sf-ai-agentforce-testing

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 sf-ai-agentforce-observability STDM / trace analysis

Reference Map

Start here

• references/interview-wizard.md

• references/multi-turn-testing.md

• references/cli-commands.md

• references/test-spec-reference.md

Execution / auth

• references/execution-protocol.md

• references/multi-turn-execution.md

• references/eca-setup-guide.md

• references/credential-convention.md

• references/connected-app-setup.md

Coverage / fix loops

• references/coverage-analysis.md

• references/agentic-fix-loops.md

• references/results-scoring.md

• references/known-issues.md

Advanced / specialized

• references/agentscript-agents.md

• references/agentscript-testing-patterns.md

• references/cli-testing-details.md

• references/deep-conversation-history-patterns.md

• references/swarm-execution.md

• references/trace-analysis.md

• references/agent-api-reference.md

Templates / assets

• references/test-templates.md

• references/test-plan-format.md

• 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

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