Use this skill when the user needs formal Agentforce testing: multi-turn conversation validation, CLI Testing Center specs, topic/action coverage analysis, preview checks, or a structured test-fix loop after publish.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsf-ai-agentforce-testingExecute the skills CLI command in your project's root directory to begin installation:
Fetches sf-ai-agentforce-testing 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-testing. Access via /sf-ai-agentforce-testing 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
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Save 3-5 hours per week on routine tasks
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Accelerate learning and skill development by 2x
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Use this skill when the user needs formal Agentforce testing: multi-turn conversation validation, CLI Testing Center specs, topic/action coverage analysis, preview checks, or a structured test-fix loop after publish.
Use sf-ai-agentforce-testing when the work involves:
sf agent test workflowsDelegate elsewhere when the user is:
curl for OAuth token validation in the ECA flow; use the provided credential tooling.Use the existing scripts under:
~/.claude/skills/sf-ai-agentforce-testing/hooks/scripts/These scripts are pre-approved. Do not recreate them.
Ask for or infer:
Preflight checks:
Use when you need:
Requires:
Use when you need:
sf agent test workflowsFor manual validation without full formal testing, use preview workflows first, then escalate to Track A or B as needed.
Cover at least:
sf agent test commandsTypical failure buckets:
When failures imply agent-authoring issues:
Never skip these:
Avoid these anti-patterns:
curl commandsWhen finishing a run, report in this order:
Suggested shape:
Agent: <name>
Track: Multi-turn API | CLI Testing Center | Preview
Executed: <specs / scenarios / turns>
Result: <passed / partial / failed>
Coverage: <topics, actions, guardrails, context>
Issues: <highest-signal failures>
Next step: <fix, republish, rerun, or expand coverage>
| Need | Delegate to | Reason |
|---|---|---|
| fix Agent Script logic | sf-ai-agentscript | authoring and deterministic fix loops |
| create test data | sf-data | action-ready data setup |
| fix Flow-backed actions | sf-flow | Flow repair |
| fix Apex-backed actions | sf-apex | Apex repair |
| set up ECA / OAuth | sf-connected-apps | auth and app configuration |
| analyze session telemetry | sf-ai-agentforce-observability | STDM / trace analysis |
| Score | Meaning |
|---|---|
| 90+ | production-ready test confidence |
| 80–89 | strong coverage with minor gaps |
| 70–79 | acceptable but coverage expansion recommended |
| 60–69 | partial validation only |
| < 60 | insufficient confidence; block release |
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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Keeps context tight: sf-ai-agentforce-testing is the kind of skill you can hand to a new teammate without a long onboarding doc.
sf-ai-agentforce-testing has been reliable in day-to-day use. Documentation quality is above average for community skills.
sf-ai-agentforce-testing reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added sf-ai-agentforce-testing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added sf-ai-agentforce-testing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in sf-ai-agentforce-testing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
sf-ai-agentforce-testing reduced setup friction for our internal harness; good balance of opinion and flexibility.
sf-ai-agentforce-testing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: sf-ai-agentforce-testing is focused, and the summary matches what you get after install.
Keeps context tight: sf-ai-agentforce-testing is the kind of skill you can hand to a new teammate without a long onboarding doc.
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