Use when the user asks to describe, profile, summarize, explore, or query tabular data without producing modified output files — e.g., "what's in this CSV", "show distributions", "what columns correlate", or ad-hoc SQL questions. Prefer data-wrangler when the user wants to clean, reshape, dedupe, join, or convert data.
Use when the user asks to clean, transform, reshape, dedupe, join, concatenate, sort, replace, or convert tabular data and produce new output files — e.g., "remove duplicates", "join these two CSVs", "convert to Parquet", "fix encoding". Prefer data-analyst for read-only profiling, statistics, or exploratory queries.
Use when the user asks to analyze policy questions that combine local tabular data with US government sources — jurisdiction comparisons, fiscal-impact analysis, demographic/employment/crime context, or "is policy X working?" questions referencing Census, BLS, FBI Crime Data, or Wikidata. Prefer data-analyst for plain…
Security Maintainer operating under PySentinel Zero-Trust Model. Enforces OWASP principles, CVE prevention, supply chain integrity, XXE/XSD validation, and secure development practices with advanced production tollgates.