdout risk is HIGH
(50% probability), which may complicate negotiations.
================================================================================

Markdown Report Sections

1. Executive Summary - Key metrics and recommendation
2. BATNA Analysis - Expected award, costs, net BATNA
3. ZOPA Analysis - Range, midpoint, leverage
4. Probability-Weighted Scenarios - Expected value across outcomes
5. Optimal Settlement Range - Opening, target, floor, ceiling, walkaway
6. Holdout Risk Assessment - Score, level, factors, mitigation
7. Settlement vs. Hearing - Cost-benefit comparison
8. Concession Strategy - Round-by-round offer progression
9. Action Items - Prioritized next steps

Negotiation Strategy Recommendations

When ZOPA Exists (Recommended: SETTLE)

Opening Strategy:

1. Open at 75% between seller min and target (leaves room for concessions)
2. Anchor below ZOPA midpoint to establish favorable reference point
3. Justify opening with comparable sales data

Concession Pattern:

• Use diminishing concessions (50% → 25% → 12.5% of remaining gap)
• Signals approaching limit without revealing walkaway point
• Final concession should be small to show you're at your maximum

Settlement Range:

• Aggressive: 25th percentile of ZOPA (favor buyer)
• Balanced: ZOPA midpoint (equal surplus)
• Defensive: 75th percentile of ZOPA (if weak position)

When No ZOPA (Recommended: PROCEED TO HEARING)

Options:

1. Walk Away - If gap is large and unlikely to close
2. Offer at Buyer Max - Test if seller will accept (low probability)
3. Improve BATNA - Find ways to reduce hearing costs or expected award
4. Create Value - Explore non-price terms (timing, partial taking, etc.)

Holdout Risk Mitigation

High Emotional Attachment:

• Emphasize fair compensation and respectful process
• Acknowledge family history and connection to land
• Consider non-monetary compensation (relocation assistance, etc.)

Limited Alternatives:

• Provide relocation assistance
• Help owner find comparable replacement property
• Consider phased acquisition or temporary easement

Sophisticated Owner:

• Use professional appraisals and market comparables
• Engage in principled negotiation (objective criteria)
• Avoid aggressive tactics that may backfire

Critical Business Impact:

• Negotiate transition timeline
• Minimize operational disruption
• Consider business continuity support

Real-World Application Examples

Example 1: Agricultural Easement (Sample Input)

Scenario:

• 125-acre farm, transmission line easement
• Sophisticated farmer with legal counsel
• High emotional attachment, critical farm impact
• Limited relocation options

Analysis:

• ZOPA exists: $150K - $200K
• Net BATNA: $270K (hearing cost)
• Holdout risk: HIGH (22/30 score, 50% probability)

Recommendation:

• Open at $162,500
• Target $175,000
• Settle if possible below $200K
• Be prepared for difficult negotiation due to high holdout risk
• Emphasize fair compensation and minimize farm disruption

Example 2: Commercial Property (Downtown Transit)

Scenario:

• Commercial building, subway station site
• Owner needs cash for retirement
• Multiple comparable properties available
• Moderate negotiating experience

Typical Profile:

• ZOPA likely exists (motivated seller)
• Lower holdout risk (financial need + alternatives)
• BATNA may be less attractive to owner

Strategy:

• Open closer to seller minimum
• Faster concession progression (seller motivated)
• Settlement likely near lower end of ZOPA

Example 3: Industrial Site (Pipeline Corridor)

Scenario:

• Industrial yard, pipeline easement
• Business-critical operations
• Few relocation alternatives
• Previous successful negotiations with other utilities

Typical Profile:

• ZOPA may not exist initially
• HIGH holdout risk (critical operations)
• Experienced negotiator

Strategy:

• Expect difficult negotiation
• Focus on operational mitigation
• May need to offer near or above buyer maximum
• Consider phased construction or alternative routing

---

Negotiation Strategy Planner - SKILL Documentation

Overview

Companion tool that develops a comprehensive negotiation approach based on owner psychology, property characteristics, and hearing alternatives. Where the Settlement Calculator quantifies BATNA/ZOPA numbers, the Strategy Planner translates those numbers into a field-ready action plan.

Python Calculator: negotiation_strategy_planner.py Input Schema: negotiation_strategy_input_schema.json

Primary Use Cases:

• Preparing for first contact with a property owner
• Determining opening offer positioning and concession sequencing
• Classifying owner type to select the right communication approach
• Generating a 3-phase negotiation plan (Opening → Negotiation → Closure)

When to Use This Tool

Use the Strategy Planner when you need tactical guidance — owner classification, communication tone, concession sequencing — as opposed to the Settlement Calculator which provides quantitative BATNA/ZOPA numbers. For complex acquisitions, run both: calculator first (numbers), planner second (field plan).

Trigger Phrases:

• "Develop a negotiation approach for this owner"
• "How should I open with this farmer?"
• "What's our concession plan?"
• "Classify this owner type"
• "Generate a 3-phase negotiation plan"
• "Assess holdout risk for this owner profile"

Calculator Usage

Command Line

# Single combined input file
python negotiation_strategy_planner.py owner_and_property.json

# Separate owner profile and property data files
python negotiation_strategy_planner.py owner_profile.json property_data.json

# Custom output path
python negotiation_strategy_planner.py samples/farmer_profile.json \
  --output $CLAUDE_PROJECT_DIR/Reports/strategy.md

Slash Command

/negotiation-strategy <owner-profile-json> [<property-data-json>]

Owner Psychology Framework

Five Owner Types

Type	Profile	Negotiation Implication
RATIONAL_INVESTOR	Maximize value, evidence-driven	Lead with comparable sales and data
LEGACY_HOLDER	Emotional attachment, multi-generational	Acknowledge history; non-monetary terms matter
OPERATING_BUSINESS	Business continuity critical	Emphasize timeline flexibility and disruption mitigation
FINANCIAL_DISTRESS	Needs liquidity quickly	Speed and certainty of close outweigh price
SOPHISTICATED_HOLDOUT	Strategic, patient, well-advised	Principled negotiation only; avoid aggressive tactics

Holdout Risk Assessment (0-30 Scale)

The planner scores three factor domains to produce a 0-30 holdout risk score:

1. Motivation (0-12): Financial need (inverse), emotional attachment, business impact
2. Sophistication (0-10): Real estate experience, legal representation, prior negotiations
3. Alternatives (0-8): Relocation options, financial flexibility, timeline pressure

Risk Levels:

• 0-9: LOW (15% holdout probability)
• 10-14: MEDIUM (30% holdout probability)
• 15-19: HIGH (50% holdout probability)
• 20+: CRITICAL (70% holdout probability)

Input Data Schema

Owner Profile Fields

{
  "owner_type": "LEGACY_HOLDER",
  "motivation": {
    "financial_need": "low",
    "emotional_attachment": "high",
    "business_impact": "critical"
  },
  "sophistication": {
    "real_estate_experience": "high",
    "legal_representation": true,
    "previous_negotiations": 2
  },
  "alternatives": {
    "relocation_options": "limited",
    "financial_flexibility": "medium",
    "timeline_pressure": "low"
  }
}

Property / Deal Fields

{
  "property_description": "125-acre farm easement",
  "acquisition_type": "easement",
  "opening_offer": 162500,
  "target_settlement": 175000,
  "walkaway_point": 200000,
  "num_concession_rounds": 3
}

Output: Negotiation Strategy Report

The planner generates a 10-section report:

1. Owner Psychology Analysis — Owner type classification, sophistication rating, key motivations
2. Holdout Risk Assessment — 0-30 score, risk level, probability, contributing factors
3. BATNA Analysis — Hearing expected value, costs, net BATNA (drawn from settlement calculator output or estimated)
4. ZOPA Analysis — Zone of possible agreement, leverage position
5. Settlement Range Recommendation — Opening / target / floor / ceiling / walkaway
6. Opening Offer Strategy — Risk-adjusted positioning with owner-type-specific messaging
7. Concession Plan — 2-4 rounds using diminishing pattern (50% → 25% → 12.5% of remaining gap) with tactical notes per round
8. Communication Strategy — Tone, rapport tactics, and key messages tailored to the classified owner type
9. Leverage Analysis — Buyer vs. owner advantages and vulnerabilities
10. 3-Phase Negotiation Plan — Phase 1: Opening (relationship, anchoring), Phase 2: Negotiation (concessions, objections), Phase 3: Closure (urgency creation, commitment)

3-Phase Negotiation Plan Structure

Phase 1 — Opening
  - Establish rapport (approach varies by owner type)
  - Present opening offer with objective justification
  - Anchor below target to preserve concession room

Phase 2 — Negotiation
  - Execute diminishing concession plan
  - Respond to objections using negotiation-expert skill
  - Monitor for closing signals

Phase 3 — Closure
  - Apply urgency framing appropriate to owner type
  - Make final small concession to signal limit
  - Secure commitment and move to documentation

Integration with Settlement Calculator

For complete negotiation preparation, run both tools in sequence:

# Step 1: Quantify the deal (Settlement Calculator)
python negotiation_settlement_calculator.py input.json --output results.json

# Step 2: Build the field plan (Strategy Planner)
python negotiation_strategy_planner.py owner_profile.json results.json \
  --output $CLAUDE_PROJECT_DIR/Reports/negotiation_plan.md

The Strategy Planner accepts Settlement Calculator JSON output as its property_data argument, using the computed BATNA, ZOPA, and optimal settlement range to populate sections 3-5 of the strategy report automatically.

---

Integration with Negotiation-Expert Skill

This calculator supports the broader negotiation-expert skill by providing:

1. Quantitative Foundation - BATNA/ZOPA gives you the numbers
2. Risk Assessment - Holdout risk informs tactical approach
3. Strategic Framework - Optimal range guides opening/target/walkaway

Workflow:

1. Use calculator to establish BATNA, ZOPA, optimal range
2. Consult negotiation-expert for tactical communication strategy
3. Apply evidence-based persuasion and calibrated questions
4. Execute concession strategy from calculator
5. Monitor for signals and adjust based on negotiation-expert guidance

Validation and Error Handling

Input Validation

Automatic Checks:

• Buyer max and seller min are positive numbers
• Hearing probabilities sum to 1.0 (±0.01 tolerance)
• Award amounts are ordered: low ≤ mid ≤ high
• All costs are non-negative
• Owner profile enums match allowed values
• Confidence level between 0 and 1

Error Messages:

• Clear, actionable error descriptions
• Field-level validation with specific issues
• Suggestions for fixes

Edge Cases Handled

• No ZOPA: Returns gap amount and hearing recommendation
• Zero costs: Handles gracefully (assumes free hearing - unrealistic but valid)
• Extreme probabilities: Warns if award distribution is unusual
• Missing optional fields: Uses sensible defaults

Technical Specifications

Performance

• Calculation Time: < 50ms for typical inputs
• Memory Usage: < 5MB
• Scalability: Handles 100+ scenarios efficiently

Dependencies

Python Standard Library:

• json - Input/output
• argparse - CLI parsing
• logging - Structured logging
• pathlib - File paths
• datetime - Timestamps

Shared Utilities:

• Shared_Utils/negotiation_utils.py
• Shared_Utils/risk_utils.py
• Shared_Utils/report_utils.py

No External Dependencies - Pure Python implementation

Logging

Log Levels:

• INFO: High-level progress (default)
• DEBUG: Detailed calculations (--verbose flag)
• WARNING: Validation issues, edge cases
• ERROR: Calculation failures

Log Output:

2025-11-17 14:30:22 - INFO - Validating input data
2025-11-17 14:30:22 - INFO - Input validation passed
2025-11-17 14:30:22 - INFO - Step 1: Calculating BATNA
2025-11-17 14:30:22 - INFO - BATNA calculation complete - Net BATNA: $270,000.00
2025-11-17 14:30:22 - INFO - Step 2: Calculating ZOPA
2025-11-17 14:30:22 - INFO - ZOPA exists - Range: $150,000.00 to $200,000.00
...

Version History

Version 1.0.0 (2025-11-17)

Initial Release:

• BATNA/ZOPA calculation engine
• Probability-weighted scenario analysis
• Optimal settlement range recommendations
• Holdout risk assessment (0-30 scale)
• Concession strategy generation
• Modular architecture (validators, calculations, analysis, formatters)
• JSON schema validation
• Markdown report generation
• Sample farmer easement scenario
• Complete integration with shared utilities

Features:

• ✅ Modular architecture (4 modules)
• ✅ Input validation with JSON schema
• ✅ Comprehensive error handling
• ✅ Structured logging
• ✅ Markdown report output
• ✅ CLI and Python API
• ✅ Sample inputs
• ✅ Zero external dependencies

Future Enhancements (Roadmap)

Version 1.1 (Planned)

Features:

• Monte Carlo simulation for uncertainty quantification
• Sensitivity analysis (vary probabilities and costs)
• Multi-party negotiations (3+ parties)
• Historical settlement database integration

Version 1.2 (Planned)

Features:

• Machine learning-based holdout risk prediction
• Negotiation outcome tracking and learning
• Automated comparable deal analysis
• Real-time negotiation dashboard

Related Skills and Tools

Skills:

• negotiation-expert - Tactical negotiation communication
• objection-handling-expert - Handling owner objections
• agricultural-easement-negotiation-frameworks - Farm-specific strategies
• expropriation-compensation-entitlement-analysis - Hearing compensation

Calculators:

• easement_calculator.py - Easement valuation (informs buyer_max)
• expropriation_calculator.py - Statutory compensation (informs BATNA awards)
• cropland_out_of_production_calculator.py - Annual agricultural compensation

Slash Commands:

• /Expropriation:expropriation-compensation - Calculate hearing awards
• /Valuation:easement-valuation - Value easement (buyer_max input)

References and Methodology

Academic Sources

Fisher & Ury (1981) - Getting to Yes

• BATNA concept development
• Principled negotiation framework
• Separating people from problem

Raiffa (1982) - The Art and Science of Negotiation

• ZOPA mathematical foundations
• Decision analysis in negotiation
• Multi-attribute utility theory

Thompson (2015) - The Mind and Heart of the Negotiator

• Reservation price determination
• Anchoring effects
• Concession patterns

Industry Practice

Ontario Expropriation Association

• Typical legal/expert fee ranges
• Hearing duration and cost data
• Settlement vs. hearing statistics

Alberta Surface Rights Board

• Agricultural easement valuations
• Annual compensation models
• Landowner negotiation patterns

Hydro One / Ontario Power Generation

• Transmission corridor acquisitions
• Farmer negotiation challenges
• Holdout risk assessment frameworks

Calculation Methodology

BATNA Calculation:

• Probability-weighted expected value
• Full cost inclusion (legal, expert, time)
• Risk adjustment (standard deviation)

Holdout Risk Scoring:

• 30-point scale (empirically derived)
• Three factor domains (motivation, sophistication, alternatives)
• Probability mapping based on historical settlement rates

Optimal Range:

• Confidence-based adjustment from ZOPA midpoint
• Opening offer set to create negotiation room
• Walkaway at BATNA or buyer max (whichever lower)

Support and Troubleshooting

Common Issues

Issue: Probabilities don't sum to 1.0

Error: hearing_probabilities must sum to 1.0, got: 0.95
Fix: Adjust probabilities to sum exactly to 1.0

Issue: No ZOPA exists

Result: No ZOPA - Gap: $50,000
Recommendation: Proceed to hearing or walk away
Options:
  1. Increase buyer_max (if budget allows)
  2. Negotiate seller_min down (difficult)
  3. Improve BATNA (reduce hearing costs)
  4. Create value (non-price terms)

Issue: High holdout risk

Result: Holdout risk CRITICAL (25/30, 70% probability)
Mitigation:
  - Use mitigation strategies from report
  - Consider early engagement
  - Offer premium for settlement
  - Be prepared for expropriation

Debug Mode

# Enable verbose logging
python negotiation_settlement_calculator.py input.json --verbose

# Output shows:
# - Input validation details
# - Step-by-step calculations
# - Intermediate results
# - Decision logic

Getting Help

Within Claude Code:

• "Explain this BATNA calculation"
• "Why is holdout risk HIGH?"
• "Walk me through the optimal settlement logic"

File Issues:

• Check JSON schema validation
• Review sample inputs for format
• Verify shared utilities are accessible

---

Skill Name: negotiation-expert-infrastructure Version: 1.0.0 Created: 2025-11-17 Author: Claude Code Status: Production Ready License: Proprietary (Lease Analysis Toolkit)