Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
github avatar

Snowflake Semanticview

  • 8.9k installs
  • 37.1k repo stars
  • Updated July 28, 2026
  • github/awesome-copilot

snowflake-semanticview is an agent skill that Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic layer definitio.

About

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. --- 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 - 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.

  • Snowflake Semantic Views
  • 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/
  • Configure a Snowflake connection with `snow connection add` per https://docs.snowflake.com/en/developer-guide/snowflake-
  • Use the configured connection for all validation and execution steps.

Snowflake Semanticview by the numbers

  • 8,941 all-time installs (skills.sh)
  • +20 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #12 of 2,066 Data Science & ML skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

snowflake-semanticview capabilities & compatibility

Capabilities
snowflake semantic views · verify snowflake cli installation by opening a n · if snowflake cli is missing or the user cannot i · configure a snowflake connection with `snow conn · use the configured connection for all validation
Use cases
documentation
From the docs

What snowflake-semanticview says it does

--- name: snowflake-semanticview description: Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow).
SKILL.md
--- # Snowflake Semantic Views ## One-Time Setup - Verify Snowflake CLI installation by opening a new terminal and running `snow --help`.
SKILL.md
- 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.
SKILL.md
- Configure a Snowflake connection with `snow connection add` per https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections#add-a-connection.
SKILL.md
npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs8.9k
repo stars37.1k
Security audit3 / 3 scanners passed
Last updatedJuly 28, 2026
Repositorygithub/awesome-copilot

What problem does snowflake-semanticview solve for developers using this skill?

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 v

Who is it for?

Developers who need snowflake-semanticview patterns described in the cached skill documentation.

Skip if: Skip when docs are empty or the task is outside the skill's documented scope.

When should I use this skill?

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 v

What you get

Actionable workflows and conventions from SKILL.md for snowflake-semanticview.

  • SEMANTIC VIEW DDL
  • CLI validation results

By the numbers

  • Uses Snowflake CLI snow commands for semantic view DDL
  • Covers CREATE and ALTER SEMANTIC VIEW operations

Files

SKILL.mdMarkdownGitHub ↗

Snowflake Semantic Views

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-view Example:

SELECT * FROM SEMANTIC_VIEW(
    my_semview_name
    DIMENSIONS customer.customer_market_segment
    METRICS orders.order_average_value
)
ORDER BY customer_market_segment;

11. 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.

Related skills

How it compares

Choose snowflake-semanticview over generic SQL skills when work is specific to Snowflake semantic views and CLI-backed validation is required.

FAQ

What does snowflake-semanticview do?

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-vie

When should I use 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-vie

Is snowflake-semanticview safe to install?

Review the Security Audits panel on this page before installing in production.

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.