OverviewHistoryStatsSecurity
npx skills add ...
Documentation
SKILL.md
npx skills add exploreomni/omni-agent-skills --skill omni-to-snowflake-semantic-view
Convert an Omni Analytics topic into a Snowflake Semantic View YAML definition. Use this skill whenever someone wants to export Omni metrics to Snowflake, create a Semantic View from an Omni topic, harden BI metrics into the warehouse, or bridge Omni's semantic layer with Snowflake Cortex Analyst.
npx skills add exploreomni/omni-agent-skills --skill omni-to-snowflake-semantic-view
Converts an Omni topic into a Snowflake Semantic View YAML definition by first exploring the Omni model via API, then translating its definitions into the Snowflake Semantic View format.
Auth: a profile authenticates with an API key or OAuth. If
whoami(or any call) returns 401, hand off — ask the user to run! omni config login <profile>(OAuth 2.1 browser flow; it blocks ~2 min on the browser). Don't runconfig loginyourself in a headless/CI session (no browser → timeout); on a local interactive machine you may. See theomni-api-conventionsrule for profile setup (omni config init --auth oauth) and discovering command and request-body shapes with--schema.
Tip: Use
-o jsonto force structured output for programmatic parsing, or-o humanfor readable tables. The default isauto(human in a TTY, JSON when piped).
Before starting, determine which environment you are running in — this controls how SQL is executed against Snowflake in Step 7.
Check for the presence of the built-in Snowflake SQL execution capability:
If unsure, ask the user: "Are you running this inside Snowflake (Cortex Code / Notebooks), or in an external tool like Claude Code or Cursor?"
For Claude Code, Cursor, or any terminal-based agent, choose one of the following. Check availability in order:
snow) ✅ RecommendedSet a default connection to avoid repeating --connection on every call:
snowflake-connector-python)✋ STOP — Confirm the Snowflake connection method with the user before proceeding to Step 7. Record which method is being used so Step 7 generates the correct execution command.
Ask the user:
⚠️ STOP — Confirm all four answers before proceeding.
Identify the Shared Model and note its id. Always prefer the Shared Model over Schema or Workbook models.
For every view in base_view and joins, fetch its YAML:
If a view is prefixed with
omni_dbt_, fetch the file that also starts withomni_dbt_(e.g.omni_dbt_ecomm__order_items.view).
The base_view in the topic file is the primary table. Convert view names to Snowflake table references:
base_view / join value | Snowflake table |
|---|---|
ecomm__order_items | ECOMM.ORDER_ITEMS |
omni_dbt_ecomm__order_items | ECOMM.ORDER_ITEMS (strip omni_dbt_) |
The __ separator maps to schema (left) and table name (right). If the schema does not exist in Snowflake, skip that table entirely.
The joins parameter in the topic uses indentation to define the join chain — a table indented beneath another joins into its parent:
Skip any view that is a derived table (CTE defined in SQL in Omni). These have no physical Snowflake table to reference.
In each view file, find the dimension with primary_key: true — this becomes the unique: true dimension in the Semantic View.
✋ STOP — Confirm the table list with the user before continuing.
The topic's fields parameter controls which fields from the views are included in the Semantic View.
| Syntax | Meaning |
|---|---|
(no fields parameter) | Include all fields from all views |
all_views.* | Include all fields from all views |
view.* | Include all fields in the named view |
tag:<value> | Include all fields tagged with this value |
view.field | Include this specific field |
-view.field | Exclude this specific field |
Exclusions (prefixed with -) must be applied after all inclusions are resolved. The process is:
fields in order:
view.*, view.field, tag:x) → add matching fields to the set-view.field) → remove that field from the set, even if it was added by a wildcardExample:
Result: all order_items fields except cost and raw_json, plus users.country.
⚠️ Critical: A
-exclusion always wins. Never include a field that has been explicitly excluded, regardless of what wildcard included it.
Using the join hierarchy from Step 3 and the relationships.yaml fetched in Step 2c, map each join to a Snowflake Semantic View relationship.
Each entry in relationships.yaml looks like:
The on_sql field tells you the join columns. Extract the column names to populate relationship_columns in the output.
Available relationship parameters:
| Parameter | Description |
|---|---|
join_from_view | Source view the join originates from |
join_to_view | Target view being joined to |
join_type | SQL join type (e.g. always_left) |
on_sql | SQL condition — extract column names from this |
relationship_type | Cardinality (e.g. assumed_many_to_one) |
reversible | Whether the join is bi-directional |
where_sql | Additional WHERE clause when join is active |
For each view in the resolved field list, translate its Omni field definitions into Semantic View entries.
⚠️ Only translate fields that survived the Step 4 inclusion/exclusion resolution. Do not add fields that were excluded.
dimensions or time_dimensionsThe field name becomes the dimension name unless a label is defined. Carry description and synonyms directly.
Standard dimension:
Date/timestamp dimension → use time_dimensions:
Group dimension → translate to a CASE WHEN expression:
Bin dimension → translate to a CASE WHEN range expression:
Duration dimension → translate to a TIMESTAMPDIFF expression:
Boolean dimension → becomes a named filter (not a dimension):
metricsThe sql field references the source column and aggregate_type defines the aggregation.
Standard measure:
Derived measure (no aggregate_type) — references other measures:
Filtered measure → wraps in CASE WHEN:
Array and boolean filter variants:
Filter-only fields in Omni are parameterized inputs defined in a filters: block in a view YAML. They don't map to a real column — they inject dynamic values into other fields' SQL at query time via Mustache templates. Snowflake Semantic Views have no equivalent runtime parameter system, so these must be resolved to static SQL during conversion.
Example filter-only field definition:
Two reference patterns to detect in dimension/measure SQL:
Pattern 1 — Block (conditional) syntax:
The block is conditionally active when the user applies a filter. A {{^ }} block is the fallback when no filter is applied:
Pattern 2 — Value (direct injection) syntax:
Scan all dimension and measure SQL for both patterns using these signatures:
{{# <view>.<field>.filter }} — block open{{^ <view>.<field>.filter }} — block fallback open{{filters.<view>.<field>.value}} — direct value{{filters.<view>.<field>.range_start}} / {{filters.<view>.<field>.range_end}} — date rangesResolution logic — apply in order for each reference found:
Step 1 — Inline {{^ }} fallback (block pattern only):
If the SQL contains a {{^ view.field.filter }} fallback block, use the fallback expression as the static SQL — it's what executes when no filter is applied.
Step 2 — default_filter on the filter field definition:
Find the referenced filter-only field in the view YAML and check for a default_filter property. Render it as a static SQL condition:
default_filter value | Rendered SQL |
|---|---|
is: "Complete" | field = 'Complete' |
is: ["New York", "CA"] | field IN ('New York', 'CA') |
is: true | field IS TRUE |
greater_than: 100 | field > 100 |
For value syntax, substitute the rendered scalar value directly into the SQL string:
For range syntax, use range_start / range_end from the default_filter if defined, otherwise warn the user.
Step 3 — No fallback and no default_filter — warn the user:
⚠️ Field
<view>.<field>references filter-only field<view>.<filter_field>but has nodefault_filterand no inline{{^ }}fallback. It cannot be statically resolved for a Snowflake Semantic View. Options:
- Skip this field from the output entirely.
- Ask the user what static value or condition to hardcode.
- Use
1=1(always-true no-op) to preserve the field shape without the filter effect.
Summary decision tree:
⚠️ Filter-only fields themselves are never emitted as dimensions, metrics, or filters in the Semantic View output — they exist only to parameterize other fields' SQL.
module_custom_instructionsIf the topic has an ai_context parameter, include it as:
verified_queriesConvert each entry under sample_queries into a SQL statement for verified_queries. Use the prompt as the question and the description as context for writing the SQL.
✋ STOP — Review all dimensions, measures, and relationships with the user before generating the final output.
Valid top-level keys only:
name,description,tables,relationships,metrics,verified_queries,module_custom_instructions
The SQL is the same regardless of environment. The difference is how it is executed.
SQL to run:
Execution by environment:
| Environment | Command |
|---|---|
| Cortex Code | Execute the SQL directly in the active Snowflake session |
snow CLI | snow sql -q "CALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML(...)" or write YAML to a temp file and pipe it |
| SnowSQL | snowsql -a <account> -u <user> -f <sql_file.sql> |
| Python | conn.cursor().execute("<sql>") |
Tip for CLI environments: If the YAML is long, write it to a temporary
.sqlfile first and execute the file rather than passing it inline — this avoids shell escaping issues with the$$dollar-quoting.
Execute this the same way as the CREATE call above, using whichever connection method was established during the Runtime Environment Detection step.
# Show available profiles and select the appropriate one
omni config show
# If multiple profiles exist, ask the user which to use, then switch:
omni config use <profile-name>
# Confirm the active profile is authenticated and inspect your permissions:
omni whoami whoamiIf the agent can execute a SQL statement like `SELECT CURRENT_USER()` directly without any shell command → you are in Cortex Code.
Otherwise → you are in an external environment.# Check if available
command -v snow
# Configure a connection (first time)
snow connection add
# Execute SQL
snow sql -q "SELECT CURRENT_USER();" --connection <connection_name>snow connection set-default <connection_name># Check if available
command -v snowsql
# Execute SQL
snowsql -a <account> -u <user> -q "SELECT CURRENT_USER();"pip install snowflake-connector-pythonimport snowflake.connector
conn = snowflake.connector.connect(
account="<account>",
user="<user>",
password="<password>", # or use key-pair / SSO
warehouse="<warehouse>",
database="<database>",
schema="<schema>",
)
conn.cursor().execute("<SQL here>")omni models list --model-kind SHAREDomni models yaml-get <modelId> --file-name <topic_name>.topicomni models yaml-get <modelId> --file-name relationshipsomni models yaml-get <modelId> --file-name <view_name>.viewjoins:
user_order_facts: {} # skip — this is a derived CTE, not a physical table
ecomm__users: {} # joins to base_view (ORDER_ITEMS)
ecomm__inventory_items: # joins to base_view (ORDER_ITEMS)
ecomm__products: # joins to INVENTORY_ITEMS
demo__product_images: {} # joins to PRODUCTS
ecomm__distribution_centers: {} # joins to PRODUCTSfields:
- ecomm__order_items.* # include all order_items fields
- ecomm__users.country # include this one users field
- -ecomm__order_items.cost # remove cost — excluded even though * was used above
- -ecomm__order_items.raw_json # remove raw_json — same reason- join_from_view: ecomm__order_items
join_to_view: ecomm__inventory_items
join_type: always_left
on_sql: ${ecomm__order_items.inventory_item_id} = ${ecomm__inventory_items.id}
relationship_type: assumed_many_to_one# Omni view YAML
city:
sql: '"CITY"'
label: City
description: Customer's city# Semantic View output
- name: city
expr: CITY
description: Customer's city
data_type: TEXT# Omni
created_at:
sql: '"CREATED_AT"'
type: time
label: Created At# Semantic View output
time_dimensions:
- name: created_at
expr: CREATED_AT
data_type: TIMESTAMP# Omni
device_type_groups:
sql: ${device_type}
label: Device Type Groups
groups:
- filter:
is: [ mobile, tablet ]
name: Handheld
- filter:
is: desktop
name: Desktop
else: Other-- expr value
CASE
WHEN "DEVICE_TYPE" IN ('mobile', 'tablet') THEN 'Handheld'
WHEN "DEVICE_TYPE" = 'desktop' THEN 'Desktop'
ELSE 'Other'
END# Omni
age_bin:
sql: ${age}
bin_boundaries: [ 18, 35, 50, 65 ]-- expr value
CASE
WHEN "AGE" < 18 THEN 'below 18'
WHEN "AGE" >= 18 AND "AGE" < 35 THEN '>= 18 and < 35'
WHEN "AGE" >= 35 AND "AGE" < 50 THEN '>= 35 and < 50'
WHEN "AGE" >= 50 AND "AGE" < 65 THEN '>= 50 and < 65'
WHEN "AGE" >= 65 THEN '65 and above'
ELSE NULL
END# Omni
fulfillment_days:
duration:
sql_start: ${created_at[date]}
sql_end: ${delivered_at[date]}
intervals: [ days ]-- expr value (days)
CASE
WHEN TIMESTAMPADD(DAY, 1 * TIMESTAMPDIFF(DAY, DATE_TRUNC('DAY', "CREATED_AT"), DATE_TRUNC('DAY', "DELIVERED_AT")), DATE_TRUNC('DAY', "CREATED_AT"))
<= DATE_TRUNC('DAY', "DELIVERED_AT")
THEN TIMESTAMPDIFF(DAY, DATE_TRUNC('DAY', "CREATED_AT"), DATE_TRUNC('DAY', "DELIVERED_AT"))
ELSE TIMESTAMPDIFF(DAY, DATE_TRUNC('DAY', "CREATED_AT"), DATE_TRUNC('DAY', "DELIVERED_AT")) - 1
END# Omni
is_returned:
sql: '"IS_RETURNED"'
description: Whether the item was returned
completed_orders:
sql: ${status} = 'Complete'
label: Completed Orders# Semantic View output — goes in filters, not dimensions
filters:
- name: is_returned
expr: IS_RETURNED
description: Whether the item was returned
- name: completed_orders
expr: STATUS = 'Complete'
description: Completed Orders# Omni
total_sale_price:
sql: ${sale_price}
aggregate_type: sum
label: Total Sale Price
description: Total revenue of orders
synonyms: [ Total Revenue, Total Receipts ]# Semantic View output
metrics:
- name: total_sale_price
expr: COALESCE(SUM("SALE_PRICE"), 0)
description: Total revenue of orders
synonyms: [ Total Revenue, Total Receipts ]# Omni
gross_margin:
sql: ${total_sale_price} - ${total_cost}
label: Gross Margin# Semantic View output — top-level derived metric
metrics:
- name: gross_margin
expr: total_sale_price - total_cost# Omni
california_revenue:
sql: ${sale_price}
aggregate_type: sum
filters:
users.state:
is: California-- expr value
COALESCE(SUM(CASE WHEN "users"."STATE" = 'California' THEN "SALE_PRICE" ELSE NULL END), 0)# is: [New York, New Jersey] → "STATE" IN ('New York', 'New Jersey')
# is: true → field IS TRUE# In a view file — under a top-level `filters:` key
filters:
status_filter:
type: string
default_filter:
is: "Complete"
order_date_filter:
type: timestamp{{# order_items.status_filter.filter }} order_items.status {{/ order_items.status_filter.filter }}{{# order_items.status_filter.filter }} order_items.status {{/ order_items.status_filter.filter }}
{{^ order_items.status_filter.filter }} 1=1 {{/ order_items.status_filter.filter }}-- Direct value substitution
DATE_TRUNC('day', created_at) >= '{{filters.order_items.order_date_filter.value}}'
-- Range variants for date filters
created_at BETWEEN '{{filters.order_items.order_date_filter.range_start}}' AND '{{filters.order_items.order_date_filter.range_end}}'-- Input
{{# order_items.status_filter.filter }} order_items.status {{/ order_items.status_filter.filter }}
{{^ order_items.status_filter.filter }} 1=1 {{/ order_items.status_filter.filter }}
-- Resolved static SQL
1=1-- Input
DATE_TRUNC('day', created_at) >= '{{filters.order_items.order_date_filter.value}}'
-- default_filter: is: "2024-01-01"
-- Resolved
DATE_TRUNC('day', created_at) >= '2024-01-01'Does the SQL reference a filter-only field?
(via {{# view.field.filter }} OR {{filters.view.field.value}} etc.)
│
├── Block pattern → Does SQL have {{^ view.field.filter }} fallback?
│ ├── Yes → Use fallback expression as static SQL
│ └── No → Go to default_filter check ↓
│
├── Value pattern → Go to default_filter check ↓
│
└── default_filter check:
├── Yes → Render default_filter as static SQL / value substitution
└── No → Warn user, ask how to proceedmodule_custom_instructions:
question_categorization: <ai_context value>name: <name>
description: <string>
tables:
- name: <name>
description: <string>
base_table:
database: <database>
schema: <schema>
table: <table name>
dimensions:
- name: <name>
synonyms: [ <string>, ... ]
description: <string>
expr: <SQL expression>
data_type: <data type>
unique: <boolean>
time_dimensions:
- name: <name>
synonyms: [ <string>, ... ]
description: <string>
expr: <SQL expression>
data_type: <data type>
facts:
- name: <name>
synonyms: [ <string>, ... ]
description: <string>
expr: <SQL expression>
data_type: <data type>
metrics:
- name: <name>
synonyms: [ <string>, ... ]
description: <string>
expr: <SQL expression>
filters:
- name: <name>
synonyms: [ <string>, ... ]
description: <string>
expr: <SQL expression>
relationships:
- name: <string>
left_table: <table>
right_table: <table>
relationship_columns:
- left_column: <column>
right_column: <column>
metrics:
- name: <name>
synonyms: [ <string>, ... ]
description: <string>
expr: <SQL expression>
verified_queries:
- name: <string>
question: <string>
sql: <string>
use_as_onboarding_question: <boolean>CALL SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML('<database>.<schema>', $$
<yaml content here>
$$);# snow CLI example with a file
snow sql -f /tmp/create_semantic_view.sqlGRANT SELECT ON SEMANTIC VIEW <database>.<schema>.<name> TO ROLE <role>;