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honeydew-ai
Query, model, and govern a Honeydew semantic layer from your code editor: define entities and metrics from Snowflake, Databricks, or BigQuery sources, build relationships and calculated attributes, validate models with execution tests, and manage workspace branches and pull requests.
What's Inside
Skills12
attribute-creation
/attribute-creation
Guides you step-by-step through defining a calculated attribute (dimension) on a Honeydew entity. Covers SQL expression building and pushes to Honeydew via the MCP tools.
context-item-creation
/context-item-creation
Guides you through creating context items — instructions, skills, knowledge pointers, and memory events — that give the AI analyst persistent knowledge about your organization. Covers when to create each type, naming conventions, folder organization, and the hard boundary between context (rules and procedures) and the semantic layer (logic and calculations).
conversation-review
/conversation-review
Use when a semantic/context layer curator wants to review past analysis conversations in bulk, categorize user feedback into actionable improvement areas, and apply targeted changes to the semantic model or context layer. Covers the full workflow from collecting conversations and classifying feedback through to creating action items and applying them on a branch.
domain-creation
/domain-creation
Guides you through creating a Honeydew domain — a governance object that scopes entity/field visibility and applies mandatory filters — ideal for setting up contexts for deep analysis.
entity-creation
/entity-creation
Guides you through defining a new Honeydew entity from a data warehouse source — covering source type, granularity key, and initial attribute mapping — then pushes to Honeydew via the MCP tools.
MCP Servers2
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Version1.1.0
Released
LanguageShell
Stars35
Copy clicks1
MaintenanceExcellent
LicenseApache-2.0
Last Commit
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Caution
External network access
Connects to servers outside your machine
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