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 creating or updating measures, KPIs, calculation groups, DAX UDFs, named expressions, Power Query parameters, or model properties, when deciding between a measure and a calculated column, or when cleaning up naming, display folders, and semantic style. For one-off DAX
$ npx -y skills add maxanatsko/mcp-engine-public --skill mcp-engine-semantic-authoring --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/mcp-engine-semantic-authoringContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when creating or updating measures, KPIs, calculation groups, DAX UDFs, named expressions, Power Query parameters, or model properties, when deciding between a measure and a calculated column, or when cleaning up naming, display folders, and semantic style. For one-off DAX
name: mcp-engine-semantic-authoring description: Use when creating or updating measures, KPIs, calculation groups, DAX UDFs, named expressions, Power Query parameters, or model properties, when deciding between a measure and a calculated column, or when cleaning up naming, display folders, and semantic style. For one-off DAX queries, use mcp-engine-query; for physical tables, columns, and relationships, use mcp-engine-schema-authoring; for consolidating or renaming measures with downstream consumers, use mcp-engine-refactoring.
Semantic-layer work through `manage_semantic`. Read only the references needed for the current task.
1. Prefer a measure for reusable business logic. Choose a calculated column only when a persisted row-level attribute is required — creating one is a `manage_schema` `create_calc_column` operation covered by `mcp-engine-schema-authoring`. 2. Ground names first: `list_model` `{ "operation": "list", "spec": { "type": "measures" } }` (repeat with `"tables"`; `spec.type` takes one value per call), and check for an existing equivalent with `{ "operation": "search", "spec": { "query": "<name>", "mode": "name" } }` before creating a near-duplicate measure. 3. Keep semantic edits separate from table or relationship edits unless the task explicitly spans both.
After semantic work, report:
1. Each object created or changed, with its DAX or M expression. 2. Whether `dry_run` or bulk mode was used and the outcome. 3. The validation query run and its result. 4. Naming or folder decisions that follow (or deliberately break) model conventions.
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 a user types onboarding, asks to set up SemanticOps MCP, wants help choosing Free vs Pro, or wants licensing, modes,…