Usage
Wren Engine CLI workflow guide for AI agents.
Install
npx skills add Canner/WrenAI --skill usage -g -a claude-code -y- Repository
- Canner/WrenAI18k
- Category
- Data and ML
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
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.
Wren Engine CLI, Agent Workflow Guide
This guide is served by the
wrenCLI (wren skills get usage), so it always matches your installed wrenai version. Pull the deeper reference docs withwren skills get usage --full.
Preflight, Verify environment and installation
Goal: Ensure the wren CLI is available before entering any workflow.
Step 1, Check Python virtual environment
Run python -c "import sys; print(sys.prefix)" (or equivalent) to determine
whether a virtual environment is active.
- If no venv is active, warn the user and ask whether to:
- Create one (e.g.,
python -m venv .venv && source .venv/bin/activate) - Continue without a venv (not recommended, may pollute global packages)
- Create one (e.g.,
Step 2, Check if the wren CLI is installed
Run wren --version. If the command is not found or errors:
-
Tell the user that the
wrenCLI is not installed. -
Ask if you should help install it.
-
If the user agrees, determine the datasource extra to install:
Auto-detect from project: Check whether the current directory is inside a wren project (look for
wren_project.ymlup to the repository root). If found, read the active profile withcat ~/.wren/profiles.ymlor look for a datasource hint in the project's profile configuration. Extract the datasource type from there.Ask the user: If no project is detected or no datasource can be inferred, ask the user which database they plan to connect to. Valid extras:
postgres(for Aurora Postgres),mysql(for Aurora MySQL),bigquery,snowflake,clickhouse,trino,mssql,databricks,redshift,spark,athena,oracle. DuckDB is included by default, no extra needed. -
Install with the detected or chosen extra:
# DuckDB (no extra needed) pip install "wrenai"
