tower

Create a dlt REST API pipeline to run as a Tower app. Use for the rest_api core source, or any generic REST/HTTP API source. Not for sql_database or filesystem sources.

Explore and profile loaded data from a data consumer perspective. Scores 5 dimensions (completeness, freshness, consistency, queryability, discoverability). Absorbs validate-data. Modes — PROFILE (automated after load), VALIDATE (cross-reference against source), EXPLORE (interactive queries). Routes issues to responsible persona.

Debug and inspect a Tower app after running it. Supports dlt pipelines, ASGI apps, and plain Python scripts. Use after a run (success or failure) to inspect logs, diagnose errors, and fix issues.

Find a dlt destination for a given storage provider. Use when the user asks about a destination, wants to find a connector, or asks to implement a pipeline for a specific data destination.

Find a dlt source for a given API or data provider. Use when the user asks about a source, wants to find a connector, or asks to implement a pipeline for a specific data source.

Detect existing project stack, learn conventions from code, and produce a project profile. Run before other skills to give them richer context, or let skills invoke it automatically when no profile exists.

Create a Tower app — either a hello-world scaffold (greenfield) or a wrapper around existing Python pipeline code.

Review data app requirements from a business analyst perspective. Auto-detects intent — SCOPE CHECK (30s) for high-intent users, DISCOVERY (5min, 6 scored dimensions) for low-intent. Gates entry into pipeline development. Use before find-source or when adding endpoints.

Query and analyze data in a Tower-managed Apache Iceberg lakehouse using the Tower CLI's built-in read-only SQL query command (`tower catalogs query`). Use this skill whenever the user asks a question about their company or business data (revenue, customers, orders, metrics, KPIs, trends), asks to explore, query, or analyze tables, mentions their lakehouse, Iceberg catalog, or Tower data — even if they don't mention SQL, DuckDB, or Tower explicitly. Also use it when asked "what data do we have?" or to build a report or summary from company data.

Author, test, deploy, and schedule data ingestion pipelines on Tower that land external data (APIs, databases, SaaS tools, files) into a Tower-managed Apache Iceberg lakehouse. Use this skill whenever the user wants to ingest, load, sync, extract, or import data into their lakehouse or Tower, mentions dlt/dltHub, connectors, ELT/ETL, or says things like "get our Stripe/Postgres/GitHub/Shopify data into Tower", "build a pipeline", or "keep this data up to date" — even if they don't say "ingestion" explicitly. Covers Tower apps, Towerfiles, parameters, secrets, environments, catalogs, tables, schedules, retries, and multi-app orchestration.

Use Tower to build, run, and deploy Python data apps, pipelines, and AI agents. Covers MCP tools, Towerfile setup, local development, cloud deployment, scheduling, and secrets management.