snowflake-semantic-views — community snowflake-semantic-views, snowflake-semantic-view-skill, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Data Analysis Agents needing advanced Snowflake semantic view creation and management capabilities. A skill for creating and enhancing snowflake semantic views in SQL

MiguelElGallo MiguelElGallo
[0]
[0]
Updated: 3/5/2026

Agent Capability Analysis

The snowflake-semantic-views skill by MiguelElGallo is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Perfect for Data Analysis Agents needing advanced Snowflake semantic view creation and management capabilities.

Core Value

Empowers agents to create and enhance Snowflake semantic views using SQL, providing advanced data visualization and query optimization capabilities through the Snowflake CLI and Snowflake connections.

Capabilities Granted for snowflake-semantic-views

Configuring Snowflake connections for semantic view management
Creating and optimizing Snowflake semantic views for advanced data analysis
Verifying and troubleshooting Snowflake CLI installations for seamless integration

! Prerequisites & Limits

  • Requires Snowflake CLI installation
  • Needs active Snowflake account and connection configuration
  • Snowflake-specific, may not be compatible with other database systems
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snowflake-semantic-views

Install snowflake-semantic-views, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command...

SKILL.md
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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 a SELECT statement with DISTINCT and LIMIT (max 1,000 rows) to discover relationships between fact and dimension tables, identify column data types, and create more meaningful comments and synonyms for the 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 the 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. Semantic views use a different SQL syntax, as shown here: https://docs.snowflake.com/en/user-guide/views-semantic/querying#querying-a-semantic-view Example:
SQL
1SELECT * FROM SEMANTIC_VIEW( 2 my_semview_name 3 DIMENSIONS customer.customer_market_segment 4 METRICS orders.order_average_value 5 ) 6 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 the Snowflake CLI before presenting it as final.
  • Prefer a temporary name for validation to avoid clobbering the real view.

Example CLI Validation (Template)

bash
1# Replace placeholders with real values. 2snow sql -q "<CREATE OR ALTER SEMANTIC VIEW ...>" --connection <connection_name>

If the Snowflake CLI uses a different connection flag in your version, run:

bash
1snow 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.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is snowflake-semantic-views?

Perfect for Data Analysis Agents needing advanced Snowflake semantic view creation and management capabilities. A skill for creating and enhancing snowflake semantic views in SQL

How do I install snowflake-semantic-views?

Run the command: npx killer-skills add MiguelElGallo/snowflake-semantic-view-skill/snowflake-semantic-views. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for snowflake-semantic-views?

Key use cases include: Configuring Snowflake connections for semantic view management, Creating and optimizing Snowflake semantic views for advanced data analysis, Verifying and troubleshooting Snowflake CLI installations for seamless integration.

Which IDEs are compatible with snowflake-semantic-views?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for snowflake-semantic-views?

Requires Snowflake CLI installation. Needs active Snowflake account and connection configuration. Snowflake-specific, may not be compatible with other database systems.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add MiguelElGallo/snowflake-semantic-view-skill/snowflake-semantic-views. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use snowflake-semantic-views immediately in the current project.

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