---

name: wren-ai description: "Use WrenAI and Wren Engine for governed semantic SQL, MDL projects, data-context memory, agent workflow guides, GenBI dashboards, MCP tools, browser WASM queries, framework SDKs, and repository development." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

WrenAI

Use this skill for WrenAI, the wrenai Python distribution, the wren CLI, wren_core, wren-langchain, wren-pydantic, or @wrenai/wren-core-wasm. It covers the semantic layer that turns SQL written against modeled objects into target-database SQL, plus the project context and agent-facing workflows around it.

Start Here

1. Install only the components required by the requested workflow:

   pip install wrenai
   # Add one datasource extra only when needed, for example:
   pip install "wrenai[postgres]"
   

2. Verify the public CLI before relying on a project:

   wren --version
   wren --help
   

3. If a task needs a Wren project, verify that wren_project.yml and target/mdl.json exist. Build the target after changing source YAML:

   wren context validate
   wren context build
   

4. Route to the owning sub-skill before giving detailed commands. Wren workflows are order-sensitive: profile/project setup precedes query execution, and model/knowledge changes precede re-indexing or deployment.

Route by Task

• Projects, profiles, MDL, connections, dbt/OSI import, or type mapping: read sub-skills/cli-projects/SKILL.md.
• SQL queries, dry plans, dry runs, cubes, connectors, policies, or the Python WrenEngine: read sub-skills/query-engine/SKILL.md.
• Business rules, knowledge/, query memory, semantic retrieval, recall, stored NL→SQL pairs, or context enrichment: read sub-skills/memory-knowledge/SKILL.md.
• wren skills, agent onboarding/generation guides, wren ask, dlt/SaaS ingestion, or authoring a CLI-served Wren guide: read sub-skills/agent-workflows/SKILL.md.
• GenBI apps, Vercel/Cloudflare deployment, wren serve mcp, MCP tools, or browser-side WebAssembly: read sub-skills/genbi-mcp-wasm/SKILL.md.
• LangChain, LangGraph, Pydantic AI, WrenToolkit, or agent-tool contracts: read sub-skills/sdk-integrations/SKILL.md.
• wren_core, Rust/PyO3/WASM maintenance, local engine builds, tests, or contributor policy: read sub-skills/engine-development/SKILL.md.

Core Operating Rules

• Treat the Wren project as portable business context: MDL source, knowledge/, and the compiled target/mdl.json serve different roles. Do not confuse the project-level catalog/schema namespace with a model's physical database table_reference.
• Profiles hold connection values and should use ${ENV_VAR} placeholders; do not put credentials in project YAML, shell-history flags, agent prompts, or static GenBI files.
• Write SQL against Wren models and views. For complex or costly work, run wren dry-plan first; use wren dry-run only when a live database validation is intended.
• Prefer wren cube query for covered aggregation questions. It constrains measure, dimension, time-grain, and filter choices instead of requiring an agent to invent GROUP BY and date logic.
• Treat knowledge/sql/ as the source of truth for accepted NL→SQL pairs. A LanceDB index is derived state and may be rebuilt; do not delete source knowledge just to repair the index.
• Keep GenBI deployment credentials server-side or in environment files. A static app is public to its viewers, so a successful deploy is not proof that it is safe or publicly reachable.
• Optional datasource, memory, MCP, UI, Rust, Node, and browser features must be named explicitly before use. Do not assume wrenai[all] or a database service is installed.

Shared References and Helper

• Read references/overview.md for the architecture and package map before crossing CLI, SDK, MCP, and WASM boundaries.
• Read references/install-and-extras.md when choosing a public install command or diagnosing optional dependencies.
• Read references/troubleshooting.md for cross-cutting installation, profile, project-discovery, dependency, and credential issues.
• Read references/repo-provenance.md before relying on this skill for a new checkout or deciding whether to refresh it.
• Run scripts/check_wren_environment.py for a safe public import/CLI check; it does not connect to a database or write a project.

Avoid

• Do not run wren query against an unreviewed production profile merely to discover schema. Start with project inspection, wren context show, memory context, or a dry plan.
• Do not pass secrets through --connection-info, GenBI deploy arguments, or static app files when an environment-backed profile is available.
• Do not treat an optional service connector, semantic-memory stack, MCP server, or WebAssembly build as verified merely because the base package imports.
• Do not use repository maintainer build commands for ordinary package-user workflows; route those tasks to engine-development.