Repeatability
High
The transformation logic is fully deterministic: read JSON files, pivot by hotel and date, compute week-over-week percentage change. No judgment varies between runs, making this trivially repeatable.
Ambiguity Tolerance
High
The input schema is explicitly defined (hotel_id, room_type, nightly_rate, occupancy_rate, date_scraped) and the output format is clearly described. The only minor ambiguity is how to handle multiple room types per hotel per date, but that's a one-question clarification, not a blocker.
Data & Tool Availability
High
The user has all 180 files ready and can share them directly. A code agent needs only Python (pandas) or similar — no external APIs, credentials, or live data access required.
Error Cost
Low
The output is a CSV feeding a dashboard, not a financial transaction or published document. Errors are immediately visible on inspection and trivially correctable by re-running the script. No irreversible consequences.
Human Judgment Required
Low
There is no taste, ethics, or relationship context involved — just data wrangling and arithmetic. The only human input needed is confirming the pivot key (e.g., how to handle multiple room types) before the agent runs.