snowflake-semanticview

github/awesome-copilot · updated Apr 8, 2026

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

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

  • 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
skill.md

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.

Discussion

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general reviews

Ratings

4.636 reviews
  • Arjun Mensah· Dec 20, 2024

    I recommend snowflake-semanticview for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ganesh Mohane· Dec 12, 2024

    Solid pick for teams standardizing on skills: snowflake-semanticview is focused, and the summary matches what you get after install.

  • Anaya Sharma· Dec 8, 2024

    Solid pick for teams standardizing on skills: snowflake-semanticview is focused, and the summary matches what you get after install.

  • Hana Li· Dec 4, 2024

    Registry listing for snowflake-semanticview matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ama Menon· Nov 27, 2024

    We added snowflake-semanticview from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Hana Verma· Nov 23, 2024

    Useful defaults in snowflake-semanticview — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Nov 11, 2024

    snowflake-semanticview is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hana Smith· Nov 11, 2024

    snowflake-semanticview reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sakshi Patil· Nov 3, 2024

    We added snowflake-semanticview from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chaitanya Patil· Oct 22, 2024

    snowflake-semanticview fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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