sf-ai-agentscript

jaganpro/sf-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/jaganpro/sf-skills --skill sf-ai-agentscript
0 commentsdiscussion
summary

Agent Script is the code-first path for deterministic Agentforce agents. Use this skill when the user is authoring .agent files, building finite-state topic flows, or needs repeatable control over routing, variables, actions, and publish behavior.

skill.md

SF-AI-AgentScript Skill

Agent Script is the code-first path for deterministic Agentforce agents. Use this skill when the user is authoring .agent files, building finite-state topic flows, or needs repeatable control over routing, variables, actions, and publish behavior.

Start with the shortest guide first: references/activation-checklist.md

Migrating from the Builder UI? Use references/migration-guide.md

When This Skill Owns the Task

Use sf-ai-agentscript when the work involves:

  • creating or editing .agent files
  • deterministic topic routing, guards, and transitions
  • Agent Script CLI workflows (sf agent generate authoring-bundle, sf agent validate authoring-bundle, sf agent preview, sf agent publish authoring-bundle, sf agent activate)
  • slot filling, instruction resolution, post-action loops, or FSM design

Delegate elsewhere when the user is:

If the user is in Builder Script / Canvas view but the outcome is a .agent authoring bundle, keep the work in sf-ai-agentscript.


Required Context to Gather First

Ask for or infer:

  • agent purpose and whether Agent Script is truly the right fit
  • Service Agent vs Employee Agent
  • target org and publish intent
  • expected actions / targets (Flow, Apex, PromptTemplate, etc.)
  • whether the request is authoring, validation, preview, or publish troubleshooting

Activation Checklist

Before you author or fix any .agent file, verify these first:

  1. Exactly one start_agent block
  2. No mixed tabs and spaces
  3. Booleans are True / False
  4. No else if and no nested if
  5. No top-level actions: block
  6. No @inputs in set expressions
  7. linked variables have no defaults
  8. linked variables do not use object / list types
  9. Use explicit agent_type
  10. Use @actions. prefixes consistently
  11. Use run @actions.X only when X is a topic-level action definition with target:
  12. Do not branch directly on raw @system_variables.user_input contains/startswith/endswith for intent routing
  13. On prompt-template outputs, prefer is_displayable: False + is_used_by_planner: True
  14. Do not assume @outputs.X is scalar — inspect the output schema before branching or assignment

For the expanded version, use references/activation-checklist.md.


Non-Negotiable Rules

1) Service Agent vs Employee Agent

Agent type Required Forbidden / caution
AgentforceServiceAgent Valid default_agent_user, correct permissions, target-org checks, prefer sf org create agent-user Publishing without a real Einstein Agent User
AgentforceEmployeeAgent Explicit agent_type Supplying default_agent_user

Full details: references/agent-user-setup.md

2) Recommended top-level block convention

Use this order for consistency in this skill's examples and reviews:

config:
variables:
system:
connection:
knowledge:
language:
start_agent:
topic:

Official Salesforce materials present top-level blocks in differing sequences, and local validation evidence indicates multiple orderings compile. Treat this as a style convention, not a standalone correctness or publish blocker.

3) Critical config fields

Field Rule
developer_name Must match folder / bundle name
description Public docs/examples should use this config field
agent_type Set explicitly every time
default_agent_user Service Agents only

Local tooling also accepts agent_description: for compatibility, but this skill's public docs and examples should prefer description:.

4) Syntax blockers you should treat as immediate failures

  • else if
  • nested if
  • comment-only if bodies
  • top-level actions:
  • invocation-level inputs: / outputs: blocks
  • reserved variable / field names like description and label

Canonical rule set: references/syntax-reference.md and references/validator-rule-catalog.md


Recommended Workflow

Recommended Authoring Workflow

Phase 1 — design the agent

  • decide whether the problem is actually deterministic enough for Agent Script
  • model topics as states and transitions as edges
  • define only the variables you truly need

Phase 2 — author the .agent

  • create config, system, start_agent, and topics first
  • add target-backed actions with full inputs: and outputs:
  • use available when for deterministic tool visibility
  • normalize raw intent/validation signals into booleans or enums before branching; avoid direct substring checks on raw user utterances for critical control flow
  • keep post-action checks at the top of instructions: ->

Default authoring stance

  • Default to direct .agent authoring and edits in source control.
  • Use sf agent generate authoring-bundle --no-spec only when the user wants local bundle scaffolding.
  • Treat sf agent generate agent-spec as optional ideation / topic bootstrap, not the default workflow.
  • Do not route Agent Script users toward sf agent create or sf agent generate template.

Phase 3 — validate continuously

Validation already runs automatically on write/edit. Use the CLI before publish:

sf agent validate authoring-bundle --api-name MyAgent -o TARGET_ORG --json

The validator covers structure, runtime gotchas, target readiness, and org-aware Service Agent checks. Rule IDs live in references/validator-rule-catalog.md.

Phase 4 — preview smoke test

Use the preview loop before publish:

  • derive 3–5 smoke utterances
  • start preview
  • inspect topic routing / action invocation / safety / grounding
  • fix and rerun up to 3 times

Full loop: references/preview-test-loop.md

Phase 5 — publish and activate

sf agent publish authoring-bundle --api-name MyAgent -o TARGET_ORG --json

# Manual activation
sf agent activate --api-name MyAgent -o TARGET_ORG

# CI / deterministic activation of a known BotVersion
sf agent activate --api-name MyAgent --version <n> -o TARGET_ORG --json

Publishing does not activate the agent. For automation, prefer --version <n> --json so activation is deterministic and machine-readable.


Deterministic Building Blocks

These execute as code, not suggestions:

  • conditionals
  • available when guards
  • variable checks
  • direct set / transition to
  • run @actions.X only when X is a topic-level action definition with target:
  • variable injection into LLM-facing text

Important distinction:

  • Deterministic: set, transition to, and run @actions.X for a target-backed topic action
  • LLM-directed: reasoning.actions: utilities / delegations such as @utils.setVariables, @utils.transition, and {[email protected]} instruction references

If you need deterministic behavior for something that is currently modeled as a reasoning-level utility, either:

  • rewrite it as direct set / transition to, or
  • promote it to a topic-level target-backed action and run that action

See references/instruction-resolution.md and references/architecture-patterns.md.


Cross-Skill Integration

Cross-Skill Orchestration

Task Delegate to Why
Build flow:// targets sf-flow Flow creation / validation
Build Apex action targets sf-apex @InvocableMethod and business logic
Test topic routing / actions sf-ai-agentforce-testing Formal test specs and fix loops
Deploy / publish sf-deploy Deployment orchestration

High-Signal Failure Patterns

Symptom Likely cause Read next
Internal Error during publish invalid Service Agent user or missing action I/O references/agent-user-setup.md, references/actions-reference.md
invalid input/output parameters on prompt template action Target template is in Draft status — activate it first references/action-prompt-templates.md
Parser rejects conditionals else if, nested if, empty if body references/syntax-reference.md
Action target issues missing Flow / Apex target, inactive Flow, bad schemas references/actions-reference.md
Prompt template runs but user sees blank response prompt output marked is_displayable: True references/production-gotchas.md, references/action-prompt-templates.md
Prompt action runs but planner behaves like output is missing output hidden from direct display but not planner-visible references/production-gotchas.md, references/actions-reference.md
ACTION_NOT_IN_SCOPE on run @actions.X run points at a utility / delegation / unresolved action instead of a topic-level target-backed definition references/syntax-reference.md, references/instruction-resolution.md
Deterministic cancel / revise / URL checks behave inconsistently raw @system_variables.user_input matching or string-method guards are being used as control-flow-critical validation references/syntax-reference.md, references/production-gotchas.md
@outputs.X comparisons or assignments behave unexpectedly the action output is structured/wrapped, not a plain scalar references/actions-reference.md, references/syntax-reference.md
Preview and runtime disagree linked vars / context / known platform issues references/known-issues.md
Validate passes but publish fails org-specific user / permission / retrieve-back issue references/production-gotchas.md, references/cli-guide.md

Reference Map

Start here

Publish / runtime safety

Architecture / reasoning

Validation / testing / debugging

Examples / scaffolds

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jaganpro/sf-skills --skill sf-ai-agentscript

The skills CLI fetches sf-ai-agentscript from GitHub repository jaganpro/sf-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/sf-ai-agentscript

Reload or restart Cursor to activate sf-ai-agentscript. Access the skill through slash commands (e.g., /sf-ai-agentscript) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ 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.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.538 reviews
  • Kofi Lopez· Dec 16, 2024

    Keeps context tight: sf-ai-agentscript is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Shikha Mishra· Dec 12, 2024

    Solid pick for teams standardizing on skills: sf-ai-agentscript is focused, and the summary matches what you get after install.

  • Ganesh Mohane· Dec 8, 2024

    Keeps context tight: sf-ai-agentscript is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Tariq Liu· Dec 8, 2024

    We added sf-ai-agentscript from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dev Kapoor· Nov 27, 2024

    Solid pick for teams standardizing on skills: sf-ai-agentscript is focused, and the summary matches what you get after install.

  • Yash Thakker· Nov 3, 2024

    We added sf-ai-agentscript from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dhruvi Jain· Oct 22, 2024

    sf-ai-agentscript fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dev Haddad· Oct 18, 2024

    sf-ai-agentscript has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Arya Sanchez· Sep 21, 2024

    sf-ai-agentscript reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Emma Lopez· Sep 13, 2024

    Registry listing for sf-ai-agentscript matched our evaluation — installs cleanly and behaves as described in the markdown.

showing 1-10 of 38

1 / 4