Agent skill
wren-sql
Write and correct SQL queries targeting Wren Engine — covers MDL query rules, filter strategies, data types (ARRAY, STRUCT, JSON/VARIANT), date/time functions, Calculated Fields, BigQuery dialect quirks, and error diagnosis. Use when generating or debugging SQL for any Wren Engine data source.
Install this agent skill to your Project
npx add-skill https://github.com/Canner/wren-engine/tree/main/skills-archive/wren-sql
Metadata
Additional technical details for this skill
- author
- wren-engine
- version
- 1.0
SKILL.md
Wren SQL
Version check
Silently fetch https://raw.githubusercontent.com/Canner/wren-engine/main/skills/versions.json.
Compare the wren-sql key with this skill's version (from the frontmatter above).
If the remote version is newer, notify the user before proceeding:
A newer version of the wren-sql skill is available. Update with:
npx skills add Canner/wren-engine --skill wren-sql --agent claude-code
Then continue with the workflow below regardless of update status.
Wren Engine translates SQL through a semantic layer (MDL — Model Definition Language) before executing it against a backend database. SQL must target MDL model names, not raw database tables.
For specific topics, load the relevant reference file:
| Topic | Reference |
|---|---|
| SQL error diagnosis and correction | references/correction.md |
| Date/time functions and intervals | references/datetime.md |
| ARRAY, STRUCT, JSON/VARIANT types | references/types.md |
| BigQuery dialect quirks | references/bigquery.md |
Context
- You are querying a semantic layer, not a database directly.
- Only use model/view/column names defined in the MDL — never raw database table references.
- Wren Engine uses a generic SQL dialect similar to ANSI SQL (DataFusion/Postgres/DuckDB), but with differences.
- Check the
dataSourcefield to identify the backend and apply dialect-specific rules if needed.
Core SQL Rules
- Only
SELECTstatements. NoDELETE,UPDATE,INSERT. - Only use tables and columns from the MDL schema.
- Do not include comments in generated SQL.
- Prefer CTEs over subqueries.
- Identifiers are case-sensitive. Quote identifiers containing unicode, special characters (except
_), or starting with a digit using double quotes.- Examples:
"客户"."姓名","table-name"."col","123column"
- Examples:
- Identifier quotes:
"(double quotes). String literal quotes:'(single quotes). - For specific date queries, use a range:
sql
WHERE ts >= CAST('2024-11-01 00:00:00' AS TIMESTAMP WITH TIME ZONE) AND ts < CAST('2024-11-02 00:00:00' AS TIMESTAMP WITH TIME ZONE) - For ranking, use
DENSE_RANK()+WHERE. Include the ranking column inSELECT. - Avoid correlated subqueries — use JOINs instead.
- Use
SAFE_CASTwhen casting might fail:SAFE_CAST(col AS INT)
Filter Strategies
| Column type | Strategy |
|---|---|
| Text | LIKE '%value%' for partial match |
| Numeric | BETWEEN 30 AND 40 |
| Date/Timestamp | >= '2024-01-01' AND < '2024-02-01' |
| Exact value | = or IN (...) |
| Primary key / indexed | Prefer equality (=) |
Supported Cast Types
bool, boolean, int, integer, bigint, smallint, tinyint, float, double, real, decimal, numeric, varchar, char, string, text, date, time, timestamp, timestamp with time zone, bytea
Example: CAST(col AS INT), TIMESTAMP '2024-11-09 00:00:00'
Aggregation
- All non-aggregated
SELECTcolumns must appear inGROUP BY(window functions excepted). - Aggregate conditions go in
HAVING, notWHERE. - Prefer ordinal
GROUP BYfor long column names:sqlSELECT very_long_column_name AS alias, COUNT(*) FROM t GROUP BY 1
Sorting and Limiting
ORDER BYfor sort;LIMITto restrict rows.- When
ORDER BYappears in a subquery or CTE, always includeLIMIT.
Subquery Patterns
- Prefer CTEs (
WITHclause) over nested subqueries. - Subquery in
SELECTmust return a single value per row. - Subquery in
WHERE: useIN,EXISTS, or comparison operators. IN SUBQUERYinJOINconditions is not supported — useJOIN ... ONinstead.RECURSIVECTEs are not supported.
Calculated Fields
Columns marked as Calculated Field in the MDL have pre-defined computation logic. Use them directly instead of re-implementing the calculation.
Read the column comment (e.g., column expression: avg(reviews.Score)) to understand what the field represents.
-- Schema has: Rating DOUBLE (Calculated Field: avg(reviews.Score))
-- ReviewCount BIGINT (Calculated Field: count(reviews.Id))
-- Correct — use Calculated Fields directly:
SELECT AVG(Rating) FROM orders WHERE ReviewCount > 10
-- Incorrect — do not re-join and re-aggregate manually
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