Generating Validation Rule

Name: Generating Validation Rule
Author: forcedotcom

forcedotcom/sf-skills

2.4k installs
763 repo stars
Updated July 24, 2026
forcedotcom/sf-skills

data360-query skill runs Data Cloud retrieve SQL, async jobs, vector or hybrid search, and metadata describe via sf data360.

About

data360-query handles Salesforce Data Cloud retrieve work including sync SQL, paginated sqlv2, async query jobs, table describe, vector search, hybrid search, and search index lifecycle via the sf data360 CLI plugin. It applies when users run sf data360 query, search-index, metadata, profile, or insight commands on Data Cloud enabled orgs. Operating rules require diagnose-org --phase retrieve readiness, describe before column guesses, sqlv2 over OFFSET paging for medium sets, and async-create for large exports. Data Cloud SQL uses double-quoted table names and differs from CRM SOQL delegated to platform-soql-query. Vector and hybrid queries need healthy indexes listed before use with prefilter limited to configured fields. Curated examples in examples search-indexes supply vector-knowledge and hybrid-structured templates instead of inventing JSON. STDM parquet tracing belongs to agentforce-observe while segment design routes to data360-segment. Output format documents retrieve task type, org alias, target object, commands run, verification rows or schema, and suggested next harmonize or segment steps.

sf data360 query sql sqlv2 and async-create flows.
diagnose-org retrieve readiness classifier script.
describe tables before selecting Data Cloud columns.
Vector and hybrid search with index list checks.
Delegation matrix to SOQL, segment, and observe skills.

Generating Validation Rule by the numbers

2,414 all-time installs (skills.sh)
+7 installs in the week ending Jul 28, 2026 (Skillselion tracking)
Ranked #42 of 2,066 Data Science & ML skills by installs in the Skillselion catalog
Security screen: LOW risk (skills.sh audit)
Data as of Jul 28, 2026 (Skillselion catalog sync)

At a glance

generating-validation-rule capabilities & compatibility

Capabilities: retrieve phase diagnose org readiness checks · sync, sqlv2, and async data cloud sql execution · table describe for schema introspection · vector and hybrid search with prefilter rules · search index list and curated example reuse · cross skill delegation for soql and segments
Works with: salesforce
Use cases: data analysis · database
Pricing: Free

From the docs

What generating-validation-rule says it does

Use the phase-owned examples instead of inventing JSON from scratch

SKILL.md

npx skills add https://github.com/forcedotcom/sf-skills --skill generating-validation-rule

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Installs	2.4k
repo stars	★ 763
Security audit	3 / 3 scanners passed
Last updated	July 24, 2026
Repository	forcedotcom/sf-skills ↗

How do I retrieve Data Cloud rows, run semantic search, and verify schema on a Data Cloud org?

Execute Data Cloud retrieve SQL, async exports, vector search, and search-index workflows with sf data360 CLI.

Who is it for?

Developers operating Data Cloud query and search-index surfaces with the community data360 CLI plugin.

Skip if: Skip for CRM-only SOQL, Salesforce validation rule XML, or STDM telemetry analysis.

When should I use this skill?

User runs Data Cloud SQL, async queries, vector or hybrid search, or sf data360 metadata commands.

What you get

Readiness-checked commands with correct SQL variant, describe output, and verified index-backed search results.

Validation Rule metadata
Formula expressions
Custom error messages

By the numbers

Skill metadata version is 1.0

Files

SKILL.mdMarkdownGitHub ↗

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 * or sf 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 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 ../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.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

README.md
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

{
  "label": "<INDEX_NAME>",
  "developerName": "<INDEX_NAME>",
  "description": "Hybrid search index on a structured Data Cloud DMO",
  "sourceDmoDeveloperName": "<SOURCE_DMO>__dlm",
  "chunkDmoName": "<INDEX_NAME> chunk",
  "chunkDmoDeveloperName": "<INDEX_NAME>_chunk",
  "vectorDmoName": "<INDEX_NAME> index",
  "vectorDmoDeveloperName": "<INDEX_NAME>_index",
  "searchType": "HYBRID",
  "vectorEmbedding": {
    "vectorEmbeddingRelatedFields": []
  },
  "rankingConfigurations": [],
  "chunkingConfiguration": {
    "fieldLevelConfigurations": [
      {
        "sourceDmoDeveloperName": "<SOURCE_DMO>__dlm",
        "sourceDmoFieldDeveloperName": "<TEXT_FIELD>__c",
        "config": {
          "id": "passage_extraction",
          "userValues": [
            { "id": "max_tokens", "value": "512" },
            { "id": "strip_html", "value": "true" }
          ]
        }
      }
    ]
  },
  "vectorEmbeddingConfiguration": {
    "embeddingModel": {
      "id": "e5_large_v2",
      "userValues": [
        { "id": "dimension", "value": "1024" },
        { "id": "max_token_limit", "value": "512" }
      ]
    },
    "index": {
      "id": "HNSW",
      "userValues": []
    },
    "similarityMetric": "COSINE"
  }
}

{
  "label": "My_kav",
  "developerName": "My_kav",
  "sourceDmoDeveloperName": "ssot__KnowledgeArticleVersion__dlm",
  "chunkDmoName": "My_kav chunk",
  "chunkDmoDeveloperName": "My_kav_chunk",
  "vectorDmoName": "My_kav index",
  "vectorDmoDeveloperName": "My_kav_index",
  "searchType": "VECTOR",
  "vectorEmbedding": {
    "vectorEmbeddingRelatedFields": []
  },
  "chunkingConfiguration": {
    "fieldLevelConfigurations": [
      {
        "sourceDmoDeveloperName": "ssot__KnowledgeArticleVersion__dlm",
        "sourceDmoFieldDeveloperName": "ssot__Name__c",
        "config": {
          "id": "passage_extraction",
          "userValues": [
            { "id": "strip_html", "value": "true" },
            { "id": "max_tokens", "value": "512" }
          ]
        }
      }
    ]
  },
  "vectorEmbeddingConfiguration": {
    "embeddingModel": {
      "id": "e5_large_v2",
      "userValues": [
        { "id": "dimension", "value": "1024" },
        { "id": "max_token_limit", "value": "512" }
      ]
    },
    "index": {
      "id": "HNSW",
      "userValues": []
    },
    "similarityMetric": "COSINE"
  },
  "rankingConfigurations": []
}

data360-query

Query and search workflows for Salesforce Data Cloud.

Use this skill for

quick SQL counts
paginated SQL (sqlv2)
async query lifecycles
table describe
vector search
hybrid search with optional prefilter
search index inspection and lifecycle work

Example requests

"Run a Data Cloud SQL query against unified profiles"
"Describe this Data Cloud table before I write SQL"
"Help me troubleshoot vector search in Data Cloud"
"Run a hybrid search with a prefilter in Data Cloud"
"Create and inspect a search index"

Common commands

sf data360 query sql -o myorg --sql 'SELECT COUNT(*) FROM "ssot__Individual__dlm"' 2>/dev/null
sf data360 query describe -o myorg --table ssot__Individual__dlm 2>/dev/null
sf data360 search-index list -o myorg 2>/dev/null
sf data360 query vector -o myorg --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null
sf data360 query hybrid -o myorg --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null

Example payloads

examples/search-indexes/vector-knowledge.json
examples/search-indexes/hybrid-structured.json

References

SKILL.md
../data360-orchestrate/assets/definitions/search-index.template.json
CREDITS.md

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Forks & variants (1)

Generating Validation Rule has 1 known copy in the catalog totaling 1.5k installs. They canonicalize to this original listing.

forcedotcom - 1.5k installs

How it compares

Use generating-validation-rule for save-time formula blocks; use Flow or Apex skills when validation requires multi-step automation beyond a single rule formula.

FAQ

Which SQL command for large exports?

Use async-create for large results rather than sync sql with heavy OFFSET paging.

Can describe probe any tenant?

No. Run describe only with a known DMO or DLO after broader retrieve readiness passes.

Where get search-index JSON?

Reuse examples search-indexes vector-knowledge.json and hybrid-structured.json templates.

Is Generating Validation Rule safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

Data Science & MLanalyticspipelines

About

Generating Validation Rule by the numbers

generating-validation-rule capabilities & compatibility

What generating-validation-rule says it does

Add your badge

How do I retrieve Data Cloud rows, run semantic search, and verify schema on a Data Cloud org?

Who is it for?

When should I use this skill?

What you get

By the numbers

Files

data360-query: Data Cloud Retrieve Phase

When This Skill Owns the Task

Required Context to Gather First

Core Operating Rules

Recommended Workflow

1. Classify readiness for retrieve work

2. Choose the smallest correct query shape

3. Use describe before guessing fields

4. Use vector or hybrid search only when an index exists

5. Reuse curated search-index examples when creating indexes

High-Signal Gotchas

Output Format

References

Credits & Acknowledgments

data360-query

Use this skill for

Example requests

Common commands

Example payloads

References

Related skills

Forks & variants (1)

How it compares

FAQ

Which SQL command for large exports?

Can describe probe any tenant?

Where get search-index JSON?

Is Generating Validation Rule safe to install?

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