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/snowflake-semanticview

@6c059c1 official
by githubgithub/awesome-copilot40k stars
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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.

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/snowflake-semanticview

This session only. Nothing lands on disk.

SKILL.md

≈84 tokens always: the name and description. ≈1k when used: this file.

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.

Source: SKILL.md on GitHub

2 warnings17d5 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The skill facilitates the management of Snowflake semantic views using the official Snowflake CLI. It executes commands locally and processes metadata from external databases, which introduces a minor risk of indirect prompt injection if database comments contain malicious instructions.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • Runlayer7mo

    1/1 file flagged

  • ZeroLeaks5mo

    1 finding · Score: 78/100

Signed by skilld at 6c059c1. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 9 months ago
  • CLI
  • snowflake
  • semantic-view
  • ddl
  • data-modeling
  • star-schema
  • sql
  • snowflake-cli
  • validation

README badge

README badge for github/awesome-copilot/snowflake-semanticview

Creates and validates Snowflake semantic views using the Snowflake CLI (snow command), handling CREATE/ALTER SEMANTIC VIEW DDL generation, dimension and fact discovery, and synonym/comment population. Targets teams building semantic layers on Snowflake and requires a working Snowflake CLI installation with configured connection.

Generated from the current SKILL.md.

Does this skill work with Snowflake CLI versions before 2.0?
The skill handles version differences by directing you to run `snow sql --help` to check the correct connection flag syntax for your installed version.
Can I use this skill without setting up a Snowflake connection first?
No. You must configure a Snowflake connection with `snow connection add` before the skill can validate semantic view DDL against Snowflake.
Does this skill generate synonyms and comments automatically?
No. The skill reads existing Snowflake table/column comments as the primary source and asks for user approval before creating new synonyms or comments; it never invents them.
What happens if semantic view validation fails?
The skill iterates on the DDL and re-runs validation via `snow sql` until the statement succeeds before applying it with the real view name.
Does this skill support non-star-schema models?
The skill workflow confirms the model follows a star schema (facts with conformed dimensions); it does not handle other data model patterns.

Generated from the current SKILL.md. These answers refresh after source changes.