Runs before the agent uses a tool for Bash and mcp__tower-mcp__tower_run_local tool calls, executing block-secret-leaks.sh, block-direct-execution.sh and suggest-debug-mode.sh via bash (3 commands). From tower/agentic-data-engineering.
Runs after a tool call finishes for mcp__tower-mcp__tower_run_local tool calls, executing log-pipeline-run.sh via bash. From tower/agentic-data-engineering.
Runs when a tool call fails for mcp__tower-mcp__tower_run_local tool calls, executing log-pipeline-run.sh via bash. From tower/agentic-data-engineering.
Create a dlt REST API pipeline to run as a Tower app. Use for the restapi core source, or any generic REST/HTTP API source. Not for sqldatabase or filesystem sources.
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.
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.
Review data model design for schema quality, dimensional modeling, and dbt best practices. Scores 6 dimensions (key integrity, normalization, naming, type correctness, join readiness, evolution safety). Modes — PRE-LOAD (config review), POST-LOAD (materialized schema), MODEL (dbt dimensional modeling). Use when schema…
Review credential handling, data sensitivity, and access control for a Tower data app. 5 pass/fail checks + 2 scored dimensions (PII awareness, secret rotation readiness). Modes — AUDIT (before first deploy), INCIDENT (credential compromise response). Use before towerdeploy or when credential issues arise.
Safely manage secrets as runtime environment variables. Use when the user directly asks to set up, configure, or inspect credentials (API keys, database passwords, tokens). Do NOT use when in need for reading secrets, for pipeline creation, source discovery, or debugging pipeline execution — those skills call…
AGENTS.md instructions for tower/agentic-data-engineering, covering tower data apps, app types in scope, tower ecosystem rules, running apps and python environment.
AGENTS.md instructions for tower/agent-skills, covering agent skills for tower, repository layout, skill conventions, design principles for data skills and making changes.
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), and record what you learn about the data with tower catalogs knowledge. Use this skill whenever the user asks a question about their company or business data (revenue…
Build a verified ontology for a Tower-managed Apache Iceberg lakehouse — profile every table, prove the grain and join keys with SQL, and record the entity model as catalog knowledge with tower catalogs knowledge. Use this skill when a catalog has no recorded knowledge, when the user asks to document, map, model…