snowflake-semanticview — GitHub Copilot skill: install and use
This skill creates, alters, and validates Snowflake semantic views through Snowflake CLI. Use it for semantic-layer definitions and related DDL troubleshooting.
npx skills add github/awesome-copilot --skill snowflake-semanticviewWhat snowflake-semanticview does
snowflake-semanticview works with CREATE SEMANTIC VIEW and ALTER SEMANTIC VIEW. It helps draft the DDL, inspect a star schema, and prepare a semantic view from facts and conformed dimensions. The workflow starts by confirming the database, schema, role, warehouse, and final view name.
For validation, the skill creates a temporary view name and sends the DDL to Snowflake with snow sql. It iterates on errors, applies the validated definition under the real name, runs a sample semantic-view query, and removes the temporary object. Dimensions, facts, and metrics should have synonyms and comments, with Snowflake comments used as the preferred source.
Snowflake CLI must be installed, and a connection must be configured with snow connection add. Install this skill when Copilot needs to validate semantic-view DDL against Snowflake. It is a poor fit for drafting SQL without access to a configured Snowflake connection.
What it helps with
- draft a semantic view DDL
- validate
CREATE SEMANTIC VIEWin Snowflake - inspect fact and dimension relationships
- add comments and synonyms to a semantic layer
- run a query against the final semantic view
Example prompts for the agent
- Create a semantic view for this star schema and draft the DDL.
- Validate this DDL with `snow sql` under a temporary view name.
- Set up the Snowflake connection and troubleshoot this `CREATE SEMANTIC VIEW` error.
How to install snowflake-semanticview
1Skills CLI (Vercel)
npx skills add github/awesome-copilot --skill snowflake-semanticview2GitHub CLI
gh skill install github/awesome-copilot snowflake-semanticview3Manually: put the files in place
.github/skills/snowflake-semanticview/SKILL.md1Skills CLI (Vercel)
npx skills add github/awesome-copilot --skill snowflake-semanticview -a claude-code2Manually: put the files in place
~/.claude/skills/snowflake-semanticview/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/snowflake-semanticview/
Manually: put the files in place
.agents/skills/snowflake-semanticview/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/snowflake-semanticview/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
Platform CLI
gemini skills install https://github.com/github/awesome-copilot --path skills/snowflake-semanticviewManually: put the files in place
.devin/skills/snowflake-semanticview/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set554
The whole set →Similar
Questions about snowflake-semanticview
How do I install snowflake-semanticview in GitHub Copilot?
Run npx skills add github/awesome-copilot --skill snowflake-semanticview or gh skill install github/awesome-copilot snowflake-semanticview. The skill file is placed at .github/skills/snowflake-semanticview/SKILL.md.
What does snowflake-semanticview do?
It helps create and alter Snowflake semantic views, validate their DDL through Snowflake CLI, and run a sample query against the final view. It also guides connection setup and the use of comments and synonyms.
What is required to validate a semantic view?
Install Snowflake CLI and configure a connection with snow connection add. The workflow also needs the target database, schema, role, and warehouse.
Source text
SKILL.md · MIT
---
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.
- 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:
```SQL
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)
```bash
# 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:
```bash
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.
© github · MIT · github/awesome-copilot