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retrieving-datacloud

retrieving-datacloud

github.com/forcedotcomData & databases1.1k2.5k installsUpdated 1d ago
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About retrieving-datacloud

A skill for Salesforce Data Cloud retrieval tasks, covering SQL queries, schema inspection, vector and hybrid search, and search-index operations. It guides query selection and readiness checks for people working with Data Cloud.

Key features

  1. 01Synchronous and paginated SQL queries
  2. 02Asynchronous query workflows
  3. 03Table schema inspection
  4. 04Vector and hybrid search
  5. 05Search-index operations

Use cases

  1. 01Inspecting Data Cloud tables and fields
  2. 02Running large query result sets
  3. 03Searching indexed content semantically

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/forcedotcom/sf-skills --skill retrieving-datacloud

After installing, type/retrieving-datacloudin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

CREDITS.md
README.md
examples/search-indexes/
hybrid-structured.json
vector-knowledge.json

Details

From the project README

retrieving-datacloud: 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 retrieving-datacloud when the work involves:

  • sf data360 query *
  • sf data360 search-index *
  • sf data360 metadata *
  • sf data360 profile * or sf data360 insight * inspection
  • understanding Data Cloud SQL results or query shape

Delegate elsewhere when the user is:

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 ../orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase retrieve --json.
  • Use describe before guessing columns.
  • Prefer sqlv2 or 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 ../orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase retrieve --json
# optional query-plane probe, only with a real table name
node ../orchestrating-datacloud/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.json
  • examples/search-indexes/hybrid-structured.json

High-Signal Gotchas

  • Data Cloud SQL is not SOQL.
  • Table names should be double-quoted in SQL.
  • sqlv2 is 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 describe is 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

Comments

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