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snowflake-semanticview

github/awesome-copilot

Create, alter, and validate Snowflake semantic views using Snowflake CLI with built-in validation workflow.

What is snowflake-semanticview?

This skill helps you build and troubleshoot Snowflake semantic views—the semantic layer definitions that enable business-friendly querying. Use it when you need to create or modify semantic views, validate DDL syntax against Snowflake, or set up Snowflake CLI connections.

  • Draft semantic view DDL following Snowflake's official syntax with dimensions, facts, and metrics
  • Discover and populate synonyms and comments from existing Snowflake table/column metadata
  • Validate DDL against Snowflake via Snowflake CLI before applying to production
  • Execute CREATE or ALTER SEMANTIC VIEW statements with automatic connection management
  • Query and test the final semantic view to confirm correctness
  • Guide Snowflake CLI installation and connection configuration

How to install snowflake-semanticview

npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview
Prerequisites
  • Snowflake CLI installed (verify with `snow --help`)
  • Snowflake connection configured via `snow connection add`
  • Access to target Snowflake database, schema, and warehouse
  • Existing fact and dimension tables in a star-schema structure
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How to use snowflake-semanticview

  1. 1.Verify Snowflake CLI is installed; if not, follow the official installation guide
  2. 2.Configure a Snowflake connection using `snow connection add`
  3. 3.Confirm the target database, schema, role, warehouse, and semantic view name
  4. 4.Provide or confirm the star-schema structure (facts with conformed dimensions)
  5. 5.Let the skill draft semantic view DDL and populate synonyms/comments from Snowflake metadata
  6. 6.Review and approve the DDL before validation
  7. 7.The skill validates the DDL against Snowflake using `snow sql` with a temporary name
  8. 8.Iterate on any validation errors until the DDL succeeds

Use cases

Good for
  • Build a semantic view on top of a star-schema fact and dimension tables for self-service BI
  • Validate semantic view DDL syntax before deploying to a Snowflake warehouse
  • Troubleshoot semantic view creation errors by iterating DDL and re-validating
  • Extract and enrich dimension/fact/metric comments and synonyms from existing Snowflake metadata
  • Query a completed semantic view to verify dimensions and metrics are accessible
Who it's for
  • Data engineers building semantic layers in Snowflake
  • Analytics engineers creating business-friendly data models
  • BI developers setting up self-service analytics with semantic views
  • Teams managing Snowflake CLI connections and validation workflows

snowflake-semanticview FAQ

What if Snowflake CLI is not installed?

Open a terminal and follow the installation guide at https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation. The skill will verify installation before proceeding.

Do I need to provide synonyms and comments manually?

No. The skill first reads existing Snowflake table/column comments as the preferred source. If missing, it will ask whether you want to provide text or approve drafted suggestions before creating them.

Why does the skill use a temporary semantic view name during validation?

Using a temporary name (e.g., appending `__tmp_validate`) prevents accidentally overwriting your production semantic view if validation fails. Once validation succeeds, the final DDL is applied with the real name.

How do I query a semantic view after it's created?

Use the SEMANTIC_VIEW() syntax with DIMENSIONS and METRICS clauses, for example: `SELECT * FROM SEMANTIC_VIEW(my_semview_name DIMENSIONS customer.customer_market_segment METRICS orders.order_average_value)`.

What happens if validation fails?

The skill will show the error, help you iterate on the DDL, and re-run validation via Snowflake CLI until it succeeds before applying the final version.

Full instructions (SKILL.md)

Source of truth, from github/awesome-copilot.


name: snowflake-semanticview description: Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic layer definitions with CREATE/ALTER SEMANTIC VIEW, to validate semantic-view DDL against Snowflake via CLI, or to guide Snowflake CLI installation and connection setup.

Snowflake Semantic Views

One-Time Setup

Workflow For Each Semantic View Request

  1. Confirm the target database, schema, role, warehouse, and final semantic view name.
  2. Confirm the model follows a star schema (facts with conformed dimensions).
  3. Draft the semantic view DDL using the official syntax:
  4. Populate synonyms and comments for each dimension, fact, and metric:
    • Read Snowflake table/view/column comments first (preferred source):
    • 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.
  5. 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.
  6. Create a temporary validation name (for example, append __tmp_validate) while keeping the same database and schema.
  7. 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.
  8. If validation fails, iterate on the DDL and re-run the validation step until it succeeds.
  9. Apply the final DDL (create or alter) using the real semantic view name.
  10. 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;
  1. 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:
  • 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.

Validation Pattern (Required)

  • Never skip validation. Always execute the DDL against Snowflake with Snowflake CLI before presenting it as final.
  • Prefer a temporary name for validation to avoid clobbering the real view.

Example CLI Validation (Template)

# Replace placeholders with real values.
snow sql -q "<CREATE OR ALTER SEMANTIC VIEW ...>" --connection <connection_name>

If the CLI uses a different connection flag in your version, run:

snow sql --help

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.