snowflake-semanticview
Manage and validate Snowflake semantic layer definitions.
Install
mkdir -p .claude/skills/snowflake-semanticview && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/542" && unzip -o skill.zip -d .claude/skills/snowflake-semanticview && rm skill.zipInstalls to .claude/skills/snowflake-semanticview
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Generates semantic view DDL
- →Validates DDL via Snowflake CLI
- →Enforces star schema modeling
- →Populates synonyms and comments
How it works
Executes SQL DDL generation based on star schema constraints and validates output by piping statements through the Snowflake CLI.
Inputs & outputs
When to use snowflake-semanticview
- →Create new semantic view DDL
- →Validate semantic layer definitions
- →Automate synonym and comment generation for views
About this skill
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.
- Use
- 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 --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.
When not to use it
- →Managing non-Snowflake databases
- →Handling non-tabular data structures
Prerequisites
Limitations
- →Requires active Snowflake connection
- →Limited to star schema design patterns
How it compares
It forces semantic layer validation directly against the database instance before finalizing the code.
Compared to similar skills
snowflake-semanticview side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| snowflake-semanticview (this skill) | 5 | 6mo | Review | Advanced |
| setup-timescaledb-hypertables | 0 | 4mo | No flags | Advanced |
| comparing-database-schemas | 1 | 27d | Review | Advanced |
| database-schema-design | 0 | 3mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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