canner/wrenai

wren

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…

All-time #8795 Trending #5100 Hot #1033 First seen Jun 4, 2026
8-week activity · all time api

Installation

$ npx skills add canner/wrenai --skill wren

Summary

  • 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 an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare.
  • Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

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Also in this package

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 17.5K
License LICENSE
Default branch main
Open issues 247
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

LicenseApache-2.0
Allowed toolsBash(wren:*)

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,293 B
  • docs SUMMARY.md 1,223 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,200 installs

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.