Repeatability
High
The transformation rules — field mapping, unit conversion, date normalization, deduplication — are structurally identical every run. Once the schema mappings are defined per feed, the pipeline is fully repeatable with no judgment variation.
Ambiguity Tolerance
High
Success criteria are concrete: a single schema, standard units, ISO timestamps, deduplicated records flagged by address+price, and valid GeoJSON output. A non-human can verify all of these programmatically.
Data & Tool Availability
High
The agent needs the six feed files and a target schema definition — both are in-scope and deliverable by the user. Standard libraries (pandas, geojson, dateutil) cover all transformation needs without external API dependencies.
Error Cost
Low
Output is a data file used for mapping and analysis, not a live transaction or irreversible action. Errors are detectable on review and correctable by re-running the pipeline — no downstream damage if caught before ingestion.
Human Judgment Required
Low
Unit conversion and field mapping are rule-based once the feed schemas are documented. The only soft judgment call is resolving ambiguous duplicate addresses (e.g., slight spelling variants), which can be flagged for human review rather than auto-resolved.