بنقرة واحدة
sql-query
Use SqlClient for raw SQL against mapped dataset aliases and parse columnar SQL responses safely.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Use SqlClient for raw SQL against mapped dataset aliases and parse columnar SQL responses safely.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Step-by-step orchestrator for building Domo App Studio apps with native KPI cards via community-domo-cli. Sequences app creation, pages, theme, hero metrics, native charts, filter cards, layout assembly, and navigation. CLI-first — no raw API calls.
Domo KPI card CRUD via community-domo-cli — body schema, column mapping, beast modes, chart type index with on-demand reference files, and gotchas.
**Generating sample data for Domo** -- invoke when a user needs to create realistic sample datasets and upload them to a Domo instance. Primary signals: requests for sample data, demo data, test data, fake data for Domo; mentions of Salesforce, Google Analytics, QuickBooks, NetSuite, Google Ads, Facebook Ads, HubSpot, Marketo, or Health Portal sample data; questions about the datagen CLI or domo_data_generator. Covers: generating datasets, uploading to Domo, creating datasets in Domo, rolling dates, entity pools, connector icons, catalog management, and adding new dataset definitions. Skip for: real connector setup, production data pipelines, data transformations (Magic ETL), or Domo App Platform.
Create AppDB collections via CLI-first workflows where collection creation also provisions the required datastore, then returns collection identifiers for manifest wiring and document-write follow-up. Use when an agent must initialize new AppDB storage for a Domo app, not just list/get collections or create documents.
Create Domo Code Engine packages from CLI workflows with deterministic payload contracts, automatic function parameter datatype mapping, and manifest packagesMapping follow-up guidance. Use when an agent must create a new package/versioned package container rather than only invoke an existing function from app runtime code.
Update Domo Code Engine packages through CLI-driven versioned lifecycle workflows with compatibility checks, datatype contract safeguards, and manifest mapping drift synchronization. Use when an agent must update package code or create a new package version and keep app mappings aligned.
| name | sql-query |
| description | Use SqlClient for raw SQL against mapped dataset aliases and parse columnar SQL responses safely. |
Apply when executing SQL queries against Domo datasets, using AI-generated SQL from AIClient.text_to_sql, or any time you need to run raw SQL against a dataset alias. Use SqlClient from @domoinc/toolkit instead of domo.post('/sql/v1/').
SqlClient is the toolkit wrapper for Domo's SQL API. Use it to execute SQL queries against datasets mapped in manifest.json.
import { SqlClient } from '@domoinc/toolkit';
const sqlClient = new SqlClient();
get(alias, query)Executes a SQL query against a dataset.
const result = await sqlClient.get('datasetAlias', 'SELECT * FROM datasetAlias');
Parameters:
alias (string): dataset alias from manifest.json mappingsquery (string): SQL query stringReturns: Promise<Response<SqlResponse>>
parsePageFilters(datasets, produceClauses?)Transforms Domo page filters into SQL predicates.
// Get predicates as objects
const predicates = sqlClient.parsePageFilters(['datasetAlias']);
// Get predicates as WHERE/HAVING clause strings
const clauses = sqlClient.parsePageFilters(['datasetAlias'], true);
CRITICAL: SQL API returns a columnar format, not an array of row objects.
// Response shape:
{
columns: ['vendor', 'Total Spend'],
rows: [
['Sysco Utah', 1880794.74],
['Intermountain Meats', 809389.15]
],
metadata: [...],
numRows: 8,
numColumns: 2,
datasource: 'dataset-uuid',
fromcache: true
}
You must zip columns + rows into objects for UI rendering:
const result = await sqlClient.get('myAlias', sql);
const res = result?.body || result?.data || result;
const colNames: string[] = res?.columns || [];
const rawRows: unknown[][] = res?.rows || [];
const rows = rawRows.map((row) => {
const obj: Record<string, unknown> = {};
colNames.forEach((col, i) => {
obj[col] = row[i];
});
return obj;
});
// rows => [{ vendor: 'Sysco Utah', 'Total Spend': 1880794.74 }, ...]
Use AIClient to generate SQL, then SqlClient to execute it:
import { AIClient, SqlClient } from '@domoinc/toolkit';
// 1) Generate SQL from natural language
const aiResponse = await AIClient.text_to_sql(question, [
{
dataSourceName: 'myAlias',
description: 'Description of the dataset',
columns: [
{ name: 'vendor', type: 'string' },
{ name: 'amount', type: 'number' }
]
}
]);
const responseBody = aiResponse.data || aiResponse.body || aiResponse;
const sql = responseBody.output || responseBody.choices?.[0]?.output;
// 2) Execute SQL
const sqlClient = new SqlClient();
const result = await sqlClient.get('myAlias', sql);
// 3) Parse columnar response into row objects
const res = result?.body || result?.data || result;
const colNames: string[] = res?.columns || [];
const rawRows: unknown[][] = res?.rows || [];
const rows = rawRows.map((row) => {
const obj: Record<string, unknown> = {};
colNames.forEach((col, i) => {
obj[col] = row[i];
});
return obj;
});
SqlClient instead of domo.post('/sql/v1/').parsePageFilters() to inject filters manually when needed.FROM must match manifest alias (example: SELECT * FROM vendorPayments).columns + rows), never a flat array of objects..body, .data, or directly on result; parse defensively.