Repeatability
High
The transformation rules are fixed and apply uniformly across all 450 records: tag normalization, date formatting, URL-based deletion, and grouping logic are all structurally identical per row. This is exactly the kind of batch data pipeline AI handles reliably.
Ambiguity Tolerance
High
Success criteria are concrete and checkable: ISO 8601 dates, deduplicated and consistently delimited tags, 12 specific posts removed, output grouped by year and category. A human can verify the output mechanically without subjective judgment.
Data & Tool Availability
High
The user has already exported the JSON and can supply the list of 12 URLs to remove. No external APIs, live systems, or credentials are needed — just the file and a script or agent with file-processing capability.
Error Cost
Low
The original JSON export is preserved, so any mistakes in the output CSV are fully reversible. The worst realistic outcome is a malformed CSV that the user catches on review before importing anywhere.
Human Judgment Required
Low
Tag normalization requires pattern matching and deduplication, not taste or editorial judgment. The only edge case is ambiguous tag synonyms (e.g., 'SEO' vs 'seo-tips'), but the user hasn't asked for semantic merging — just formatting cleanup.