Salesforce Data Cloud Segment phase. TRIGGER when: user creates or publishes segments, manages calculated insights, inspects segment counts or membership, or troubleshoots audience SQL in Data Cloud. DO NOT TRIGGER when: the task is DMO/mapping/identity-resolution work (use sf-datacloud-harmonize), activation work (use sf-datacloud-act), query/search-index work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Install
npx skills add https://github.com/jaganpro/sf-skills --skill sf-datacloud-segmentSKILL.md
sf-datacloud-segment: Data Cloud Segment Phase
Use this skill when the user needs audience and insight work: segments, calculated insights, publish workflows, member counts, or troubleshooting Data Cloud segment SQL.
When This Skill Owns the Task
Use sf-datacloud-segment when the work involves:
sf data360 segment *sf data360 calculated-insight *- segment publish workflows
- member counts and segment troubleshooting
- calculated insight execution and verification
Delegate elsewhere when the user is:
- still building DMOs, mappings, or identity resolution → sf-datacloud-harmonize
- activating a segment downstream → sf-datacloud-act
- writing read-only SQL or search-index queries → sf-datacloud-retrieve
Required Context to Gather First
Ask for or infer:
- target org alias
- unified DMO or base entity name
- whether the user wants create, publish, inspect, or troubleshoot
- whether the asset is a segment or calculated insight
- expected success metric: member count, aggregate value, or publish status
Core Operating Rules
- Treat Data Cloud segment SQL as distinct from CRM SOQL.
- Run the shared readiness classifier before mutating audience assets:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase segment --json. - Prefer reusable JSON definitions for repeatable segment and CI creation.
- Use
--api-version 64.0when segment creation behavior is unstable on newer defaults. - Verify with counts or SQL after publish/run steps instead of assuming success.
- Use SQL joins rather than
segment memberswhen readable member details are needed.
Recommended Workflow
1. Classify readiness for segment work
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase segment --json
2. Inspect current state
sf data360 segment list -o <org> 2>/dev/null
sf data360 calculated-insight list -o <org> 2>/dev/null
3. Create with reusable JSON definitions
sf data360 segment create -o <org> -f segment.json --api-version 64.0 2>/dev/null
sf data360 calculated-insight create -o <org> -f ci.json 2>/dev/null
4. Publish or run explicitly
sf data360 segment publish -o <org> --name My_Segment 2>/dev/null
sf data360 calculated-insight run -o <org> --name Lifetime_Value 2>/dev/null
5. Verify with counts or SQL
sf data360 segment count -o <org> --name My_Segment 2>/dev/null
sf data360 query sql -o <org> --sql 'SELECT COUNT(*) FROM "UnifiedssotIndividualMain__dlm"' 2>/dev/null
High-Signal Gotchas
- Segment creation can require
--api-version 64.0. segment membersreturns opaque IDs; use SQL joins when human-readable member details are needed.- Segment SQL is not SOQL.
- Calculated insight assets and segment SQL have different limitations.
- Publish/run steps may kick off asynchronous work even when the command returns quickly.
- An empty segment or calculated-insight list usually means the module is reachable but unconfigured, not unavailable.
Output Format
Segment task: <segment / calculated-insight>
Action: <create / publish / inspect / troubleshoot>
Target org: <alias>
Artifacts: <definition files / commands>
Verification: <member count / query result / publish state>
Next step: <act / retrieve / follow-up>
References
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