Cold Chain Risk Calculator

Assess temperature excursion risk for cold chain transport routes. Evaluates packaging type, transit duration, and route conditions to produce a structured JSON risk score and mitigation recommendations.

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

• Evaluating shipping risk for biological samples, vaccines, or temperature-sensitive pharmaceuticals
• Selecting appropriate packaging (dry ice, liquid nitrogen, gel packs) for a given route and duration
• Generating risk documentation for regulatory or QA purposes

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Fallback template: If scripts/main.py fails or required inputs are absent, report: (a) which parameter is missing, (b) what partial assessment is still possible, (c) the manual risk-scoring approach.

Parameters

Usage

python scripts/main.py --route "NYC-Boston" --duration 48 --packaging dry-ice
python scripts/main.py --route "LAX-London" --duration 120 --packaging liquid-nitrogen --output risk_report.json

Output Format

The script outputs a structured JSON object:

{
  "route": "NYC-Boston",
  "duration_hours": 48,
  "packaging": "dry-ice",
  "risk_score": 19.2,
  "risk_level": "Medium",
  "mitigation_recommendations": [
    "Add temperature logger to shipment",
    "Pre-condition dry ice 2h before packing",
    "Notify recipient of expected arrival window"
  ]
}

The mitigation_recommendations field is always present and contains at least one actionable item. Recommendations are generated based on risk level and packaging type.

Risk Model

Risk score = duration_hours × 0.5 × packaging_factor

Risk levels: Low (< 15), Medium (15–30), High (> 30)