Service Helpagent Coordinate

service-helpagent-coordinate: Service Cloud Help Agent, guided setup

Use this skill to stand up a Service Cloud Help Agent (an Agentforce Service Agent) on a Salesforce org from Claude Code, following the same guided flow as the Help Agent Quick Setup wizard. This is a coordinate skill: it orchestrates existing skills against a canonical spec — it does not author a new agent primitive.

Why this skill exists

Salesforce's official Help Agent template-creation API is not yet shipped. Without it, Claude has no built-in concept of "Help Agent" and would otherwise generate a generic agent. assets/help-agent-spec.md substitutes for the missing API: its agent script is the canonical template the eventual Quick Start UI will produce. Treat the spec as source of truth for the agent's lineage (topics, actions, instructions).

Scope

In scope:

• Guided, four-checkpoint Help Agent setup (identity → grounding → channel → go-live)
• Knowledge grounding via Agentforce Data Library (ADL)
• Web Chat / Help Portal channel setup and Experience Cloud site embed
• Readiness checks (licenses, Einstein Agent User, Data Cloud permission sets)

Out of scope — delegate elsewhere:

• OAuth / External Client App setup → integration-connectivity-connected-app-configure
• Raw agent authoring with no Help Agent lineage → agentforce-generate
• Metadata deploy/retrieve → platform-metadata-deploy

Prerequisites

• Claude Code + Salesforce CLI installed and an authenticated org (see repo README.md)
• MCP servers registered: salesforce-api-context, metadata-experts, sobject-reads
• Salesforce Skills installed into .agents/skills/ (or .claude/skills/)
• A Salesforce org with the required features enabled (or enable-able via metadata): Agentforce, Einstein Generative AI, Knowledge, Experience Cloud, and Data Cloud. Any org shape that meets this bar works — production, sandbox, scratch, or Developer Edition. The readiness check in assets/help-agent-spec.md §4.0 detects each feature and enables what can be enabled; it stops with a clear message if a required capability is missing and cannot be turned on.

Skills this coordinates

The spec feeds these existing skills — do not author a new Help Agent skill:

Skills inventory pre-flight (advisory — never a hard stop)

This skill delegates to several sibling skills. Depending on the runtime, those dependencies resolve one of two ways: as directories under .claude/skills/, or through a runtime skill catalog that the harness resolves on demand (no local directory). An absent .claude/skills/<name> directory therefore does not prove a dependency is unavailable — it is normal when the catalog resolves skills at invocation time.

Run this check once, silently, for your own awareness — never as the run's first user-facing output:

for skill in agentforce-generate dx-org-permission-set-assign service-digital-engagement-channel-configure service-digital-engagement-deployment-configure experience-lwr-site-generate service-digital-engagement-messaging-site-integrate service-concierge-portal-generate service-agentforce-channel-configure; do
  [ -d ".claude/skills/$skill" ] && echo "OK: $skill" || echo "resolve-at-runtime: $skill"
done

Do not stop, and do not open the run with a missing-dependency roll call. Proceed with the checkpoints; delegate to each sibling skill only when the flow actually reaches it. If — and only if — a delegation step is actually reached and that specific skill cannot be resolved at that moment, surface that one skill by name at that point. Never front-load the full eight-skill inventory as the deliverable; it is not a decision the user owns and it is not the report.

Workflow

Read assets/help-agent-spec.md first — it is the authoritative flow and is intentionally kept small. Do not pre-load the rest. The heavy or conditional material is split into references/ and read only when the flow reaches it (progressive disclosure — this is deliberate, to keep token usage low):

• references/agent-script.md — the ~500-line canonical agent script + placeholder list. Load it only when you are ready to create the agent, after Checkpoint 2 — not during Checkpoints 1, 3, or 4.
• references/channel-web-chat.md — Web Chat provisioning detail. Load only if the user picks Web Chat at Checkpoint 3.
• service-concierge-portal-generate — Help Portal / Agentforce Concierge portal deploy. Delegate to this skill if the user picks Help Portal at Checkpoint 3 — do not inline the portal runbook steps here. Pass $ORG, $BOT_ID, and $BOT_DEV_NAME as context so the skill skips its own entry-point questions.
• references/channel-voice.md — Voice channel wiring detail (existing numbers only). Load only if the user picks Voice.

Read the one channel file that matches the user's selection — never all three. Then run the interactive setup without one-shotting: walk the user through four checkpoints in order, waiting for a reply at each.

Readiness check (silent, MANDATORY, do not reorder)

Order is load-bearing — running step 3 before step 2 fails with PermissionSet not found: GenieUserEnhancedSecurity because the Data Cloud permission sets do not exist in the org until Data Cloud itself is turned on:

1. Verify PSL seat availability, then create a dedicated Einstein Agent User for this agent. First confirm the three required PSLs have available seats:

   sf data query --target-org $ORG --json \
     --query "SELECT MasterLabel, TotalLicenses, UsedLicenses FROM PermissionSetLicense WHERE DeveloperName IN ('AgentforceServiceAgentUserPsl', 'GenieDataPlatformStarterPsl', 'EinsteinGPTPromptTemplatesPsl')"

   For each, UsedLicenses < TotalLicenses must be true. If any PSL is at capacity, stop and surface which one is exhausted — the PSG assignment will fail and there is nothing the skill can do until a seat is freed or provisioned.

   If all three have capacity, create the user. Do not reuse any existing Einstein Agent User — each Help Agent gets its own. Username: {agentDevName}_user@{orgId}.ext (15-char org Id from sf org display). Email: noreply@salesforce.com. Profile: Einstein Agent User (query SELECT Id FROM Profile WHERE Name = 'Einstein Agent User' to get the ProfileId, then sf data create record --sobject User). If a user with exactly that username already exists, reuse it (idempotent). Then assign all four of the following before publishing the agent:

   • AgentforceServiceAgentUserPsg (Permission Set Group) — assigns three PSLs in one call: Agentforce Service Agent User, Data Cloud, and Einstein Prompt Templates. Use sf org assign permsetgroup.
   • AgentforceServiceAgentSecureBase (Permission Set) — required for all service agents. Use sf org assign permset.
   • AgentforceKnowledgeUser (Permission Set, force namespace) — required because the Help Agent uses the knowledge: block. Use sf org assign permset.
   • {AgentName}_Access (custom Permission Set) — created by agentforce-generate for agent-specific Apex/object access.

   Verify PSL assignments landed: SELECT PermissionSetLicense.DeveloperName FROM PermissionSetLicenseAssign WHERE Assignee.Username = '{agentDevName}_user@{orgId}.ext' — expect AgentforceServiceAgentUser, DataCloud, EinsteinPromptTemplates.

   Capture the username ({agentDevName}_user@{orgId}.ext) — it is the value for <default_agent_user_placeholder> in the agent script. The agent runs as this user at runtime.

   Pre-publish gate — verify before every sf agent publish authoring-bundle call. Skipping this causes a masked 401→404: SFAP returns HTTP 401 "User doesn't have access to agent" when the Einstein Agent User is missing AgentforceServiceAgentUserPsg, and jsforce's session-refresh retry silently converts that 401 to ERROR_HTTP_404. Verify all four assignments are present before invoking the CLI:

   AGENT_USER_ID=$(sf data query --target-org $ORG --json \
     --query "SELECT Id FROM User WHERE Username='{agentDevName}_user@{orgId}.ext'" \
     | python3 -c "import sys,json; print(json.load(sys.stdin)['result']['records'][0]['Id'])")
   
   # Must return exactly 1 row — if 0, assign before continuing
   sf data query --target-org $ORG --json \
     --query "SELECT PermissionSetGroup.DeveloperName FROM PermissionSetAssignment \
              WHERE AssigneeId='${AGENT_USER_ID}' \
              AND PermissionSetGroup.DeveloperName='AgentforceServiceAgentUserPsg'"
   
   # Must return 2 rows — if any are missing, assign before continuing
   sf data query --target-org $ORG --json \
     --query "SELECT PermissionSet.Name FROM PermissionSetAssignment \
              WHERE AssigneeId='${AGENT_USER_ID}' \
              AND PermissionSet.Name IN ('AgentforceServiceAgentSecureBase','AgentforceKnowledgeUser')"

   Do not call sf agent publish authoring-bundle until all four assignments return non-empty results.

2. Enable Data Cloud — must complete before step 3 (permission sets don't exist until Data Cloud is on). If Data Cloud is not yet provisioned, offer the user the choice up front — enable and come back later, or wait through it now.

3. CRITICAL — Assign the Data Cloud permission sets immediately after enablement. Non-negotiable — skipping it ships an agent whose grounding returns empty knowledgeSummary at runtime even though ADL indexing reports SUCCESS. The PSG assigned in step 1 covers this once Data Cloud is on.

Recognizing where the user is entering

Checkpoint 1 (agent identity) and Checkpoint 3 (channel) must always be confirmed with the user in the current conversation — never inferred from a previous session, a compacted summary, or skill arguments. These are decisions the user owns; acting on stale context from a prior run will configure the wrong agent or the wrong channel.

If the user's opening message in the current conversation turn explicitly names the agent and/or channel (e.g. "set up Master Yoda on Web Chat"), accept those as the inputs and confirm them before proceeding. If not stated in the current turn, ask.

For the org alias: if the user has been working against a specific org in the current session, use that. Otherwise ask.

Do not assume every run starts at Checkpoint 1. Read the opening prompt and enter at the right checkpoint: identity decided → Checkpoint 2 (grounding); grounding done → Checkpoint 3 (channel). When the prompt explicitly names or implies a later checkpoint (e.g. "set up the grounding", "wire up the web chat channel") — and states or clearly implies prior checkpoints are already done — accept those prior checkpoints as established, enter scoped at the named checkpoint, and produce a settled-facts report for it. Do NOT force a full guided-identity re-confirmation, do NOT restart at Checkpoint 1, and do NOT demand in-conversation re-confirmation of prior decisions. Values the prompt supplies for the entered checkpoint (audience → authMode, named site, categories) are decided, not questions to re-ask.

If the opening prompt names Voice / phone / telephony / IVR, read references/channel-voice.md and follow it from the top of Checkpoint 3 as the Voice branch.

Checkpoint 1 — Meet Your Agent

Ask for four things (offer these exact defaults so the guided-decision report can enumerate them without loading assets/help-agent-spec.md):

1. Agent Name — default Help Agent (DeveloperName Help_Agent).
2. Language — default en_US.
3. Welcome Greeting — default "Hi, I'm {Agent Name}. How can I help you today?".
4. Tone — default "calm, patient, friendly service agent — warm but professional, short sentences, never robotic."

When the opener names Q&A / case management / human escalation, note explicitly that these map to the canonical four-subagent shape (Agent Router → General FAQ, Service Customer Verification, Case Management, Escalation) — do not invent a different design.

Checkpoint 2 — Give Your Agent Context (grounding)

Ask which knowledge source (Salesforce Knowledge / files / website sync). Grounding is provisioning an Agentforce Data Library, not designing a search — the agent's knowledge: block does the retrieval at runtime. This checkpoint MUST produce all five of:

1. Delegate provisioning to agentforce-generate — it owns ADL create/index/publish. Do not hand-roll data-library metadata.
2. A dedicated, named library — create Help_Agent_Knowledge. Never wire the stock All_Records_and_Fields_Default (it sits in NOT_SCHEDULED on trial or preloaded sample-data orgs and returns empty knowledgeSummary with no error).
3. Category selection — for Salesforce Knowledge, query the org's Data Category Groups. Ask which categories to ground on only in an interactive run; for a non-interactive/scoped run, decide the sensible default (the org's default Data Category Group, or all groups if none is designated) and note the default explicitly in the settled-facts report's Blocking Issues line (e.g. "Knowledge data category not specified — chose the org's default group"). Do not stall the report asking.
4. Wait-for-indexing gate — poll and only proceed once indexingStatus.status ∈ {COMPLETED, READY, SUCCESS}. NOT_SCHEDULED is not success.
5. Capture the rag_feature_config_id (format ARFPC_<libraryId>) and wire it into the agent script's knowledge: block — never hardcode.

Anti-rule: never respond to a grounding request by designing a SOQL/SOSL/GraphQL/Apex search over Knowledge articles. Grounding is ADL provisioning; retrieval is the agent's job at runtime.