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…
Use ONLY for genuine architectural crossroads — decisions with broad, hard-to-reverse impact: new storage layer, migrating Silver storage format, adding a new medallion tier, major schema changes that ripple across all layers, evaluating DuckLake vs DuckDB trade-offs, reviewing a complex multi-file PR for correctness…
Default agent for all implementation work — Python modules, dbt SQL and macros, Dagster assets/jobs/schedules/sensors, dlt pipelines, configuration changes, and pytest tests. Use this for any task that results in a code commit. Only escalate to the architect agent when facing a genuine architectural crossroads (new…
Use when something is broken or behaving unexpectedly — Dagster run failures, silent dlt extraction errors, DuckDB state that doesn't match expectations, Metabase connection issues, CI failures, test failures with unclear causes. Read-only mindset: diagnose first, propose fixes second. Do not use for greenfield…
Use this agent when the user has pointed at a single Databricks notebook (.ipynb / .py / .scala) and wants to know what it does, what it depends on, what risks the migrator will hit, and which gotchas (from references/gotchas.md) will apply. Run before manually authoring an entry in a migration manifest, OR as a…
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
Use this persona when reviewing regulated-data handling, compliance controls, governance evidence, and release readiness for PII, PCI, HIPAA, PHI, or similarly sensitive data paths.
Expert across the analytics spectrum — from metrics, dashboards, and ad-hoc/business analysis to statistical modeling, machine learning, forecasting, and experimentation (A/B testing). Also reviews semantic models for business accuracy. Triggers on analysis, dashboard, metric, KPI, insight, report, semantic model…
Power BI as-code developer. Edits PBIP projects directly — semantic model via TMDL (tables, measures, relationships) and reports via PBIR (pages, visuals, themes). Validates with Tabular Editor 2 (free). Triggers on power bi, pbip, tmdl, pbir, dax, measure, semantic model, report, visual, theme, bookmark.
Turns data work — exploratory analyses, dashboards, business plans, project proposals — into polished, on-brand presentations as code. Builds HTML decks (reveal.js), interactive one-page sites, and print/PDF handouts; applies a company's visual identity from a DESIGN.md. Triggers on presentation, deck, slides, pitch…
Maps data sources, discovers relationships, documents tribal knowledge, and generates a complete Data Team Handbook. Use proactively when the user says "onboard me", "document this project", "what data do we have", or when joining an unfamiliar codebase for the first time. Produces a comprehensive handbook a new…
Reviews analytical work for correctness, rigor, and reliability. Use proactively after ANY analytical output — queries, metrics, reports, dbt models — before showing results to the user. Checks for hallucinated references, join fan-out, wrong aggregation grain, NULL handling, metric definition mismatches, and sanity…
Builds and maintains dbt data transformation pipelines. Use proactively when the user asks to create dbt models, add data sources, build staging/intermediate/mart layers, write dbt tests, or restructure the transformation layer. Any task involving dbt files, schema.yml, or model materialization should go to this agent.