sf-ai-agentforce_skill

This skill helps you configure and manage UI-driven Agentforce agents with topics, actions, and prompts for declarative development.
  • Python

67

GitHub Stars

4

Bundled Files

3 weeks ago

Catalog Refreshed

2 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstart where the catalogue uses aiagentskills.

npx veilstart add skill jaganpro/sf-skills --skill sf-ai-agentforce

  • CREDITS.md936 B
  • LICENSE1.1 KB
  • README.md2.1 KB
  • SKILL.md19.1 KB

Overview

This skill describes the standard Salesforce Agentforce platform workflow for building agents using the Setup UI (Agentforce Builder). It covers topic and action configuration, GenAiFunction/GenAiPlugin metadata, PromptTemplate authoring, Einstein Models API usage from Apex, and custom Lightning types for rich inputs/outputs. Use this when you prefer declarative, point-and-click agent development rather than code-first Agent Script DSL.

How this skill works

Configure agents in Setup → Agentforce → Agents by creating topics, writing instructions, and adding actions that map to Flows, Apex InvocableMethods, or PromptTemplates. Register each executable as a GenAiFunction, optionally group functions into a GenAiPlugin, then publish the agent so the Agentforce runtime can plan and invoke actions. For custom AI logic, call Einstein Models API from Apex; for rich UI inputs/outputs, create LightningTypeBundle bundles.

When to use it

  • Building agents via the Salesforce Setup UI without authoring .agent files
  • Mapping Autolaunched Flows or @InvocableMethod Apex to agent actions
  • Reusing prompts across agents and Flows with PromptTemplate metadata
  • Creating rich input forms or formatted outputs with custom Lightning types
  • Registering grouped actions using GenAiPlugin for organization

Best practices

  • Deploy Flow/Apex/Prompt targets first, then deploy GenAiFunction, then GenAiPlugin, then publish the agent
  • Write explicit, scoped topic descriptions and instructions to guide the LLM planner
  • Define required inputs and mark displayable outputs so the agent collects slots cleanly
  • Use PromptTemplate variable names exactly (case-sensitive) when binding fields
  • Use Einstein Models API in async jobs (Queueable/Batch) for heavy or bulk tasks

Example use cases

  • Service agent that looks up order status: Flow target, GenAiFunction wrapper, topic routed by planner
  • Employee agent that runs a payroll validation Apex @InvocableMethod via GenAiFunction
  • Knowledge agent that generates personalized replies using a PromptTemplate invoked by an action
  • Order management plugin grouping functions: lookup, cancel, and return as a GenAiPlugin
  • Custom product detail input form using LightningTypeBundle editor and renderer

FAQ

Always deploy the underlying Flow/Apex/PromptTemplate first. GenAiFunction references must point to active targets.

When should I use Einstein Models API instead of a PromptTemplate?

Use the Models API for custom, programmatic LLM calls from Apex (async patterns, batching, or advanced orchestration). Use PromptTemplates for reusable authoring inside the platform and Flows.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational