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Agent skill

Sf AI Agentforce Testing

by Jaganpro in Jaganpro/sf-skills

Agentforce agent testing with dual-track workflow and 100-point scoring.

MITAuto-check passedTesting & QA

Install Sf AI Agentforce Testing

skills CLI
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sf-ai-agentforce-testing .claude/skills/sf-ai-agentforce-testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sf-ai-agentforce-testing
GitHub stars
424
Token cost
~2.2k tokens
SKILL.md length
761 words
Files
61 (incl. references, assets)
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

Agentforce agent testing with dual-track workflow and 100-point scoring.

  • Works in 5 steps: Discover and verify → Plan tests → Execute the right track → …
  • : user tests Agentforce agents
  • SKILL.md covers When This Skill Owns the Task, Core Operating Rules, Required Context to Gather First and Dual-Track Workflow, plus 6 more sections
  • Calls sf

What it does

Sf AI Agentforce Testing is an agent skill from Jaganpro/sf-skills. 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).

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 61 other files, including reference files and assets (for example `CREDITS.md`, `README.md` and `assets/agentscript-test-spec.yaml`). Compatibility notes: Requires API v66.0+ (Spring '26) and Agentforce enabled org

It sits in Testing & QA, covering Agent evaluation and testing, Unit testing and Building AI agents. The repository describes itself as: [ARCHIVED — migrated to forcedotcom/afv-library] Salesforce Skills for Agentic Coding Tools — Apex, Flow, LWC, SOQL, Agentforce, Data Cloud, OmniStudio. Read-only archive; active… The licence is MIT.

When your agent uses it

  • : user tests Agentforce agents
  • Runs sf agent test commands
  • Creates test specs
  • Validates topic routing

Example prompts

  • “/sf-ai-agentforce-testing”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires API v66.0+ (Spring '26) and Agentforce enabled org

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Discover and verify
  2. Plan tests
  3. Execute the right track
  4. Classify failures
  5. Run fix loop

What it can do on your machine

Read from SKILL.md and the folder at commit 53c9956. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • sf

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires API v66.0+ (Spring '26) and Agentforce enabled org

    From compatibility in the SKILL.md frontmatter.

Context cost

Sf AI Agentforce Testing loads about 2.2k tokens when it runs, and up to ~66k if it reads all its reference files. Until then it costs about 97 tokens; SKILL.md has 761 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~66k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Jaganpro/sf-skills at commit 53c9956, republished under its MIT licence (© Jaganpro). 761 words, ~2,205 tokens.

Download SKILL.mdSave it as .claude/skills/sf-ai-agentforce-testing/SKILL.md (or your agent's skills folder). This skill also uses 60 other files; get the full folder from GitHub.
name
sf-ai-agentforce-testing
description
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).
compatibility
Requires API v66.0+ (Spring '26) and Agentforce enabled org
license
MIT
metadata.version
2.1.0
metadata.author
Jag Valaiyapathy
metadata.scoring
100 points across 7 categories

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:

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 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.

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
Show full SKILL.md (296 more words)Show less
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:

  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:

text
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

NeedDelegate toReason
fix Agent Script logicsf-ai-agentscriptauthoring and deterministic fix loops
create test datasf-dataaction-ready data setup
fix Flow-backed actionssf-flowFlow repair
fix Apex-backed actionssf-apexApex repair
set up ECA / OAuth for Agent Runtime APIsf-connected-appsauth and app configuration
analyze session telemetrysf-ai-agentforce-observabilitySTDM / trace analysis

Reference Map

Start here
Execution / auth
Coverage / fix loops
Advanced / specialized
Templates / assets

Score Guide

ScoreMeaning
90+production-ready test confidence
80–89strong coverage with minor gaps
70–79acceptable but coverage expansion recommended
60–69partial validation only
< 60insufficient confidence; block release

© Jaganpro, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 60 other files (references, assets) in skills/sf-ai-agentforce-testing of Jaganpro/sf-skills.

  • SKILL.md
  • CREDITS.md
  • LICENSE
  • README.md
  • assets/agentscript-test-spec.yaml
  • assets/basic-test-spec.yaml
  • assets/cli-auth-guardrail-tests.yaml
  • assets/cli-deep-history-tests.yaml
  • assets/comprehensive-test-spec.yaml
  • assets/context-vars-test-spec.yaml
  • assets/custom-eval-test-spec.yaml
  • assets/escalation-tests.yaml
  • assets/guardrail-tests.yaml
  • assets/multi-turn-agentscript-comprehensive.yaml
  • assets/multi-turn-comprehensive.yaml
  • assets/multi-turn-context-preservation.yaml
  • assets/multi-turn-escalation-flows.yaml
  • assets/multi-turn-topic-routing.yaml
  • assets/standard-test-spec.yaml
  • assets/test-plan-template.yaml
  • … and 41 more

Open the folder on GitHubat commit 53c9956

Compare with similar skills

Sf AI Agentforce Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Sf AI Agentforce Testing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sf AI Agentforce Testing this skillJaganpro/sf-skills424—~2.2kAutomated safety check: PassMIT
Testing Livekit Agentslivekit-examples/agent-starter-python2641 repos~1.9kAutomated safety check: PassMIT
Platform Apex Test Runforcedotcom/sf-skills1.1k—~1.9kAutomated safety check: PassApache-2.0
Ag2 Testingag2ai/build-with-ag2252—~1.3kAutomated safety check: PassApache-2.0
Langchain Local Dev Loopjeremylongshore/tons-of-skills-marketplace2.8k—~4.1kAutomated safety check: PassMIT
Adk Agent Buildergoogle/adk-python22k—~879Automated safety check: PassApache-2.0

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Categories

Questions about Sf AI Agentforce Testing

What does Sf AI Agentforce Testing do?

Agentforce agent testing with dual-track workflow and 100-point scoring. Sf AI Agentforce Testing is an agent skill from Jaganpro/sf-skills. Agentforce agent testing with dual-track workflow and 100-point scoring.

When should I use Sf AI Agentforce Testing?

Sf AI Agentforce Testing fits situations like: : user tests Agentforce agents; runs sf agent test commands; creates test specs; validates topic routing.

How do I install Sf AI Agentforce Testing in Claude Code?

Run `npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-testing -a claude-code`. Or copy the skill folder (skills/sf-ai-agentforce-testing in Jaganpro/sf-skills) into .claude/skills/sf-ai-agentforce-testing in your project. Claude Code loads it when a task matches its description.

How do I install Sf AI Agentforce Testing in Codex?

Run `npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-testing -a codex`. Or copy the skill folder (skills/sf-ai-agentforce-testing in Jaganpro/sf-skills) into .agents/skills/sf-ai-agentforce-testing in your project. Codex loads it when a task matches its description.

Can I use Sf AI Agentforce Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sf-ai-agentforce-testing, .gemini/skills/sf-ai-agentforce-testing, .github/skills/sf-ai-agentforce-testing and .opencode/skills/sf-ai-agentforce-testing in your project.

What does Sf AI Agentforce Testing need to run?

Going by SKILL.md and its folder, Sf AI Agentforce Testing needs the command-line tools its instructions call (sf). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires API v66.0+ (Spring '26) and Agentforce enabled org.

Does Sf AI Agentforce Testing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Sf AI Agentforce Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Sf AI Agentforce Testing use?

Sf AI Agentforce Testing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sf AI Agentforce Testing use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 64k tokens, read only when the agent opens those files.

What are the alternatives to Sf AI Agentforce Testing?

Skills that share tags, products or a category with Sf AI Agentforce Testing: Testing Livekit Agents (livekit-examples/agent-starter-python, 264 stars), Platform Apex Test Run (forcedotcom/sf-skills, 1.1k stars), Ag2 Testing (ag2ai/build-with-ag2, 252 stars) and Langchain Local Dev Loop (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sf AI Agentforce Testing?

Jaganpro (a GitHub user) maintains it in Jaganpro/sf-skills, which has 424 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on April 27, 2026.

Source: Jaganpro/sf-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.