Use this skill for the Setup UI / Agent Builder path: declarative topics, Builder-managed actions, GenAiFunction / GenAiPlugin metadata, Prompt Builder templates stored as GenAiPromptTemplate metadata, Models API usage from Apex, and custom Lightning types.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsf-ai-agentforceExecute the skills CLI command in your project's root directory to begin installation:
Fetches sf-ai-agentforce from jaganpro/sf-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sf-ai-agentforce. Access via /sf-ai-agentforce in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Use this skill for the Setup UI / Agent Builder path: declarative topics, Builder-managed actions, GenAiFunction / GenAiPlugin metadata, Prompt Builder templates stored as GenAiPromptTemplate metadata, Models API usage from Apex, and custom Lightning types.
For new code-first agent development, prefer sf-ai-agentscript.
If the work produces or edits a
.agentfile — including Builder Script / Canvas work that results in an authoring bundle — use sf-ai-agentscript.
Use sf-ai-agentforce when the user is:
GenAiFunction, GenAiPlugin, or GenAiPromptTemplate metadataDo not use it for:
.agent files or deterministic FSM design → sf-ai-agentscriptAsk for or infer:
.agent authoring bundle| Path | Skill | Best fit |
|---|---|---|
| Builder metadata path | sf-ai-agentforce |
Declarative maintenance, existing Builder agents, metadata-driven action registration |
| Agent Script authoring bundle path | sf-ai-agentscript |
Code-first .agent authoring, deterministic routing, version-controlled agent logic |
If the user is starting from scratch and wants strong control over flow/state, route to Agent Script.
sf org create agent-user)<agentAccesses>Expanded workflow: references/builder-workflow.md
Topic descriptions are routing instructions for the planner. They must be:
| Target type | Typical use | Registered via |
|---|---|---|
| Flow | safest default for Builder actions | GenAiFunction |
| Apex | complex business logic via @InvocableMethod |
GenAiFunction |
| Prompt Builder template | generated summaries / drafts / recommendations | GenAiFunction |
GenAiPromptTemplate is the current Metadata API type for source-driven template work.genAiPromptTemplates/*.genAiPromptTemplate-meta.xml.{!$Input:TargetRecord} or {!$Input:AdditionalContext}.Before publishing the agent itself, deploy the supporting stack:
GenAiPromptTemplate / GenAiFunction / GenAiPluginFor Service Agents, prefer the native GA command:
sf org create agent-user --target-org <alias> --json
Use the returned username in the running-user configuration.
For Employee Agents, ensure end users receive a Permission Set containing <agentAccesses>. Without this, the agent can be active but still invisible in Lightning Experience.
See ../sf-permissions/references/agent-access-guide.md.
After publish, run sf agent activate separately.
For automation, prefer sf agent activate --api-name <AgentName> --version <n> --target-org <alias> --json so the rollout is deterministic and machine-readable.
Use when registering a single callable action. Validate:
Use when grouping related functions into one logical package.
Use for generated content, not deterministic business rules.
Prefer the current metadata shape:
GenAiPromptTemplategenAiPromptTemplates/.genAiPromptTemplate-meta.xmltemplateVersionsUse when the solution belongs in Apex-driven AI orchestration rather than Builder-only actions.
Use when the action needs richer structured input or output presentation.
Expanded references:
sf-metadata → sf-apex → sf-flow → sf-ai-agentforce → sf-deploy
| Requirement | Delegate to | Why |
|---|---|---|
| Create / fix Flows | sf-flow | Action target creation and Flow validation |
| Create / fix Apex actions | sf-apex | @InvocableMethod and Apex correctness |
| Deploy / publish | sf-deploy | Deployment orchestration |
| Test the agent | sf-ai-agentforce-testing | Formal test execution and assertions |
| Employee Agent visibility / access | sf-permissions | Permission Set <agentAccesses> setup |
| Symptom | Likely cause | Read next |
|---|---|---|
| Action not available in Builder | target metadata missing or not deployed | references/metadata-reference.md |
| Prompt action fails during publish or activation | template is Draft, missing inputs, or old metadata shape is being used | references/genaiprompttemplate.md |
| Need more than 5 template inputs | flex template input limit hit | references/genaiprompttemplate.md |
| Apex AI logic times out | Models API work placed in the wrong context | references/models-api.md |
| Rich input/output UI not rendering | Lightning type config or prerequisites are incomplete | references/custom-lightning-types.md |
| Agent publishes but is not usable | forgot explicit activation | references/cli-commands.md |
| Service Agent publish/runtime failure | missing or invalid running user | ../sf-ai-agentscript/references/agent-user-setup.md |
| Employee Agent active but not visible to users | missing <agentAccesses> permission set |
../sf-permissions/references/agent-access-guide.md |
| Score | Meaning |
|---|---|
| 90+ | Ready to deploy |
| 80–89 | Strong, minor cleanup only |
| 70–79 | Review before deploy |
| 60–69 | Needs work |
| < 60 | Block deployment |
Full rubric: references/scoring-rubric.md
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Registry listing for sf-ai-agentforce matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend sf-ai-agentforce for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
sf-ai-agentforce fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
sf-ai-agentforce reduced setup friction for our internal harness; good balance of opinion and flexibility.
sf-ai-agentforce is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in sf-ai-agentforce — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for sf-ai-agentforce matched our evaluation — installs cleanly and behaves as described in the markdown.
We added sf-ai-agentforce from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in sf-ai-agentforce — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
sf-ai-agentforce reduced setup friction for our internal harness; good balance of opinion and flexibility.
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