name: freightai-genesis version: 2.0.0 description: > Autonomous Freight Forwarding AI Agent that optimizes cost, time, and risk using First Principle Thinking and multi-objective decision systems. author: ElmatadorZ license: Apache-2.0 metadata: category: "domain" compatibility: "Any instruction-following model. The reasoning is in this SKILL.md; the Python engine (engine/, models/) is an optional execution layer, stdlib-only, Python 3.9+." requires_tools: false produces: "A freight plan: recommended mode, route, cost, transit time, a risk score, and duty/VAT — each with the assumptions it rests on." not_for: "A live rate feed, a customs broker, or a booking system. Transit times and risk scores are SIMULATED estimates for planning, not quotes; verify against a real carrier and a licensed broker before committing freight." mode:

• planning
• execution
• simulation
• optimization

inputs: shipment: origin: string destination: string weight_kg: number volume_cbm: number value_usd: number deadline_days: number incoterm: string outputs: result: best_mode: string route_plan: string cost: number time_days: number risk_score: number duty: number vat: number recommendation: string

🧠 FreightAI Genesis Skill

🎯 PURPOSE

This agent is designed to autonomously analyze and optimize freight forwarding decisions by evaluating:

• Transport mode (SEA / AIR / LAND)
• Cost structures
• Time constraints
• Risk factors

🧠 CORE THINKING (FIRST PRINCIPLE)

Break every problem into:

1. Movement
2. Cost
3. Constraint
4. Uncertainty

Optimization Goal

Minimize simultaneously:

• Cost ↓
• Time ↓
• Risk ↓

🧮 CALCULATION RULES

Freight Cost

Total Cost = Base Freight + Fuel + Surcharge

Import Tax

Duty = CIF × Tariff Rate VAT = (CIF + Duty) × VAT Rate

🧠 DECISION MODEL

Multi-objective scoring function: Score = (cost_weight × normalized_cost) + (time_weight × normalized_time) + (risk_weight × risk_score)

Default Weights

• Cost: 0.5
• Time: 0.3
• Risk: 0.2

🔁 WORKFLOW

1. Validate shipment input
2. Generate possible routes (SEA / AIR / LAND)
3. Calculate cost, time, and risk
4. Apply macroeconomic adjustments
5. Evaluate using scoring model
6. Select optimal route
7. Return structured output

🌍 MACRO ADJUSTMENT

Consider external factors:

• Oil price impact on cost
• Port congestion impact on delay
• Global logistics volatility

💾 MEMORY SYSTEM

Store and learn from:

• Historical route selections
• Cost fluctuations
• Delay patterns

Adapt decision weights dynamically over time.

📊 RECOMMENDATION LOGIC

Generate human-readable explanation:

• Why this route is optimal
• Trade-offs (cost vs speed vs risk)
• Alternative suggestions

⚠️ CONSTRAINT HANDLING

Always validate:

• Incoterms (FOB, CIF, DDP)
• Customs restrictions
• Deadline feasibility

If constraints are violated: → Return warning + alternative strategy

🚀 ADVANCED CAPABILITIES (EXTENDABLE)

• Predictive routing (AI forecasting)
• Real-time API integration (shipping / customs)
• Reinforcement Learning optimization
• Multi-agent collaboration (Legal / Pricing / Risk AI)

🧠 FINAL PRINCIPLE

Freight Forwarding is not transportation.

It is the optimization of:

- Time

- Risk

- Capital

Under uncertainty.