Repeatability
High
The transformations are structurally identical every run: normalize timestamps, canonicalize device_type strings, handle missing referrers, and emit a report. No unique judgment is needed per row.
Ambiguity Tolerance
High
Success criteria are concrete and enumerable — unified timestamp format, consistent device_type casing, documented missing-value handling, and a single output CSV plus report. An agent can verify completion without human input.
Data & Tool Availability
High
The input is a local CSV file; the agent needs only file access and a Python/pandas environment, both of which are standard. No external APIs, credentials, or live systems are required.
Error Cost
Low
The original CSV is preserved, so any transformation error is fully reversible. Downstream analysis may be delayed but no data is destroyed and no irreversible action is taken.
Human Judgment Required
Low
All decisions — how to handle missing referrers (flag vs. impute), which casing to canonicalize — are either specified or have obvious defaults. No taste, ethics, or relationship context is involved.