Repeatability
High
The task is structurally identical every time: read rows, normalize strings, flag duplicates, export. This pattern generalizes well and could be re-run monthly with minimal changes.
Ambiguity Tolerance
Medium
The goal is clear — standardized names, flagged duplicate IDs, clean export — but edge cases like near-identical instructor names or ambiguous class variants require a judgment call that the agent may not resolve correctly without a reference list.
Data & Tool Availability
High
The data lives in Google Sheets, which is accessible via API or direct export. A code agent can read, transform, and write back or export to CSV with standard libraries and OAuth credentials.
Error Cost
Low
The original sheet is preserved, so any misclassification is reversible. Worst case is a few incorrectly merged names that skew analytics, which a human spot-check can catch before the clean file is used.
Human Judgment Required
Low
Fuzzy string matching and duplicate detection are well-solved algorithmic problems. A human should review the agent's proposed canonical name mappings before finalizing, but the heavy lifting is mechanical.