jaganpro/sf-skills · Archived

sf-ai-agentforce-testing

Agentforce agent testing with dual-track workflow and 100-point scoring. TRIGGER when: user tests Agentforce agents, runs sf agent test commands, creates test specs, validates topic routing, or analyzes agent test coverage. DO NOT TRIGGER when: Apex unit tests (use sf-testing), building agents (use sf-ai-agentforce), or Agent Script DSL (use sf-ai-agentscript).

All-time #8356 First seen Jan 22, 2026
8-week activity · all time api

Installation

$ npx skills add jaganpro/sf-skills --skill sf-ai-agentforce-testing

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
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Repository health

Stars 423
License LICENSE
Default branch main
Open issues 1
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Version2.1.0
LicenseMIT
CompatibilityRequires API v66.0+ (Spring '26) and Agentforce enabled org
Declared agents claude-code
More metadata
version
2.1.0
author
Jag Valaiyapathy
scoring
100 points across 7 categories

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 8,835 B
  • docs README.md 4,638 B
  • docs SUMMARY.md 394 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,300 installs

SKILL.md

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](../sf-ai-agentforce/SKILL.md) or [sf-ai-agentscript](../sf-ai-agentscript/SKILL.md)
  • running Apex unit tests → [sf-testing](../sf-testing/SKILL.md)
  • creating seed data for actions → [sf-data](../sf-data/SKILL.md)
  • analyzing session telemetry / STDM traces → [sf-ai-agentforce-observability](../sf-ai-agentforce-observability/SKILL.md)

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.
  • Interactive and programmatic CLI preview use standard sf org login web authentication; ECA is only required for Agent Runtime API testing, not for live preview.
  • Fixes to the agent should be delegated to [sf-ai-agentscript](../sf-ai-agentscript/SKILL.md) 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.


<a id="phase-0-prerequisites--agent-discovery"></a>

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:

  1. discover the agent
  2. confirm publish / activation state
  3. verify dependencies (Flows, Apex, data)
  4. 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](../sf-ai-agentscript/SKILL.md)
  • 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:

  1. Test track used
  2. What was executed
  3. Pass/fail summary
  4. Coverage gaps
  5. Root-cause themes
  6. 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](../sf-ai-agentscript/SKILL.md) authoring and deterministic fix loops
create test data [sf-data](../sf-data/SKILL.md) action-ready data setup
fix Flow-backed actions [sf-flow](../sf-flow/SKILL.md) Flow repair
fix Apex-backed actions [sf-apex](../sf-apex/SKILL.md) Apex repair
set up ECA / OAuth for Agent Runtime API [sf-connected-apps](../sf-connected-apps/SKILL.md) auth and app configuration
analyze session telemetry [sf-ai-agentforce-observability](../sf-ai-agentforce-observability/SKILL.md) STDM / trace analysis

Reference Map

Start here

  • [references/interview-wizard.md](references/interview-wizard.md)
  • [references/multi-turn-testing.md](references/multi-turn-testing.md)
  • [references/cli-commands.md](references/cli-commands.md)
  • [references/test-spec-reference.md](references/test-spec-reference.md)

Execution / auth

  • [references/execution-protocol.md](references/execution-protocol.md)
  • [references/multi-turn-execution.md](references/multi-turn-execution.md)
  • [references/eca-setup-guide.md](references/eca-setup-guide.md)
  • [references/credential-convention.md](references/credential-convention.md)
  • [references/connected-app-setup.md](references/connected-app-setup.md)

Coverage / fix loops

  • [references/coverage-analysis.md](references/coverage-analysis.md)
  • [references/agentic-fix-loops.md](references/agentic-fix-loops.md)
  • [references/results-scoring.md](references/results-scoring.md)
  • [references/known-issues.md](references/known-issues.md)

Advanced / specialized

  • [references/agentscript-agents.md](references/agentscript-agents.md)
  • [references/agentscript-testing-patterns.md](references/agentscript-testing-patterns.md)
  • [references/cli-testing-details.md](references/cli-testing-details.md)
  • [references/deep-conversation-history-patterns.md](references/deep-conversation-history-patterns.md)
  • [references/swarm-execution.md](references/swarm-execution.md)
  • [references/trace-analysis.md](references/trace-analysis.md)
  • [references/agent-api-reference.md](references/agent-api-reference.md)

Templates / assets

  • [references/test-templates.md](references/test-templates.md)
  • [references/test-plan-format.md](references/test-plan-format.md)
  • [assets/](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