Snowflake Semanticview
GitHubOfficialFirst committed by MiguelElGalloDistributed by GitHub
- Skill
- Tool integration
- Data and AI
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic approach to creating, altering, and validating Snowflake semantic views. It emphasizes a rigorous validation process using the Snowflake CLI to ensure DDL correctness before final deployment. The workflow covers initial environment setup, DDL drafting based on star schema models, mandatory documentation of synonyms and comments, and iterative testing using temporary validation views.
What it does
- Guides the installation and configuration of the Snowflake CLI.
- Drafts DDL for semantic views based on star schema models.
- Integrates existing Snowflake table comments and synonyms into semantic definitions.
- Executes DDL validation against Snowflake instances via the CLI.
- Provides a workflow for iterative troubleshooting of semantic view definitions.
- Ensures clean deployment by using temporary views for validation.
When to use it
- When you need to build or modify a semantic layer in Snowflake.
- When you need to validate semantic view DDL against a live Snowflake environment.
- When setting up the Snowflake CLI for semantic view management.
- When you need to ensure semantic views are properly documented with synonyms and comments.
Advantages
- Reduces errors by enforcing a mandatory validation step before final creation.
- Promotes consistency by leveraging existing Snowflake metadata for comments.
- Prevents accidental overwrites by utilizing temporary validation views.
- Provides a clear, repeatable process for managing complex semantic layer definitions.
Usage examples
- “Help me create a new semantic view for my sales data.”
- The agent will guide you through confirming your schema, drafting the DDL with required synonyms and comments, and executing a validation command via the Snowflake CLI.
- “My semantic view DDL is failing validation, how do I fix it?”
- The agent will help you inspect the error, suggest DDL adjustments, and guide you through re-running the validation command using the Snowflake CLI.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/github/snowflake-semanticview/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip snowflake-semanticview.zip -d .agents/skills
Documentation by MiguelElGallo
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 addper 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
Confirm the target database, schema, role, warehouse, and final semantic view name.
Confirm the model follows a star schema (facts with conformed dimensions).
Draft the semantic view DDL using the official syntax:
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.
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.
Create a temporary validation name (for example, append
__tmp_validate) while keeping the same database and schema.Always validate by sending the DDL to Snowflake via Snowflake CLI before finalizing:
Use
snow sqlto execute the statement with the configured connection.If flags differ by version, check
snow sql --helpand use the connection option shown there.
If validation fails, iterate on the DDL and re-run the validation step until it succeeds.
Apply the final DDL (create or alter) using the real semantic view name.
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;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 --helpNotes
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.
Files
- ATTRIBUTION.mdAdded by AutoGineer887 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md4.3 KiB
Requirements and compatibility
Written for: GitHub Copilot
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
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Attribution and licence
- First committed by
MiguelElGallo
- Publisher
- GitHub (Official)
- Distributed by
- GitHub
- Source
- github/awesome-copilot · skills/snowflake-semanticview · commit 797337f
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- MiguelElGallo
AutoGineer is not affiliated with or endorsed by MiguelElGallo or GitHub.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download snowflake-semanticview.zip