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Onboard a user to Wren Engine end-to-end. Walks through environment checks, project scaffolding, connection configuration via .env, and first query. Use when: user wants to install Wren Engine, set up a new data source connection, or bootstrap a new project from scratch. Triggers: '/wren-onboarding', 'install wren', 'set up wren engine', 'wren onboarding', 'connect new database to wren'.

Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.

Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

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.

Reference guide for Wren Engine connection info — explains required fields for all 18 supported data sources (PostgreSQL, MySQL, BigQuery, Snowflake, ClickHouse, Trino, DuckDB, Databricks, Spark, Athena, Redshift, Oracle, SQL Server, Apache Doris, S3, GCS, MinIO, local files). Covers sensitive field handling, Docker host hints, and BigQuery credential encoding. Use when the user asks how to configure a data source connection or what fields to fill in.

Generate a Wren MDL manifest from a database using ibis-server metadata endpoints. Use when a user wants to create or set up a new Wren MDL, scaffold a manifest from an existing database, or onboard a new data source without installing any database drivers locally.

Interact with Wren Engine MCP server via plain HTTP JSON-RPC requests — no MCP client SDK required. Covers session initialization, tool discovery, and calling all 20+ Wren tools (query, deploy, metadata, health check) using standard HTTP POST with JSON-RPC 2.0 payloads. Use when the client cannot or prefers not to use the MCP protocol directly (e.g. OpenClaw, custom HTTP clients, shell scripts, or any environment without an MCP SDK).

Set up Wren Engine MCP server via Docker and register it with an AI agent. Covers pulling the Docker image, running the container with docker run, mounting a workspace, configuring connection info via the Web UI (with Docker host hint), registering the MCP server in Claude Code (or other MCP clients) using streamable-http transport, and starting a new session to interact with Wren MCP. Trigger when a user wants to run Wren MCP in Docker, configure Claude Code MCP, or connect an AI client to a Dockerized Wren Engine.

Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the

Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.

Onboard a user to Wren Engine end-to-end. Walks through environment checks, project scaffolding, connection configuration via .env, and first query. Use when: user wants to install Wren Engine, set up a new data source connection, or bootstrap a new project from scratch. Triggers: '/wren-onboarding', 'install wren', 'set up wren engine', 'wren onboarding', 'connect new database to wren'.

Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

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.

Set up WrenAI MCP server connection. Detects Node.js, configures .mcp.json with stdio proxy, and verifies the connection. Triggers on 'connect to wrenai', 'setup wrenai mcp', 'connect mcp', or 'wrenai mcp setup'.

WrenAI data analyst — answers data questions like a senior analyst. Generates SQL, runs queries, builds charts, and provides business insights. Reads schema automatically so you never need to explain your data model. Use when asked to "analyze", "query", "chart", "visualize", "what are the top...", "how many...", "show me...", or any data/business intelligence question. Proactively suggest when the user asks about their data or business metrics. Requires WrenAI MCP connection (run /setup-wrenai-mcp first).