mcp-engine-ai-readines…
Use when preparing or assessing a Power BI semantic model for Copilot, Fabric data agents, or natural-language Q&A — clear…
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 Teams posture tailored to their workflow. For changing
$ npx -y skills add maxanatsko/mcp-engine-public --skill mcp-engine-onboarding --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/mcp-engine-onboardingContext preview
The summary Claude sees to decide when to auto-load this skill.
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 Teams posture tailored to their workflow. For changing
name: mcp-engine-onboarding description: 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 Teams posture tailored to their workflow. For changing security, policy, or masking on an already-configured server, use mcp-engine-security-governance.
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.
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](references/feature-setup-guide.md) as the source-of-truth checklist for Pro and Teams 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. `manage_license` reports the Teams tier as `tier: "enterprise"`; treat it as Teams. 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.
Use existing SemanticOps MCP surfaces only:
Always preview pack application first when the tool supports it:
Full guided setup is allowed only after the user approves the exact batch. Approved setup can include:
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. Teams 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.
The SemanticOps MCP (formerly MCP Engine) is a Power BI tool that lets AI assistants like Claude interact with your Power BI models programmatically: read your model structure, run DAX queries, modify your semantic model, and perform advanced analytics - all through natural conversation.
Use when preparing or assessing a Power BI semantic model for Copilot, Fabric data agents, or natural-language Q&A — clear…
Use when starting a SemanticOps MCP session, connecting to a Power BI model, checking the current connection, applying saved…
Use when a Power BI measure or query returns wrong values, unexpected blanks, inflated or duplicated totals, a total row that…
Use when a DAX query, measure, or visual is slow, when interpreting analyze timings, Storage Engine / Formula Engine splits, or…
Use when the user asks for a model audit, model quality review, scorecard, bad-practices or best-practices assessment, or a…
Use when writing or fixing DAX query text, choosing between run_query operations, validating results, testing RLS access for…