Use when designing or auditing a Salesforce data architecture to support AI features — Einstein, Agentforce, Data Cloud, or custom ML models. Covers field-level data quality requirements, structured vs unstructured data placement, embedding-ready text field design, knowledge article structure for RAG grounding, Data Cloud harmonized data model, Einstein Feature Store patterns, grounding data for Agentforce actions, AI-ready field naming conventions, and data freshness requirements for model retraining. Trigger phrases: design data model for AI, make Salesforce data AI-ready, data quality for Einstein, grounding data for Agentforce, Data Cloud for AI, RAG data architecture Salesforce. NOT for Data Cloud initial setup and licensing, not for LWC or Flow implementation, not for general data migration, not for Einstein Analytics dashboard design.
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryData & analyticsStars0
About this skill
Use when designing or auditing a Salesforce data architecture to support AI features — Einstein, Agentforce, Data Cloud, or custom ML models. Covers field-level data quality requirements, structured vs unstructured data placement, embedding-ready text field design, knowledge article structure for RAG grounding, Data Cloud harmonized data model, Einstein Feature Store patterns, grounding data for Agentforce actions, AI-ready field naming conventions, and data freshness requirements for model retraining. Trigger phrases: design data model for AI, make Salesforce data AI-ready, data quality for Einstein, grounding data for Agentforce, Data Cloud for AI, RAG data architecture Salesforce. NOT for Data Cloud initial setup and licensing, not for LWC or Flow implementation, not for general data migration, not for Einstein Analytics dashboard design.
Quick start
Copy the install prompt. Use the green button above this README.
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Use when designing or auditing a Salesforce data architecture to support AI features — Einstein, Agentforce, Data Cloud, or custom ML models. Covers field-level data quality requirements, structured vs unstructured data placement, embedding-ready text field design, knowledge article structure for RAG grounding, Data Cloud harmonized data model, Einstein Feature Store patterns, grounding data for Agentforce actions, AI-ready field naming conventions, and data freshness requirements for model retraining. Trigger phrases: design data model for AI, make Salesforce data AI-ready, data quality for Einstein, grounding data for Agentforce, Data Cloud for AI, RAG data architecture Salesforce. NOT for Data Cloud initial setup and licensing, not for LWC or Flow implementation, not for general data migration, not for Einstein Analytics dashboard design.
About this listing
Category
Data & analytics
Reported license
Apache-2.0
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI