Agent Skills

wren

databasescanner1.3K installs

Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate

Install

npx skills add https://github.com/canner/wrenai --skill wren
SKILL.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 wrenai version (no skill cache, no version drift).

Install: pip install wrenai.

Workflow guides

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

wren docs connection-info <ds>          # required + optional connection fields for a data source

Prompt enhancement (wraps a user question for an agent)

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)

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.

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