snowflake-semanticview

Build and validate Snowflake semantic views using Snowflake CLI with guided DDL creation and testing.

github/awesome-copilot|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

What it does

Handles the complete semantic view lifecycle: drafting DDL, populating synonyms and comments from Snowflake table metadata, validating against Snowflake via CLI, and executing final CREATE or ALTER statements
Requires one-time Snowflake CLI installation and connection setup; confirms prerequisites before proceeding with validation
Validates all DDL against Snowflake using temporary v

Repository

github/awesome-copilot

Last updated

Apr 8, 2026

Installation Guide

Select your AI agent

How to use snowflake-semanticview on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add snowflake-semanticview

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview

Fetches snowflake-semanticview from github/awesome-copilot and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/snowflake-semanticview

Restart Cursor to activate snowflake-semanticview. Access via /snowflake-semanticview in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

One-Time Setup

Verify Snowflake CLI installation by opening a new terminal and running snow --help.

If Snowflake CLI is missing or the user cannot install it, direct them to https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation.

Configure a Snowflake connection with snow connection add per https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections#add-a-connection.

Use the configured connection for all validation and execution steps.

Workflow For Each Semantic View Request

Confirm the target database, schema, role, warehouse, and final semantic view name.

Confirm the model follows a star schema (facts with conformed dimensions).

Draft the semantic view DDL using the official syntax:

https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view

Populate synonyms and comments for each dimension, fact, and metric:

Read Snowflake table/view/column comments first (preferred source):
- https://docs.snowflake.com/en/sql-reference/sql/comment
If comments or synonyms are missing, ask whether you can create them, whether the user wants to provide text, or whether you should draft suggestions for approval.

Use SELECT statements with DISTINCT and LIMIT (maximum 1000 rows) to discover relationships between fact and dimension tables, identify column data types, and create more meaningful comments and synonyms for columns.

Create a temporary validation name (for example, append __tmp_validate) while keeping the same database and schema.

Always validate by sending the DDL to Snowflake via Snowflake CLI before finalizing:

Use snow sql to execute the statement with the configured connection.
If flags differ by version, check snow sql --help and use the connection option shown there.

If validation fails, iterate on the DDL and re-run the validation step until it succeeds.

Apply the final DDL (create or alter) using the real semantic view name.

Run a sample query against the final semantic view to confirm it works as expected. It has a different SQL syntax as can be seen here: https://docs.snowflake.com/en/user-guide/views-semantic/querying#querying-a-semantic-view Example:

SELECT * FROM SEMANTIC_VIEW( my_semview_name DIMENSIONS customer.customer_market_segment METRICS orders.order_average_value ) ORDER BY customer_market_segment;

Clean up any temporary semantic view created during validation.

Synonyms And Comments (Required)

Use the semantic view syntax for synonyms and comments:

WITH SYNONYMS [ = ] ( 'synonym' [ , ... ] ) COMMENT = 'comment_about_dim_fact_or_metric'

Treat synonyms as informational only; do not use them to reference dimensions, facts, or metrics elsewhere.

Use Snowflake comments as the preferred and first source for synonyms and comments:

https://docs.snowflake.com/en/sql-reference/sql/comment

If Snowflake comments are missing, ask whether you can create them, whether the user wants to provide text, or whether you should draft suggestions for approval.

Do not invent synonyms or comments without user approval.

Notes

Treat installation and connection setup as one-time steps, but confirm they are done before the first validation.

Keep the final semantic view definition identical to the validated temporary definition except for the name.

Do not omit synonyms or comments; consider them required for completeness even if optional in syntax.

snowflake-semanticview

What it does

Category

Repository

Last updated

Installation Guide

How to use snowflake-semanticview on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Snowflake Semantic Views

One-Time Setup

Workflow For Each Semantic View Request

Synonyms And Comments (Required)

Validation Pattern (Required)

Example CLI Validation (Template)

Notes

Stakeholder Communication

Implementation Guide

Best Practices

When to Use This

Learning Path

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Reviews

Discussion