Repeatability
High
The task is structurally identical every time: read a sheet, apply fuzzy matching and domain heuristics, normalize strings, output two CSVs. This is a pipeline, not a judgment call, and it can be re-run on any similar dataset with minimal reconfiguration.
Ambiguity Tolerance
Medium
Title case normalization and domain-based deduplication have crisp rules, but the fuzzy match threshold for 'likely duplicate' is inherently a judgment call. The flagged-duplicates CSV offloads the hard cases to a human, which is the right design — but the agent still needs a defensible threshold to avoid over- or under-flagging.
Data & Tool Availability
High
The Google Sheet is the only required input, and it's fully accessible. Standard Python libraries (pandas, fuzzywuzzy or rapidfuzz, tldextract) cover all the technical requirements. No external APIs, credentials, or live data sources are needed.
Error Cost
Low
The output is a CSV file, not a live database write — nothing is irreversible. The flagged-duplicates review step adds a human checkpoint before any changes are committed, keeping the blast radius of a bad fuzzy match very small.
Human Judgment Required
Low
The task is almost entirely mechanical: string normalization, similarity scoring, and domain parsing. The design correctly reserves genuinely ambiguous cases for human review, so the agent only needs to execute rules, not exercise taste or business context.