wren
Wren CLI provides a semantic SQL layer connecting 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, etc.) with AI-powered natural language querying. Use it when users ask data questions (metrics, trends, comparisons), need to install Wren Engine, connect databases or SaaS sources like HubSpot/Stripe via dlt, generate MDL projects from schemas, or enrich projects with business context like enums, units, and calculated cubes. Actual workflow guides live in the CLI itself, ensuring version alignment.
git clone --depth 1 https://github.com/Canner/WrenAI /tmp/wren && cp -r /tmp/wren/skills/wren ~/.claude/skills/wrenSKILL.md
# Wren CLI This is a discovery stub. The actual workflow guides and prompt helpers live inside the `wren` CLI itself, so they always match the installed wren-engine version (no skill cache, no version drift). Install: `pip install wrenai`. ## Workflow guides ```bash wren skills list # all available workflow guides wren skills get onboarding # set up Wren end-to-end wren skills get usage # day-to-day querying wren skills get generate-mdl # generate MDL from a database schema wren skills get dlt-connector # connect SaaS sources via dlt wren skills get enrich-context # add business context (units, enums, cubes) wren skills get genbi # build & deploy a shareable GenBI web app # add --full to include the skill's reference docs # add --script <name> to fetch a bundled script (e.g. dlt-connector / introspect_dlt) ``` ## Reference docs Full reference docs live on the web: <https://github.com/Canner/WrenAI/tree/main/docs/core> ```bash wren docs connection-info <ds> # required + optional connection fields for a data source ``` ## Prompt enhancement (wraps a user question for an agent) ```bash wren ask "<question>" --guided # for weaker LLMs (strict task flow) wren ask "<question>" --direct # for stronger LLMs (minimal wrapping) ``` ## Day-to-day data commands (not a sub-app — top-level) ```bash wren --sql '...' # execute SQL through the MDL layer wren query --sql '...' # same, explicit wren dry-plan --sql '...' # transpile only, no DB hit wren context show / build / validate # project / MDL lifecycle wren profile add / list / switch # named connection profiles wren memory index / recall / store # semantic memory (needs `[memory]` extra) ``` Run `wren --help` for the full surface; load the matching `wren skills get <name>` guide before driving any multi-step workflow.
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.
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 right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
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.
Turn a Wren project's context layer into a shareable, browser-side GenBI web app and deploy it to the user's Vercel or Cloudflare account. Orchestrates the full flow: `wren genbi build` returns a project-hydrated build instruction, the agent authors the app from scratch into apps/<name>/, then register → verify → deploy produce a shareable URL. Use this skill whenever the user wants to: build a dashboard from their Wren project, make a shareable analytics app, deploy their context layer as a web app, host a GenBI app on Vercel or Cloudflare Pages, or asks for a 'genbi app'.