Repeatability
High
The transformation logic is structurally identical every time: group by user_id, compute aggregates, derive cohort labels, and write CSV. This is a deterministic data pipeline with no instance-specific judgment required.
Ambiguity Tolerance
Medium
The output columns are explicitly named and most derivations are unambiguous, but 'acquisition channel' and 'feature adoption pattern' cohort definitions are underspecified — the agent will need to make reasonable assumptions or ask for clarification before coding the classification logic.
Data & Tool Availability
High
The input is a local JSON Lines file with a known schema, and the output is a CSV — no external APIs, credentials, or live systems required. A code agent with file access and a Python runtime has everything it needs.
Error Cost
Low
The output is a reviewable CSV file; any errors in aggregation logic are visible and correctable before downstream use. No irreversible actions, financial transactions, or external communications are involved.
Human Judgment Required
Low
The work is pure data engineering — grouping, counting, date arithmetic, and conditional classification. The only human input needed is defining the cohort taxonomy upfront; execution requires no taste, ethics, or relationship context.