Repeatability
High
The structure is identical across all 320 rows: parse fields, detect missing or malformed dates, standardize format, compute a quality score. This is a deterministic pipeline that runs the same way every time.
Ambiguity Tolerance
Medium
The output format (CSV with a 1–5 score) is clear, but the scoring rubric for 'data_quality_score' needs to be defined explicitly — the agent must be told what combination of missing fields maps to each score level, or it will make reasonable but potentially wrong assumptions.
Data & Tool Availability
High
The user provides the Excel file directly; no external APIs, logins, or live data sources are needed. A code agent with pandas or similar can handle all parsing, cleaning, and output generation locally.
Error Cost
Low
The output is a clean CSV for human review before any follow-up action is taken — no irreversible decisions are made. A misclassified date or wrong score is easily caught and corrected in a spot-check.
Human Judgment Required
Low
Date standardization and completeness flagging are rule-based. The scoring logic can be fully specified upfront, leaving no meaningful judgment calls that require domain intuition or relationship context.