What problem does it solve? Data engineering agents often start sessions in environments where uv, Python virtual environments, or dltHub are missing or misconfigured, causing failed commands and blocked pipelines. This Skill verifies the workspace state at session start and sets up a working dltHub environment in place, then routes user intent to the correct data engineering toolkit. ## Core Features & Use Cases - Environment verification and setup: Checks uv availability and runs uvx dlthub-init@latest (or the dlt[hub] fallback) to scaffold a workspace in place, then confirms with dlthub ai status. - Intent-to-toolkit routing: Maps user goals (REST API ingestion, SQL databases, files, transformations, data quality, deployment) to the correct toolkit install command and entry skill. - Safe secrets handling: Enforces rules that prevent reading secrets.toml files or printing credentials, directing all secret operations through the dlt-workspace-mcp server. - Use Case: A user asks an agent to build a pipeline from the Stripe API. The agent verifies the workspace, installs the rest-api-pipeline toolkit, and hands off to the find-source entry skill without manual setup. ## Quick Start Ask the agent to set up a dltHub workspace in this project and start building a pipeline from a REST API source.
