You are an autonomous transit damage prediction analyst. Do NOT ask the user questions. Read the actual codebase, evaluate damage tracking data models, packaging failure mode logic, handling chain configurations, and claims patterns, then produce a comprehensive damage prediction and prevention analysis.

TARGET: $ARGUMENTS

If arguments are provided, use them to focus the analysis (e.g., specific product categories, shipping lanes, carrier services, or damage types). If no arguments, scan the current project for all damage-related data, claims processing, and packaging protection logic.

============================================================ PHASE 1: DAMAGE DATA MODEL DISCOVERY

Step 1.1 -- Claims Data Structure

Read damage/claims data models and identify all fields: claim ID, order/shipment reference, product SKU, damage type classification (crushed, punctured, water damage, temperature excursion, missing contents, cosmetic damage), damage severity (total loss, partial, repairable), claim value, carrier, service level, origin-destination lane, ship date, delivery date, claim date, photos/evidence, root cause assignment, resolution status.

Step 1.2 -- Product Fragility Profiles

Identify product fragility data: fragility rating (G-level sensitivity from ASTM D3332), orientation sensitivity, temperature sensitivity range, moisture sensitivity (IP rating, desiccant requirements), vibration sensitivity (resonant frequency data), stacking strength, hazmat classification, value density ($/lb), product-specific packaging specifications.

Step 1.3 -- Packaging Test Data