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github/snowflake-semanticview

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snowflake-semanticview

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.

v1.0Latest
New~1.1kUpdated Jun 26, 2026

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.
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Overall Score

82/100

Grade

B

Good

Safety

85

Quality

82

Clarity

88

Completeness

75

Summary

A skill for creating, altering, and validating Snowflake semantic views using the Snowflake CLI. It guides agents through one-time setup (CLI installation and connection configuration), drafting semantic view DDL following star schema patterns, populating synonyms and comments, discovering table relationships, validating DDL against Snowflake via CLI before deployment, and running sample queries to confirm functionality.

Detected Capabilities

snowflake-cli-executionsql-executionfile-discoverytable-schema-inspectionddl-validation

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

create semantic viewsemantic view ddlsnowflake semantic layervalidate semantic viewsnowflake cli setup

Risk Signals

INFO

Outbound reference to docs.snowflake.com for installation and syntax guidance

One-Time Setup section and throughout
INFO

SQL query execution against Snowflake via snow CLI with user-provided connection credentials

Validation Pattern and Example CLI Validation sections
INFO

SELECT statements with LIMIT 1000 for schema discovery

Step 5 of Workflow

Referenced Domains

External domains referenced in skill content, detected by static analysis.

docs.snowflake.com

Use Cases

  • Build new semantic views in Snowflake with proper synonyms and comments
  • Alter existing semantic views based on schema or business logic changes
  • Validate semantic view DDL against Snowflake before production deployment
  • Debug semantic view definitions by iterating validation against live Snowflake
  • Discover table relationships and column metadata to improve semantic view documentation

Quality Notes

  • Clear, step-by-step workflow with 11 well-defined steps for semantic view creation and validation
  • Excellent scope boundaries: skill is laser-focused on semantic views, not general Snowflake operations
  • Security guardrails are strong: validation is mandatory before final deployment, temporary view naming convention prevents accidental overwrites
  • Comprehensive references to official Snowflake documentation for syntax and best practices
  • Good error handling guidance: iteration on DDL until validation succeeds (Step 8)
  • Synonyms and comments treated as required completeness standards, not optional
  • Sample query syntax provided with clear example of semantic view querying
  • One notable omission: no explicit guidance on handling sensitive data in comments (e.g., PII or business secrets)
  • No guidance on role/permission model for semantic view creation in shared environments
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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