SaaS Data Connector
Loads SaaS data into DuckDB with dlt and builds a Wren semantic project for SQL.
Install
npx skills add Canner/WrenAI --skill dlt-connector -g -a claude-code -y- Repository
- Canner/WrenAI18k
- Category
- Data and ML
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 2,728 tokens
- Runs scripts
- Yes, review before use
What it does
Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.
wren-dlt-connector
Reference docs (
dlt_sources) and theintrospect_dltscript are bundled. Pull references withwren skills get dlt-connector --full; fetch a script withwren skills get dlt-connector --script <name>.
Connect SaaS data to Wren Engine for SQL analysis, from zero to a verified, queryable project in one conversation.
Who this is for
Data analysts who know SQL and some Python, but may not have used dlt or Wren before. Explain concepts briefly when they first appear, but don't over-explain things a SQL-literate person would already know.
Overview
This skill walks through a four-phase workflow:
- Extract, Use dlt (data load tool) to pull data from a SaaS API into a local DuckDB file
- Model, Introspect the DuckDB schema and auto-generate a Wren semantic project (YAML models, relationships, profile)
- Build & Verify, Build the project and run actual SQL queries to confirm everything works end-to-end
- Handoff, Show the user their data and next steps
The user might enter at any phase. Ask which phase they're starting from, they may already have a .duckdb file and just need phases 2, 4.
The goal is a project that actually queries successfully, not just files that look correct. Always run the verification step before declaring success.
Critical: DuckDB catalog naming
When wren engine connects to a DuckDB file, it ATTACHes it using the filename (without .duckdb extension) as the catalog alias:
ATTACH DATABASE 'stripe_data.duckdb' AS "stripe_data" (READ_ONLY)
This means every model's table_reference.catalog must equal the DuckDB filename stem. If the file is hubspot.duckdb, the catalog is hubspot. If it's my_pipeline.duckdb, the catalog is my_pipeline.
Getting this wrong causes "table not found" errors at query time. The introspect_dlt.py script handles this automatically.
