| name | topdesk-powerbi-modelling |
| description | Design, review, and harden Power BI semantic models for TOPdesk data. Use for star schemas, fact/dimension grain, relationships, DAX measure catalogs, calculation groups, field parameters, RLS/OLS, incremental refresh, aggregation tables, reconciliation, tenant mapping, and model documentation for TOPdesk incidents, changes, assets, knowledge, SLAs, operators, branches, and AI features. |
TOPdesk Power BI Modelling
Use this skill for semantic modelling decisions after source queries are known or when a model must be reviewed.
Workflow
- Fix the fact grain before choosing visuals or writing DAX.
- Separate facts from dimensions. Prefer conformed dimensions for date, branch, caller/person, operator, operator group, category, priority, status, source, asset, service, and knowledge article.
- Use explicit measures. Avoid implicit aggregation in report visuals.
- Define KPI semantics with numerator, denominator, date basis, exclusions, and reconciliation source.
- Design security early: RLS by branch/customer/operator scope; avoid exposing PII in broad dimensions.
- Specify refresh: OData/API pagination, incremental policy, historical backfill, refresh windows, and failure checks.
- Produce model documentation: table list, relationships, measures, RLS, refresh, validation, known tenant assumptions.
References
- Load
references/model-patterns.md for model architecture, relationships, and DAX rules.
- Load
references/topdesk-model-catalog.md for canonical TOPdesk facts, dimensions, and measures.
Assets
- Use
assets/semantic-model-spec-template.md for model specifications.
- Use
assets/measure-catalog-template.csv for DAX backlog planning.
Scripts
- Use
scripts/new_semantic_model_spec.py to generate a semantic model starter spec.
