Agent skill

Service Itsm Agentic Setup Agentforce Studio Configure

by forcedotcom in forcedotcom/sf-skills

Enable the Agentforce for IT Service Salesforce Go feature toggles (Agentforce Studio, Einstein Generative AI, the parent umbrella, and the Fulfiller/Employee agent templates) using the Salesforce…

Apache-2.0Auto-check: notesSales & Support

Install Service Itsm Agentic Setup Agentforce Studio Configure

skills CLI
$ npx skills add forcedotcom/sf-skills --skill service-itsm-agentic-setup-agentforce-studio-configure -a claude-code

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

GitHub CLI
$ gh skill install forcedotcom/sf-skills service-itsm-agentic-setup-agentforce-studio-configure --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/forcedotcom/sf-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/service-itsm-agentic-setup-agentforce-studio-configure .claude/skills/service-itsm-agentic-setup-agentforce-studio-configure && 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
service-itsm-agentic-setup-agentforce-studio-configure
GitHub stars
1.1k
Token cost
~5.8k tokens
SKILL.md length
2,370 words
Files
5 (incl. scripts, references)
Skills in repo
251
Repo updated
First seen
Licence
Apache-2.0

At a glance

Enable the Agentforce for IT Service Salesforce Go feature toggles (Agentforce Studio, Einstein Generative AI, the parent umbrella, and the Fulfiller/Employee agent templates) using the Salesforce…

  • Works in 6 steps: Read Current State → Plan (helper script) → Confirm-to-Write Checkpoint (REQUIRED) → …
  • Asked to enable Agentforce Studio
  • SKILL.md covers Scope, Which path?, Preconditions and Operations at a glance, plus 7 more sections
  • Runs JavaScript scripts from its folder; calls sf and node

What it does

Service Itsm Agentic Setup Agentforce Studio Configure is an agent skill from forcedotcom/sf-skills. Enable the Agentforce for IT Service Salesforce Go feature toggles (Agentforce Studio, Einstein Generative AI, the parent umbrella, and the Fulfiller/Employee agent templates) using the Salesforce CLI (sf). Turns ON org prefs via the Setup Discovery feature/{apiName}/enable Connect API route. Write-capable, idempotent, dependency-ordered, confirm-to-write required. Use when asked to enable Agentforce Studio, turn on Agentforce for IT Service, enable Einstein generative AI, or configure the org-level Agentforce…

Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/cli-invocation.md`).

It sits in Sales & Support, covering CRM management. It works with Salesforce. The repository describes itself as: Salesforce's curated collection of agent skills for building applications. Optimized for Agentforce Vibes, compatible with all AI tools. The licence is Apache-2.0.

When your agent uses it

  • Asked to enable Agentforce Studio
  • Turn on Agentforce for IT Service
  • Enable Einstein generative AI
  • Configure the org-level Agentforce for IT Service prerequisites

Example prompts

  • “/service-itsm-agentic-setup-agentforce-studio-configure”

Requirements

  • Node.js
  • Pre-approved tools (allowed-tools): Bash, Read, AskUserQuestion

Workflow steps

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

  1. Read Current State
  2. Plan (helper script)
  3. Confirm-to-Write Checkpoint (REQUIRED)
  4. Enable (Dependencies First, Re-Checked Before Each Toggle)
  5. Verify Enablement
  6. Aggregate Verdict (helper script)

What it can do on your machine

Read from SKILL.md and the folder at commit e5164d9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • AskUserQuestion

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • sf
    • node

    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.

Context cost

Service Itsm Agentic Setup Agentforce Studio Configure loads about 5.8k tokens when it runs, and up to ~9.6k if it reads all its reference files. Until then it costs about 237 tokens; SKILL.md has 2,370 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~237
When it runs · the whole SKILL.md, loaded when a task matches
~5.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.6k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, AskUserQuestion, 

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); the scripts in this folder are not scanned.

SKILL.md

The full file from forcedotcom/sf-skills at commit e5164d9, republished under its Apache-2.0 licence (© forcedotcom). 2,370 words, ~5,765 tokens.

Download SKILL.mdSave it as .claude/skills/service-itsm-agentic-setup-agentforce-studio-configure/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
service-itsm-agentic-setup-agentforce-studio-configure
description
Enable the Agentforce for IT Service Salesforce Go feature toggles (Agentforce Studio, Einstein Generative AI, the parent umbrella, and the Fulfiller/Employee agent templates) using the Salesforce CLI (sf). Turns ON org prefs via the Setup Discovery feature/{apiName}/enable Connect API route. Write-capable, idempotent, dependency-ordered, confirm-to-write required. Use when asked to enable Agentforce Studio, turn on Agentforce for IT Service, enable Einstein generative AI, or configure the org-level Agentforce for IT Service prerequisites. Triggers: enable agentforce studio, turn on agentforce for it service, enable einstein generative ai, configure agentforce org prefs. DO NOT TRIGGER: read-only prerequisite check (service-itsm-agentic-setup-agentforce-studio-validate), create/activate an agent (service-itsm-agentic-setup-fulfiller-agent-configure), or assigning permission sets.
allowed-tools
Bash, Read, AskUserQuestion
metadata.version
1.4
metadata.domains
Service, Agentforce
metadata.minApiVersion
67.0
metadata.relatedSkills
service-itsm-agentic-setup-agentforce-studio-validate, service-itsm-agentic-setup-employee-agent-configure, service-itsm-agentic-setup-fulfiller-agent-configure

Enable Agentforce for IT Service Prerequisites

Enable the Agentforce for IT Service Salesforce Go feature toggles — Einstein Generative AI, Agentforce Studio, the parent umbrella, and the path-specific agent template (Fulfiller or Employee) — entirely through the Salesforce CLI (sf). These toggles are the prerequisites for creating the Fulfiller/Employee agent; this write-capable step turns them on via the Setup Discovery POST /connect/setup/discovery/feature/{apiName}/enable Connect API route. Each toggle is enabled idempotently (skipped if already ENABLED), dependencies are enabled first, and explicit confirmation is required before any write.

StepSkillWhat it does
1. Validateservice-itsm-agentic-setup-agentforce-studio-validateRead the toggles → READY / NOT-READY (no writes)
2. Configure (this skill)service-itsm-agentic-setup-agentforce-studio-configureTurn the disabled toggles ON
3. Create agentservice-itsm-agentic-setup-fulfiller-agent-configureCreate + activate the Fulfiller agent

This skill is typically reached via hand-off from the validate skill's NOT-READY report, but can also be run directly.

A helper script — scripts/classify-enable-plan.mjs — reads the batched /features/status response and deterministically computes the dependency-ordered enable plan (before writing) and the final per-feature verdict (after writing). This mirrors the validate skill's classify-readiness.mjs contract (authoring standard A9): the decision logic lives in a script, not in prose.

Scope

  • In scope: Enabling Einstein Generative AI (sales-cloud-einstein-generative-ai), Agentforce Studio (sales-cloud-agent-studio), the parent umbrella (service-cloud-agentforce-for-itsm), and the path-specific template (service-cloud-it-fulfiller-agent for fulfiller; service-cloud-requestor-agent + service-cloud-it-service-employee-agent for employee) via POST .../feature/{apiName}/enable; reading live per-org state from /features/status before and after each write; enabling dependencies first; confirming enablement stuck via re-query; surfacing an ENABLED / ALREADY-ENABLED / FAILED verdict per feature. Writes are idempotent — skip if already ENABLED.
  • Out of scope: Provisioning Agentforce licenses (Setup > Company Information > Permission Set Licenses); assigning permission sets to users; creating, configuring, or activating an agent (service-itsm-agentic-setup-fulfiller-agent-configure); read-only prerequisite validation without writes (service-itsm-agentic-setup-agentforce-studio-validate); the org-wide multi-agent orchestration toggle (a Headless360-only pref, not a Connect feature).

Which path?

Determine whether the user is enabling prerequisites for the fulfiller agent or an employee agent. If unclear, ask (AskUserQuestion) — same path selection the validate skill uses:

  • fulfiller → sales-cloud-einstein-generative-ai → sales-cloud-agent-studio → service-cloud-agentforce-for-itsm → service-cloud-it-fulfiller-agent
  • employee → sales-cloud-einstein-generative-ai → sales-cloud-agent-studio → service-cloud-agentforce-for-itsm → service-cloud-requestor-agent → service-cloud-it-service-employee-agent

Einstein Generative AI is a dependency of Agentforce Studio and is not one of the toggles the validate skill reports on directly, but it must be enabled first if it is off — the classifier includes it in the enable plan for both paths.


Preconditions

Same as the validate skill (they share the target org and API surface). If unmet, sf surfaces an auth error or a 401/403/404; do not fabricate state — surface the raw error and stop.

  1. sf CLI installed and authenticated to the target org (sf org display -o <alias> shows Connected). All calls use --target-org <alias>; never extract or pass the access token by hand.
  2. API v67.0+: the connect/setup/discovery feature APIs are available at v67.0. The version is pinned in the URL path; do not hand-edit it below the minimum.
  3. node ≥ 18 on PATH (runs the classifier script).
  4. Agentforce license on the org (accessCheck) — a missing license surfaces as 403 or as enableBlockedReasons on the read.

Operations at a glance

OperationCommandReturns
Read feature togglessf api request rest ".../connect/setup/discovery/features/status" --method POST --body '{"featureApiNames":[...]}' --target-org <alias>{items:[{apiName,status,enableBlockedReasons[],dependencyStatuses[]}]}
Enable one togglesf api request rest ".../connect/setup/discovery/feature/{apiName}/enable" --method POST --body '{}' --target-org <alias>{success:boolean} — endpoint takes no meaningful body, but --body '{}' must be passed explicitly (see gotchas)

Both are Connect API routes reachable via sf api request rest — no Headless360 dispatcher required. Full command shapes, the response envelope, and the error taxonomy live in references/cli-invocation.md.

Never extract the access token. Use sf api request rest directly — it uses the CLI's stored session for the target org. Do not pull the accessToken out of sf org display and hand-build an HTTP request with it.

CRITICAL: DO NOT use IPCManagement updateOrgPref to flip agent prefs. That controller's write allow-list rejects the agent prefNames (Invalid prefName, 500). The Setup Discovery POST /feature/{apiName}/enable endpoint is the only correct write path for these toggles.


Architecture — How enablement works

StepWhat happensTool used
Pick pathDetermine fulfiller vs employee (ask if unclear)AskUserQuestion
Read current statePOST the feature-status batch for the path's toggles, capture to a fileBash (sf api request rest)
PlanRun scripts/classify-enable-plan.mjs <file> <agentType> [exitStatus] → dependency-ordered pending listBash (node)
Confirm-to-writePresent the exact pending list and require explicit "yes"AskUserQuestion
EnableRe-read + reclassify before each apiName in order, POST .../feature/{apiName}/enable when unblocked, record the resultBash (sf api request rest + node scripts/record-enable-result.mjs)
VerifyRe-read /features/status, re-run the classifierBash (sf + node)
ReportRun scripts/classify-final-report.mjs over the before/results/after files → final verdictBash (node)

Workflow

Substitute <alias> with the target org alias. <agentType> is fulfiller or employee.

Phase 1 — Read Current State
  1. POST the feature-status batch for every toggle the chosen path needs, capturing stdout to a file. Do not add --json. Write the request body once to a temp file and reuse it verbatim in Phases 1, 4, and 5 via --body "$(cat ...)" — never retype it, substitute a placeholder like [...], or rely on a shell variable, since each Bash invocation may run in a fresh shell where a plain variable would be unset:

    bash
    cat > /tmp/feature-status-body.json <<'EOF'
    {"featureApiNames":["sales-cloud-einstein-generative-ai","sales-cloud-agent-studio","service-cloud-agentforce-for-itsm","service-cloud-it-fulfiller-agent","service-cloud-requestor-agent","service-cloud-it-service-employee-agent"]}
    EOF
    
    sf api request rest "/services/data/v67.0/connect/setup/discovery/features/status" \
      --method POST \
      --body "$(cat /tmp/feature-status-body.json)" \
      --target-org <alias> > /tmp/enable-status-before.json 2>/tmp/enable-status-before.err
    echo $? > /tmp/enable-status-before.exit

    Capture the exit status — do not swallow it with || true.

Phase 2 — Plan (helper script)
  1. Run the classifier over the captured file to compute the dependency-ordered enable plan, saving its output — Phase 6 consumes this file, not the raw /features/status response:

    bash
    node "<skill_dir>/scripts/classify-enable-plan.mjs" /tmp/enable-status-before.json <agentType> "$(cat /tmp/enable-status-before.exit)" > /tmp/enable-plan-before.json

    It prints { agentType, readState, features, order, alreadyEnabled, pending, blocked, unconfirmed, verdict, reasons, rawError }. verdict: "ALL-ENABLED" means nothing to do — skip to Phase 6. verdict: "NEEDS-ENABLE" means pending (in order) lists what to enable. blocked lists any pending toggle whose enableBlockedReasons is non-empty as of this read — a toggle blocked only on an earlier dependency in order becomes enable-able once that dependency is on, so Phase 4 re-checks each toggle immediately before attempting it rather than trusting this snapshot for the whole loop. unconfirmed lists any required toggle missing from the response or carrying a status this classifier doesn't recognize — verdict: "CANNOT-CONFIRM" (not "ALL-ENABLED") when unconfirmed is non-empty and pending is empty. readState: "error" ⇒ surface rawError and stop; readState: "not-wired" ⇒ report CANNOT-CONFIRM and stop.

Phase 3 — Confirm-to-Write Checkpoint (REQUIRED)
  1. Present the exact pending list (excluding anything in blocked) and require an explicit "yes" from the user via AskUserQuestion before proceeding. Enabling org prefs mutates org state. Proceed to Phase 4 ONLY on an explicit "yes". On "no", stop and report the current state without any writes.
Phase 4 — Enable (Dependencies First, Re-Checked Before Each Toggle)
  1. Iterate order in sequence (Einstein GenAI → Studio → parent → child template(s)). Before attempting to enable each <apiName>, re-read and reclassify — this is what lets a child that was blocked in Phase 2 (only because Studio/parent was still off) become enable-able once that dependency's own enable has landed, instead of being permanently written off from the Phase-2 snapshot:

    bash
    sf api request rest "/services/data/v67.0/connect/setup/discovery/features/status" \
      --method POST \
      --body "$(cat /tmp/feature-status-body.json)" \
      --target-org <alias> > /tmp/enable-status-loop.json 2>/tmp/enable-status-loop.err
    echo $? > /tmp/enable-status-loop.exit
    node "<skill_dir>/scripts/classify-enable-plan.mjs" /tmp/enable-status-loop.json <agentType> "$(cat /tmp/enable-status-loop.exit)"

    Inspect features["<apiName>"].signal from that output:

    • PASS → already ENABLED; nothing to do, move to the next apiName in order.

    • FAIL with an empty enableBlockedReasons → enable it now:

      bash
      sf api request rest "/services/data/v67.0/connect/setup/discovery/feature/<apiName>/enable" \
        --method POST \
        --body '{}' \
        --target-org <alias> > /tmp/enable-<apiName>.json 2>/tmp/enable-<apiName>.err
      node "<skill_dir>/scripts/record-enable-result.mjs" /tmp/enable-<apiName>.json <apiName> /tmp/enable-results.json

      The /enable endpoint itself takes no meaningful body, but sf api request rest --method POST with no --body flag at all fails with Error (SfError): No 'mode' found in 'body' entry — always pass --body '{}' explicitly. record-enable-result.mjs reads the response, classifies it ENABLED/FAILED, and accumulates it into /tmp/enable-results.json keyed by apiName — the deterministic per-toggle bookkeeping Phase 6 consumes.

    • FAIL with a non-empty enableBlockedReasons → still blocked even after this iteration's re-check (a real, not merely-sequential, blocker — e.g. unlicensed) — do not POST; move to the next apiName in order and let Phase 6 report the blocker verbatim.

    • CANNOT-CONFIRM / ERROR on this specific apiName's read → stop the loop and surface the read failure; do not guess at remaining toggles.

    One failed toggle does not block the rest of the plan — continue the loop.

Phase 5 — Verify Enablement
  1. Re-run the Phase 1 read (same /tmp/feature-status-body.json) into a fresh file, and re-run the classifier over it, saving its output for Phase 6:

    bash
    sf api request rest "/services/data/v67.0/connect/setup/discovery/features/status" \
      --method POST \
      --body "$(cat /tmp/feature-status-body.json)" \
      --target-org <alias> > /tmp/enable-status-after.json 2>/tmp/enable-status-after.err
    echo $? > /tmp/enable-status-after.exit
    node "<skill_dir>/scripts/classify-enable-plan.mjs" /tmp/enable-status-after.json <agentType> "$(cat /tmp/enable-status-after.exit)" > /tmp/enable-plan-after.json

    verdict: "ALL-ENABLED" ⇒ every toggle in order is confirmed ENABLED — success. Anything still in pending/blocked/unconfirmed needs Phase 6 to classify it precisely (FAILED vs CANNOT-CONFIRM).

Show full SKILL.md (1,034 more words)Show less
Phase 6 — Aggregate Verdict (helper script)
  1. Run the final aggregator over the Phase-2 classifier output (/tmp/enable-plan-before.json, not the raw /features/status response), the Phase-4 accumulated results (or - if Phase 2 was already ALL-ENABLED/CANNOT-CONFIRM/ERROR and Phase 4 never ran), and the Phase-5 classifier output (/tmp/enable-plan-after.json):

    bash
    node "<skill_dir>/scripts/classify-final-report.mjs" /tmp/enable-plan-before.json /tmp/enable-results.json /tmp/enable-plan-after.json

    It prints { features: { <apiName>: { finalStatus, reason } }, order, overall, reasons } where finalStatus is ALREADY-ENABLED | ENABLED | FAILED | CANNOT-CONFIRM | ERROR and overall is SUCCESS | PARTIAL | FAILED | CANNOT-CONFIRM | ERROR. Render this directly into the Output Format — do not re-derive the per-feature verdict or the overall summary in prose (authoring standard A9). On overall: "SUCCESS", point the user at service-itsm-agentic-setup-fulfiller-agent-configure (fulfiller) or service-itsm-agentic-setup-employee-agent-configure (employee) to proceed.


Rules / Constraints

ConstraintRationale
Enable via POST /connect/setup/discovery/feature/{apiName}/enable onlyThe IPCManagement updateOrgPref write allow-list rejects the agent prefNames (Invalid prefName) — this is the only correct write path
Read live per-feature state from POST /features/status, never a flat catalog listOnly /status carries per-org ENABLED/NOT_ENABLED + enableBlockedReasons[] + dependencyStatuses[]
Enable dependencies before children, in the classifier's orderEnabling a child before Einstein GenAI / Studio surfaces unmet-dependency blockers
Re-read and reclassify immediately before each toggle in the Phase-4 loop, not once at the top of the loopA child blocked only because an earlier dependency was still off becomes enable-able the instant that dependency's own /enable lands — a single Phase-2 snapshot would report it FAILED even though it was never really blocked
Idempotent: skip /enable for anything already ENABLEDThe classifier's alreadyEnabled list is authoritative; enabling an already-enabled feature returns {success:true} but is redundant
REQUIRED confirm-to-write checkpoint before any /enable POSTEnabling org prefs mutates org state; the user must explicitly approve the exact pending list
Never attempt to enable a toggle whose current (re-checked) enableBlockedReasons is non-emptyNon-empty enableBlockedReasons means the write would fail — report the blocker instead of a doomed POST
verdict: "CANNOT-CONFIRM" when unconfirmed is non-empty, even if pending is emptyA required toggle missing from the response, or with an unrecognized status, must not be reported as ALL-ENABLED just because nothing is left in pending
Classification and per-toggle result recording live in scripts/classify-enable-plan.mjs, scripts/record-enable-result.mjs, and scripts/classify-final-report.mjs, invoked via Bash — not in proseDeterministic decision tables and aggregation over fixed feature statuses (authoring standard A9)
The read/write goes through sf api request rest; never extract the access tokensf api request rest uses the CLI's stored session for --target-org
Do not pass --json to sf api request restUnsupported on some Connect endpoints; the raw stdout body is already JSON
These are Connect API features — use SF CLI, not Headless360A Connect/Tooling equivalent exists, so SF CLI is preferred (avoids the Headless360HostedMcpServer org-perm gate)

Gotchas

IssueResolution
sf api request rest --method POST with no --body flag errors No 'mode' found in 'body' entryThe /enable endpoint itself takes no meaningful body, but the CLI still requires the flag — always pass --body '{}' explicitly (verified on CLI 2.140.6 and 2.145.6)
Feature API name unavailable / unlicensed on the orgNo catalog endpoint enumerates valid names — the Phase-1 /features/status read surfaces an unavailable/unlicensed feature via enableBlockedReasons[] before any enable attempt
/features/status shows NOT_ENABLED with dependencyStatuses[] unmetEnable the listed dependency first (Einstein GenAI before Studio, Studio before the parent/child toggles)
updateOrgPref → 500 Invalid prefName for an agent prefWrong write path — use the Setup Discovery feature/{apiName}/enable endpoint instead
Auth error from sf api request restThe target org's session needs re-authentication (sf org login web)
Treating an auth/permission/empty-body read failure as "not wired"Pass the captured $? as the classifier's 3rd arg — only a confirmed 404 is CANNOT-CONFIRM; anything else is ERROR (surface rawError, stop)
Reporting ALL-ENABLED because pending is emptyAlso check unconfirmed — a required toggle missing from the response or with an unrecognized status is neither confirmed ENABLED nor NOT_ENABLED
Re-deriving the per-feature / overall verdict in prose from the before/after JSONRun scripts/classify-final-report.mjs — the aggregation is fixed comparison logic (authoring standard A9), not a judgment call
Setting the feature-status request body in a shell variable in Phase 1 and expecting it in Phase 4/5Each Bash invocation may run in a fresh shell where the variable is unset, silently sending an empty body — persist it to /tmp/feature-status-body.json once and read it back with --body "$(cat /tmp/feature-status-body.json)" in every phase

Verification Checklist

  • The agent path (fulfiller / employee) was determined (asked if unclear).
  • Phase 1 read /features/status via sf api request rest, captured to a file, with its exit status captured.
  • scripts/classify-enable-plan.mjs computed the pending (dependency-ordered), blocked, and unconfirmed lists before any write.
  • The user explicitly confirmed the exact pending list at the Phase-3 checkpoint before any /enable POST.
  • Phase 4 re-read and reclassified before each toggle, not once for the whole loop — so a child unblocked by an earlier dependency's enable was still attempted.
  • Each attempted /enable POST's response was recorded via scripts/record-enable-result.mjs, accumulated across the loop.
  • /features/status was re-read after enablement and the classifier re-run to confirm the final per-feature verdict.
  • scripts/classify-final-report.mjs (not prose) computed the final per-feature status and overall summary from the before/results/after files; the access token was never extracted.

Output Format

Emit the report as live Markdown — never inside a code fence (a fenced table shows raw | pipes, not a table). This is Stage 1 (Foundation) that Stage 2 (install & activate the agent) builds on; each Status is ENABLED / ALREADY-ENABLED / FAILED / CANNOT-CONFIRM. Lay it out exactly like this:

Agentforce for IT Service — Stage 1: Enable Platform Features (via service-itsm-agentic-setup-agentforce-studio-configure)

  • Org: <org-alias> (API v67.0)
  • Agent path: fulfiller | employee
#Platform featureStatus
1Einstein Generative AI<status>
2Agentforce Studio<status>
3Agentforce for IT Service<status>
4<path-specific toggle(s)><status>

Verdict: SUCCESS | PARTIAL | FAILED | CANNOT-CONFIRM | ERROR

Next steps:

  • If SUCCESS: "Stage 1 (foundation) complete — proceed to Stage 2: install + activate the <path> agent via service-itsm-agentic-setup-fulfiller-agent-configure (fulfiller) / the employee-agent skill."
  • If PARTIAL/FAILED/CANNOT-CONFIRM/ERROR: list the affected toggle(s) + reason (enableBlockedReasons, unconfirmed status, or read error) + remediation steps

Substitute overall and each feature's finalStatus from scripts/classify-final-report.mjs into the <status> cells verbatim — do not recompute. No files are produced beyond the classifiers' temporary response captures.

Label rows exactly as the classifier emits them — never add Requestor, Specialized, or parent.


Reference File Index

FileWhen to read
references/cli-invocation.mdEvery phase — exact sf api request rest read/write call shapes, response envelope, feature API names, the classifier contract, and the error taxonomy

© forcedotcom, Apache-2.0. 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 4 other files (scripts, references) in skills/service-itsm-agentic-setup-agentforce-studio-configure of forcedotcom/sf-skills.

  • SKILL.md
  • references/cli-invocation.md
  • scripts/classify-enable-plan.mjs
  • scripts/classify-final-report.mjs
  • scripts/record-enable-result.mjs

Open the folder on GitHubat commit e5164d9

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    Get from a fresh clone of Portwood to a working, fully-tested Salesforce org.

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Works with

Categories

Questions about Service Itsm Agentic Setup Agentforce Studio Configure

What does Service Itsm Agentic Setup Agentforce Studio Configure do?

Enable the Agentforce for IT Service Salesforce Go feature toggles (Agentforce Studio, Einstein Generative AI, the parent umbrella, and the Fulfiller/Employee agent templates) using the Salesforce…. Service Itsm Agentic Setup Agentforce Studio Configure is an agent skill from forcedotcom/sf-skills. Enable the Agentforce for IT Service Salesforce Go feature toggles (Agentforce Studio, Einstein Generative AI, the parent umbrella, and the Fulfiller/Employee agent templates) using the Salesforce CLI (sf).

When should I use Service Itsm Agentic Setup Agentforce Studio Configure?

Service Itsm Agentic Setup Agentforce Studio Configure fits situations like: asked to enable Agentforce Studio; turn on Agentforce for IT Service; enable Einstein generative AI; configure the org-level Agentforce for IT Service prerequisites.

How do I install Service Itsm Agentic Setup Agentforce Studio Configure in Claude Code?

Run `npx skills add forcedotcom/sf-skills --skill service-itsm-agentic-setup-agentforce-studio-configure -a claude-code`. Or copy the skill folder (skills/service-itsm-agentic-setup-agentforce-studio-configure in forcedotcom/sf-skills) into .claude/skills/service-itsm-agentic-setup-agentforce-studio-configure in your project. Claude Code loads it when a task matches its description.

How do I install Service Itsm Agentic Setup Agentforce Studio Configure in Codex?

Run `npx skills add forcedotcom/sf-skills --skill service-itsm-agentic-setup-agentforce-studio-configure -a codex`. Or copy the skill folder (skills/service-itsm-agentic-setup-agentforce-studio-configure in forcedotcom/sf-skills) into .agents/skills/service-itsm-agentic-setup-agentforce-studio-configure in your project. Codex loads it when a task matches its description.

Can I use Service Itsm Agentic Setup Agentforce Studio Configure 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 forcedotcom/sf-skills --skill service-itsm-agentic-setup-agentforce-studio-configure -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/service-itsm-agentic-setup-agentforce-studio-configure, .gemini/skills/service-itsm-agentic-setup-agentforce-studio-configure, .github/skills/service-itsm-agentic-setup-agentforce-studio-configure and .opencode/skills/service-itsm-agentic-setup-agentforce-studio-configure in your project.

What does Service Itsm Agentic Setup Agentforce Studio Configure need to run?

Going by SKILL.md and its folder, Service Itsm Agentic Setup Agentforce Studio Configure needs JavaScript for the scripts in its folder and the command-line tools its instructions call (sf and node). Our summary lists: Node.js. Its frontmatter pre-approves these tools: Bash, Read, AskUserQuestion.

Does Service Itsm Agentic Setup Agentforce Studio Configure 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 Service Itsm Agentic Setup Agentforce Studio Configure safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Service Itsm Agentic Setup Agentforce Studio Configure use?

Service Itsm Agentic Setup Agentforce Studio Configure is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Service Itsm Agentic Setup Agentforce Studio Configure use?

About 5.8k tokens (SKILL.md is roughly 23k 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 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Service Itsm Agentic Setup Agentforce Studio Configure?

Skills that share tags, products or a category with Service Itsm Agentic Setup Agentforce Studio Configure: Soql Lib Query Builder (beyond-the-cloud-dev/soql-lib, 154 stars), Sf Datacloud (Jaganpro/sf-skills, 424 stars), Soql Lib Selector (beyond-the-cloud-dev/soql-lib, 154 stars) and Dev Setup (Portwood-Global-Solutions/Portwood, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Service Itsm Agentic Setup Agentforce Studio Configure?

forcedotcom (a GitHub organization) maintains it in forcedotcom/sf-skills, which has 1,065 GitHub stars. The repository holds 251 skills in this directory. The repository was last updated on October 7, 2026.

Source: forcedotcom/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.