Repeatability
High
The three sub-tasks — parse freeform text into booleans, deduplicate by domain, flag stale rows — are structurally identical every time and follow deterministic rules. This is exactly the kind of structured transformation that agents handle reliably.
Ambiguity Tolerance
Medium
The deduplication and date-flagging rules are crisp, but parsing freeform feature text into 22 specific boolean columns requires a canonical feature list and synonym mapping that the user hasn't fully specified. Edge cases like 'single sign-on' vs 'SSO' or partial feature mentions need a defined resolution rule.
Data & Tool Availability
High
The user has the Google Sheet and can export it as CSV; no external APIs or live data fetching are required. A code agent with Python (pandas, re, or an LLM-assisted parser) has everything it needs to execute all three steps.
Error Cost
Low
The original sheet is untouched and the output is a new CSV, so mistakes are fully reversible. A human spot-check of a sample of rows is sufficient to validate correctness before replacing any source data.
Human Judgment Required
Low
No taste, ethics, or relationship context is needed. The only judgment call is defining the canonical 22-feature list and synonym mappings, which the user can supply upfront and the agent applies mechanically.