mcp-engine-ai-readiness

Use when preparing or assessing a Power BI semantic model for Copilot, Fabric data agents, or natural-language Q&A — clear business terminology, unambiguous metrics, usable date defaults, focused field exposure, descriptions, AI instructions, AI data schema recommendations, verified-answer candidates, or natural-language validation tests. For general modeling quality unrelated to AI consumption, use mcp-engine-model-quality.

Jump to install

Repository: maxanatsko/mcp-engine-public
Last source activity: August 1, 2026 at 15:37
Detected SKILL.md language: English
Stars: 255
Forks: 65

Install options

The review-first prompt is selected by default. You can switch to a direct command or download a local copy.

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

A direct command skips the review prompt. Inspect the source before running it.

npx skills add https://github.com/maxanatsko/mcp-engine-public --skill mcp-engine-ai-readiness

The command stays on one line. Scroll horizontally to inspect it before copying.

Prefer a local copy? Download the files currently available to SkillsMP.

Review the source files

Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.

File Explorer

6 files

SKILL.md

Source instructions · Read-only preview

Back to search Back to installation

mcp-engine-bootstrap

Use when starting a SemanticOps MCP session, connecting to a Power BI model, checking the current connection, applying saved preferences, composing bulk or write payloads, or recovering from empty results, stale metadata, or argument validation errors. For task work after the session is healthy, use the matching mcp-engine skill (query, schema-authoring, semantic-authoring, testing-changes, security-governance).

2026-08-27

mcp-engine-model-quality

Use when the user asks for a model audit, model quality review, scorecard, bad-practices or best-practices assessment, or a review of star-schema fit, relationships, DAX maintainability, VertiPaq/storage risk, metadata hygiene, governance signals, or validation gaps in a Power BI semantic model. For diagnosing one slow query, use mcp-engine-dax-performance; for Copilot or natural-language readiness, use mcp-engine-ai-readiness; to execute the remediation backlog, use mcp-engine-refactoring.

2026-08-27

mcp-engine-schema-authoring

Use when creating, updating, renaming, or deleting tables, columns, calculated columns, relationships, hierarchies, calendars, or partitions, or changing refresh strategy and incremental refresh policy. For measures, calculation groups, or named expressions, use mcp-engine-semantic-authoring; for RLS roles or perspectives, use mcp-engine-security-governance; for multi-object refactors or renames with downstream consumers, use mcp-engine-refactoring.

2026-08-27

View all from this repository

name

mcp-engine-ai-readiness

description

PBI AI Readiness

Use this skill to turn an existing Power BI semantic model into a Copilot-ready assessment and artifact pack. This is an authoring and review workflow, not a runtime MCP tool.

Start Here

Confirm the user wants a readiness assessment, artifact drafts, or both.
Prefer metadata-level inspection before querying data values.
Use existing SemanticOps MCP tools when available: list_model, manage_dependencies, run_query, manage_tests, and manage_model_properties.
Keep unsupported Prep data for AI actions as drafts for Power BI Desktop, Power BI service, PBIP, Git, or manual review.
Separate recommendations into:
- can apply through MCP/model metadata now
- draft/export for Prep data for AI UI or PBIP/Git workflow
- validate manually in Copilot

Workflow

Read copilot-readiness-workflow for the assessment sequence, tool usage, privacy guardrails, and output order.
Read readiness-scorecard when producing severity, score, business impact, and remediation priority.
Read ai-artifact-templates when drafting AI instructions, AI data schema recommendations, verified answers, or manage_tests candidates.
Read domain-examples when the model is sales, finance, support, or operational and the user wants concrete starting examples.

Guardrails

Do not claim SemanticOps MCP can directly configure all Power BI Prep data for AI settings over live TOM/XMLA.
Treat AI instructions, AI data schemas, and verified answers as draft artifacts unless the user provides an explicit supported PBIP/Git path or asks for manual-application guidance.
Do not expose sensitive values from data previews. Prefer names, descriptions, expressions, relationships, dependencies, and aggregate-only validation queries.
Respect SemanticOps MCP mode, policy, confirmation, license, and audit gates for any suggested or requested model change.
Make nondeterminism explicit: readiness work can improve Copilot behavior, but it cannot guarantee identical answers for every prompt.

Output Standard

Return a compact readiness pack unless the user asks for raw details:

Executive summary with readiness level.
Scorecard grouped by critical, high, medium, and low findings.
Recommended MCP-applicable model metadata fixes.
Draft AI instructions.
Draft AI data schema recommendation.
Verified-answer backlog.
Optional natural-language test suggestions.
Manual validation checklist for Power BI Desktop or service.

mcp-engine-ai-readiness

Source facts

Install options

Review the source files

More from this repository

More from this repository

PBI AI Readiness

Start Here

Workflow

Guardrails

Output Standard

PBI AI Readiness

Start Here

Workflow

Guardrails

Output Standard