salesforce-einstein
Use for Salesforce Einstein work — Discovery, Predictions, Next Best Action, Einstein Bots (legacy → migrating to Agentforce), prompt builder, model management.
What this skill does
# salesforce-einstein Salesforce Einstein platform. Distinct from Agentforce (which subsumes some Einstein features). Use for: predictions, classifications, recommendations, prompt-builder workflows. ## Surfaces - **Einstein Discovery** — AutoML on CRM data; predictions + model explainability - **Einstein Prediction Builder** — clicks-not-code prediction model - **Einstein Next Best Action** — recommendation strategy + flows - **Prompt Builder** — prompt templates for AI features - **Einstein Bots** — legacy chatbots; new builds → Agentforce - **Model deployment** — Connect API for custom models, Trust Layer integration ## Method (typical workflows) ### Discovery model 1. Identify the **target field** (what to predict). 2. Identify the **comparison data** (positive vs negative outcomes). 3. Configure **dataset** — fields to include, exclusions for bias. 4. Train. Inspect **model fit** (R², accuracy, lift). 5. Deploy — embed in Lightning page or Flow. 6. **Monitor drift** — schedule re-train or alert on accuracy drop. ### Prompt template 1. **Type** — Field Generation / Email Generation / Sales Email / Flex / Record Summary. 2. **Resources** — fields, related lists, files, Apex methods, Flows. 3. **Instructions** — natural language; avoid contradicting the resource shape. 4. **Test** — Prompt Builder preview; eval against sample records. 5. **Surface** — embed in record page action, Flow, or via Apex. ## Output shape ``` Workflow: <Discovery | Prediction Builder | NBA | Prompt | Bot> Inputs: Object: <SObject> Dataset: <fields, filters> Configuration: <key params> Quality gates: Model fit: <metric, threshold> Bias check: <how> Trust Layer: <PII handling> Deploy: Surface: <Lightning page | Flow | Apex | Bot> Test plan: <eval cases> Monitoring: Metric: <accuracy, drift, latency> Alert: <threshold> ``` ## Rules - **Bias review is part of quality, not optional.** Exclude biased fields (race, age, gender unless compliance-required). - **Trust Layer applies to LLM-backed features** (prompts, generative responses). - **Capacity** — Einstein has compute limits per edition; check before adding heavy models. - **Migration path** — new chatbot work goes to Agentforce, not Einstein Bots. - **Eval cases captured to memory** for regression testing. ## Anti-patterns - Discovery model using PII as input without compliance signoff - Prompt template that ignores resources, hallucinates from imagined records - Deploying without monitoring (drift bites silently) - Building new Einstein Bot when org has Agentforce licence - Custom-model integration without Trust Layer ## When NOT to use - Non-Einstein-licenced org — features unavailable - Pure rule-based logic — use Flow / Apex - Agentforce work — `/siftcoder:salesforce-agentforce` ## Subagent dispatch - `salesforce-architect` for capacity + bias review - `Plan` for multi-step workflows (data prep + train + deploy) - `general-purpose` for the metadata generation ## Key references - Einstein Discovery: trailhead.salesforce.com (Einstein Discovery Basics) - Prompt Builder: help.salesforce.com → Prompt Builder - Einstein Trust Layer: help.salesforce.com (also referenced from agentforce skill) ## Value over native CC CC will discuss ML if asked. CC won't naturally know Einstein-specific shapes (Discovery model fit metrics, Prompt Builder template types, NBA strategy components). Platform-specific knowledge IS the value.
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