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snowflake-semanticview

github/awesome-copilot github/awesome-copilot

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.

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16
Updated time August 23, 2026

About snowflake-semanticview

This skill guides the assistant through creating, altering, and validating Snowflake semantic views using the official Snowflake CLI (snow). It solves the problem of building reliable semantic-layer definitions by enforcing a disciplined, validation-first workflow: rather than producing untested DDL, the assistant drafts CREATE/ALTER SEMANTIC VIEW statements, validates them against Snowflake via the CLI using a temporary name, iterates until they pass, and only then applies the final definition. It also provides one-time setup guidance for installing the Snowflake CLI and configuring a connection via snow connection add, pointing to official Snowflake documentation.

The workflow is thorough. It has the assistant confirm the target database, schema, role, warehouse, and view name, verify a star-schema model, and draft DDL from the official syntax. It requires populating synonyms and comments for every dimension, fact, and metric, preferring existing Snowflake column comments as the source and asking permission before inventing any. It uses read-only SELECT statements with DISTINCT and a LIMIT of at most 1000 rows to discover relationships and column types, validates DDL under a temporary name (for example a __tmp_validate suffix) to avoid clobbering the real view, runs a sample SEMANTIC_VIEW query to confirm behavior, and cleans up temporary objects afterward.

The target users are data engineers and analytics engineers building Snowflake semantic layers or metric definitions. Credential handling is delegated to the official Snowflake CLI connection configuration rather than being managed by the skill, and the emphasis on temporary validation names, LIMIT caps, and asking before creating comments reflects careful, low-risk data practices.

FAQ

What prerequisites are required?

The Snowflake CLI (snow) must be installed and a connection configured via snow connection add. The skill links to official Snowflake docs for installation and connection setup and treats these as one-time steps.

How are credentials handled?

Authentication is delegated to the official Snowflake CLI connection configuration. The skill uses the configured connection for validation and execution and does not manage or store credentials itself.

How does it avoid overwriting an existing view?

It validates DDL under a temporary name, such as appending a __tmp_validate suffix in the same database and schema, then applies the final DDL only after validation succeeds and cleans up the temporary object.

Does it query my actual data?

It runs read-only SELECT statements with DISTINCT and a LIMIT of at most 1000 rows to discover fact/dimension relationships and column types for building meaningful comments and synonyms.

Will it invent synonyms or comments?

No. It prefers existing Snowflake column comments as the source and asks whether it may create them, whether you want to provide text, or whether it should draft suggestions for approval before adding any.

View on GitHub

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

  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:
    • https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view
  4. 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.
  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-viewExample:
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:
    • 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.

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.

All Files

0 files

Install snowflake-semanticview

Download and extract the skill files to your .claude/skills/ directory.

Download ZIP

Clone the repository and copy the skill files to your project.

git clone https://github.com/github/awesome-copilot/blob/main/skills/snowflake-semanticview/SKILL.md # Copy SKILL.md to your .claude/skills/ directory

Copy Copy
Quick Setup: Copy the skill folder to .claude/skills/Claude will automatically detect and use the skill

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