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sf-ai-agentforce-testing: Agentforce Test Execution & Coverage Analysis
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.
When This Skill Owns the Task
Use sf-ai-agentforce-testing when the work involves:
sf agent test workflows- multi-turn Agent Runtime API testing
- topic routing, action invocation, context preservation, guardrail, or escalation validation
- test-spec generation and coverage analysis
- post-publish / post-activate test-fix loops
Delegate elsewhere when the user is:
- building or editing the agent itself → sf-ai-agentforce or sf-ai-agentscript
- running Apex unit tests → sf-testing
- creating seed data for actions → sf-data
- analyzing session telemetry / STDM traces → sf-ai-agentforce-observability
Core Operating Rules
- Testing comes after deploy / publish / activate.
- Use multi-turn API testing as the primary path when conversation continuity matters.
- Use CLI Testing Center as the secondary path for single-utterance and org-supported test-center workflows.
- Fixes to the agent should be delegated to sf-ai-agentscript when Agent Script changes are needed.
- Do not use raw
curl for OAuth token validation in the ECA flow; use the provided credential tooling.
Script path rule
Use the existing scripts under:
~/.claude/skills/sf-ai-agentforce-testing/hooks/scripts/
These scripts are pre-approved. Do not recreate them.
Required Context to Gather First
Ask for or infer:
- agent API name / developer name
- target org alias
- testing goal: smoke test, regression, coverage expansion, or bug reproduction
- whether the agent is already published and activated
- whether the org has Agent Testing Center available
- whether ECA credentials are available for Agent Runtime API testing
Preflight checks:
- discover the agent
- confirm publish / activation state
- verify dependencies (Flows, Apex, data)
- choose testing track
Dual-Track Workflow
Track A — Multi-turn API testing (primary)
Use when you need:
- multi-turn conversation testing
- topic re-matching validation
- context preservation checks
- escalation or action-chain analysis across turns
Requires:
- ECA / auth setup
- agent runtime access
Track B — CLI Testing Center (secondary)
Use when you need:
- org-native
sf agent test workflows
- test spec YAML execution
- quick single-utterance validation
- CLI-centered CI/CD usage where Testing Center is available
Quick manual path
For manual validation without full formal testing, use preview workflows first, then escalate to Track A or B as needed.
Recommended Workflow
1. Discover and verify
- locate the agent in the target org
- confirm it is published and activated
- confirm required actions / Flows / Apex exist
- decide whether Track A or Track B fits the request
2. Plan tests
Cover at least:
- main topics
- expected actions
- guardrails / off-topic handling
- escalation behavior
- phrasing variation
3. Execute the right track
Track A
- validate ECA credentials with the provided tooling
- retrieve metadata needed for scenario generation
- run multi-turn scenarios with the provided Python scripts
- analyze per-turn failures and coverage
Track B
- generate or refine a flat YAML test spec
- run
sf agent test commands
- inspect structured results and verbose action output
4. Classify failures
Typical failure buckets:
- topic not matched
- wrong topic matched
- action not invoked
- wrong action selected
- action invocation failed
- context preservation failure
- guardrail failure
- escalation failure
5. Run fix loop
When failures imply agent-authoring issues:
- delegate fixes to sf-ai-agentscript
- re-publish / re-activate if needed
- re-run focused tests before full regression
Testing Guardrails
Never skip these:
- test only after publish/activate
- include harmful / off-topic / refusal scenarios
- use multiple phrasings per important topic
- clean up sessions after API tests
- keep swarm execution small and controlled
Avoid these anti-patterns:
- testing unpublished agents
- treating one happy-path utterance as coverage
- storing ECA secrets in repo files
- debugging auth with brittle shell-expanded
curl commands
- changing both tests and agent simultaneously without isolating the cause
Output Format
When finishing a run, report in this order:
- Test track used
- What was executed
- Pass/fail summary
- Coverage gaps
- Root-cause themes
- Recommended fix loop / next test step
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>
Cross-Skill Integration
| 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 | |
Reference Map
Start here
Execution / auth
Coverage / fix loops
Advanced / specialized
Templates / assets
Score Guide
| 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 |
Weekly Installs
240
Repository
jaganpro/sf-skills
GitHub Stars
223
First Seen
Jan 22, 2026
Security Audits
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Installed on
cursor232
codex232
gemini-cli230
opencode230
github-copilot227
amp224
