data360-query
forcedotcom/sf-skills
Query, search, and inspect Salesforce Data Cloud objects with SQL, vector search, and metadata introspection.
What is data360-query?
This skill handles Data Cloud retrieve operations: SQL queries (sync and async), vector and hybrid search, search-index lifecycle, and metadata inspection. Use it when working with Data Cloud SQL, async queries, vector search, search-index workflows, or describing Data Cloud objects—not for standard CRM SOQL or segment design.
- Run sync and async SQL queries against Data Cloud tables
- Execute vector and hybrid semantic searches on search indexes
- Describe Data Cloud object schemas and field metadata
- Manage search-index lifecycle and configuration
- Classify org readiness for Data Cloud retrieve operations
- Handle paginated result sets with sqlv2 and async query flows
How to install data360-query
npx skills add https://github.com/forcedotcom/sf-skills --skill data360-query- Node.js ≥18.0.0
- Salesforce CLI (sf) ≥2.0.0
- Access to a Salesforce org with Data Cloud enabled
- Target org alias configured in sf
How to use data360-query
- 1.Run the readiness classifier to confirm org health: `node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --json`
- 2.Choose the appropriate query shape: COUNT for quick counts, sql for small sets, sqlv2 for medium paginated results, async-create for large exports
- 3.Use describe to inspect table schema before querying: `sf data360 query describe -o <org> --table <table_name>`
- 4.For search operations, list available indexes first: `sf data360 search-index list -o <org>`
- 5.Execute your query or search command and verify results match expected row counts and schema
Use cases
- Query a large Data Cloud table asynchronously and export results
- Search a knowledge base using vector embeddings with semantic queries
- Inspect the schema of a Data Cloud object before writing queries
- Set up and validate a hybrid search index with prefilter conditions
- Diagnose Data Cloud readiness and query-plane health before retrieve work
- Data Cloud architects and developers
- Analytics engineers querying enterprise data models
- Teams building semantic search experiences
- Data integration specialists inspecting Data Cloud schemas
data360-query FAQ
Use data360-query for Data Cloud SQL, async queries, vector search, and search-index operations. Use platform-soql-query only for standard CRM SOQL against Salesforce objects.
sql is for quick counts and small result sets; sqlv2 handles medium result sets with pagination; async-create is for large exports that run in the background.
Yes, but only on fields configured as prefilter-capable when the search index was created. Use the --prefilter flag to narrow results.
Run `sf data360 search-index list -o <org>` to list indexes and check their status. Use the readiness classifier to diagnose broader Data Cloud health.
Yes, always use double quotes around table names in Data Cloud SQL queries, e.g., `SELECT * FROM "ssot__Individual__dlm"`.
Full instructions (SKILL.md)
Source of truth, from forcedotcom/sf-skills.
name: data360-query description: "Salesforce Data Cloud Retrieve phase. Use this skill when the user runs Data Cloud SQL, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use platform-soql-query), segment creation or calculated insight design (use data360-segment), or STDM/session tracing/parquet analysis (use agentforce-observe)." metadata: cliTools: - tool: ["node"] semver: ">=18.0.0" - tool: ["sf"] semver: ">=2.0.0" relatedSkills: - "agentforce-observe" - "data360-orchestrate" - "data360-segment" - "platform-soql-query" version: "1.0" domains: ["Data 360"]
data360-query: Data Cloud Retrieve Phase
Use this skill when the user needs query, search, and metadata introspection for Data Cloud: sync SQL, paginated SQL, async query workflows, table describe, vector search, hybrid search, or search index operations.
When This Skill Owns the Task
Use data360-query when the work involves:
sf data360 query *sf data360 search-index *sf data360 metadata *sf data360 profile *orsf data360 insight *inspection- understanding Data Cloud SQL results or query shape
Delegate elsewhere when the user is:
- writing standard CRM SOQL only → platform-soql-query
- designing segment or calculated insight assets → data360-segment
- analyzing STDM/session tracing/parquet telemetry → agentforce-observe
Required Context to Gather First
Ask for or infer:
- target org alias
- whether the user needs quick count, medium result set, large export, schema inspection, or semantic search
- table/index name if known
- whether the task is read-only SQL or search-index lifecycle management
Core Operating Rules
- Treat Data Cloud SQL as its own query language, not SOQL.
- Run the shared readiness classifier before relying on query/search surfaces:
node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --json. - Use describe before guessing columns.
- Prefer
sqlv2or async query flows for larger result sets. - Use vector search or hybrid search only when the search index lifecycle is healthy.
- Keep STDM/parquet/session-tracing workflows out of this skill family.
Recommended Workflow
1. Classify readiness for retrieve work
node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --json
# optional query-plane probe, only with a real table name
node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --describe-table MyDMO__dlm --json
2. Choose the smallest correct query shape
sf data360 query sql -o <org> --sql 'SELECT COUNT(*) FROM "ssot__Individual__dlm"' 2>/dev/null
sf data360 query sqlv2 -o <org> --sql 'SELECT * FROM "ssot__Individual__dlm"' 2>/dev/null
sf data360 query async-create -o <org> --sql 'SELECT * FROM "ssot__Individual__dlm"' 2>/dev/null
3. Use describe before guessing fields
sf data360 query describe -o <org> --table ssot__Individual__dlm 2>/dev/null
4. Use vector or hybrid search only when an index exists
sf data360 search-index list -o <org> 2>/dev/null
sf data360 query vector -o <org> --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null
sf data360 query hybrid -o <org> --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null
sf data360 query hybrid -o <org> --index Insurance_Index --query "weather damage coverage" --prefilter "Type_of_Insurance__c='Home'" --limit 10 2>/dev/null
5. Reuse curated search-index examples when creating indexes
Use the phase-owned examples instead of inventing JSON from scratch:
examples/search-indexes/vector-knowledge.jsonexamples/search-indexes/hybrid-structured.json
High-Signal Gotchas
- Data Cloud SQL is not SOQL.
- Table names should be double-quoted in SQL.
sqlv2is better than ad hoc OFFSET paging for medium result sets.- async query is preferable for large results.
- search-index operations and vector/hybrid queries depend on the index lifecycle being healthy.
- Hybrid search can use
--prefilter, but only on fields configured as prefilter-capable when the search index was created. - HNSW index parameters are typically read-only on create; leave
userValues: []unless the platform explicitly documents otherwise. query describeis not a universal tenant probe; only run it with a known DMO or DLO table after broader readiness has been confirmed.
Output Format
Retrieve task: <sql / sqlv2 / async / describe / vector / search-index>
Target org: <alias>
Target object: <table or index>
Commands: <key commands run>
Verification: <query rows / schema / status>
Next step: <segment / harmonize / follow-up>
References
- examples/search-indexes/vector-knowledge.json
- examples/search-indexes/hybrid-structured.json
- ../data360-orchestrate/assets/definitions/search-index.template.json
- ../data360-orchestrate/references/plugin-setup.md
- ../data360-orchestrate/references/feature-readiness.md
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