Canner
Official@canner · Global
Reimagining Business Intelligence through Generative BI (GenBI)
Agent Skills by Canner
Showing 17 vetted skills indexed across 3 GitHub repositories.
Cannerenrich-context
Augment Wren projects with business context extracted from raw documents and database probes.
Cannerusage
Guides AI agents through Wren CLI workflows for querying databases via the MDL semantic layer.
Cannerdlt-connector
Load SaaS API data into DuckDB with dlt and generate a queryable Wren semantic project.
Cannergenbi
Builds and deploys shareable GenBI dashboard apps from a Wren context layer to Vercel or Cloudflare.
Canneronboarding
Guides agents through Wren Engine installation, project scaffolding, connection setup, and first query.
Cannerwren
Generates governed SQL and deployable dashboards through a semantic layer over 22+ databases.
Cannerwren-dlt-connector
Connect SaaS APIs to Wren Engine via dlt pipelines and generate queryable semantic projects.
Cannerwren-onboarding
Guides end-to-end Wren Engine setup from environment checks to first query.
Cannersetup-wrenai-mcp
Configure WrenAI MCP servers by writing .mcp.json and validating connections.
Cannerwrenai-analyst
Convert natural language data questions into SQL, charts, and insights via WrenAI MCP.
Cannerwren-connection-info
Explain required connection fields and credential handling for Wren Engine data sources.
Cannerwren-quickstart
Guide Wren Engine setup from workspace creation to MCP verification.
Cannerwren-mcp-setup
Deploy and configure a containerized Wren Engine MCP server for AI agents.
Cannerwren-project
Convert Wren MDL definitions between YAML projects and MDL JSON.
Cannerwren-usage
Automate Wren Engine workflows for SQL, MDL, and MCP server tasks.
Cannergenerate-mdl
Generate a Wren MDL manifest by introspecting database schemas via ibis-server metadata endpoints.
Cannerwren-sql
Translate user requests into MDL-aware SQL for Wren semantic models.
Frequently Asked Questions About Canner
FAQPage SchemaWhat specific data tasks does Canner enable?▼
Canner enables the conversion of natural language questions into structured SQL queries and visual insights. It facilitates the creation of semantic models from database schemas, manages credential handling for data sources, and supports the deployment of MCP servers to bridge semantic layers with external query interfaces.
Which technical personas benefit from Canner?▼
Data engineers and analytics developers benefit from Canner by streamlining the mapping of raw database schemas to semantic models. It is designed for professionals managing complex data environments who require a standardized way to translate business-level questions into accurate, model-aware SQL queries for enterprise reporting.
What are the prerequisites for deploying Canner?▼
Deployment requires an existing database environment and the ability to configure containerized MCP servers. Users must define MDL manifests, either manually or via schema introspection, and ensure proper credential handling for the target data sources to establish secure connectivity within the engine.