PBI Onboarding

SkillSecurity

Use when a user types onboarding, asks to set up SemanticOps MCP, wants help choosing Free vs Pro, or wants licensing, modes, masking, guardrails, preferences, model safety, tests, reporting, diagnostics, RLS testing, or Enterprise posture tailored to their workflow. For changing security, policy, or masking on an already-configured server, use mcp-engine-security-governance.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the PBI Onboarding skill

What this skill tells your AI

The instructions your AI receives, as published by maxanatsko/mcp-engine-public in skills/mcp-engine-onboarding/SKILL.md and read by ahel’s review.

Use this skill to turn "onboarding" into a regular-user setup concierge. This is an orchestration workflow over existing SemanticOps MCP tools, not a runtime MCP tool.

Start Here

  1. Start with: "I'll ask a series of questions to tailor your SemanticOps MCP setup."
  2. Speak to regular users. Avoid jargon like CRUD, AST, resource/action, internal feature, or raw payload unless the user asks.
  3. Use feature-setup-guide as the source-of-truth checklist for Pro and Enterprise coverage.
  4. Ask one feature-area question at a time. Explain the feature, give examples, offer choices, recommend a default, and say what would be changed, previewed, or deferred.
  5. Check current license, mode, connection, preferences, and policy state when SemanticOps MCP tools are available.
  6. Produce a setup action plan before any activation, write, pack application, model selection, validation query, test creation, checkpoint, report generation, or audit operation.
  7. Ask for explicit approval before calling tools that activate licenses, mutate preferences, policies, tests, model metadata, select a model, run validation queries, create checkpoints, generate reports, or access audit logs.

Workflow

  • Read questionnaire for the short interview, defaults, and follow-up questions.
  • Read feature-setup-guide to cover all user-configurable Pro and Enterprise feature areas.
  • Read setup-profiles to map answers to recommended connection, mode, workflow, and safety posture.
  • Read policy-presets when recommending manage_policy packs or custom policy rules.
  • Read test-pack-presets when recommending manage_tests packs or starter validation tests.
  • Read playbook-template when producing the final user playbook.

Tool Usage

Use existing SemanticOps MCP surfaces only:

  • manage_license for license status, activation, refresh, and tier explanation.
  • manage_model_connection for connection discovery, selection, and current state.
  • list_model for metadata grounding after a model is connected.
  • manage_preferences for approved runtime preference changes.
  • manage_policy for approved policy pack previews or applications.
  • manage_tests for approved test pack previews or applications.
  • manage_model_changes for approved checkpoints, change history, undo/redo, and rollback planning.
  • manage_dependencies for approved impact checks before risky changes.
  • run_query for approved validation queries and Pro query diagnostics.
  • manage_audit only when Enterprise is active and the user asks for audit posture or evidence.

Always preview pack application first when the tool supports it:

  • manage_policy with operation: "packs_apply", spec.pack_id, and dry_run: true.
  • manage_tests with operation: "packs_apply", spec.pack_id, and dry_run: true.

Full guided setup is allowed only after the user approves the exact batch. Approved setup can include:

  • License activation through manage_license.
  • Connection selection through manage_model_connection.
  • Runtime preference changes through manage_preferences.
  • Policy pack preview and application through manage_policy.
  • Test pack preview and application through manage_tests.
  • Checkpoint or change-safety setup through manage_model_changes.
  • Impact checks through manage_dependencies.
  • Targeted validation queries through run_query.

Guardrails

  • Do not auto-apply preferences, policies, tests, or model changes from interview answers alone.
  • Do not echo license keys back to the user.
  • Do not use or reintroduce the removed onboarding_completed preference.
  • Do not collect or store sensitive business data in the onboarding notes.
  • Respect SemanticOps MCP mode, license, policy, confirmation, and audit gates.
  • Keep Enterprise-only recommendations clearly separated from local Pro workflows.
  • Treat require_confirm policy rules as user-experience guardrails, not a portable compliance control across every MCP client.
  • Prefer deny-style policy for high-risk operations when the user needs server-enforced protection.
  • If no model is connected, produce the general plan and mark model-specific actions as deferred.
  • Do not overclaim Desktop refresh observability. Desktop refresh status is best-effort diagnostics, not durable progress tracking.

Output Standard

Return a compact onboarding packet:

  1. License and mode posture.
  2. Masking choices.
  3. Guardrails previewed, applied, or deferred.
  4. Preferences and memory saved or recommended.
  5. Model-change safety and dependency-analysis choices.
  6. Test setup.
  7. Reporting, diagnostics, and RLS testing choices.
  8. Enterprise audit or admin-bundle posture when relevant.
  9. What was changed, previewed, deferred, and explicitly not changed.
  10. Copy/paste prompts for the user's next sessions.

Signals

GitHub stars
256
Forks
65
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
mcp-engine-onboarding
Source
github.com/maxanatsko/mcp-engine-public