About dax
dax is a skill for writing, debugging, and optimizing DAX in semantic models. It provides object-reference conventions and a tiered optimization workflow with references for engine internals, query patterns, and model performance.
Key features
- 01Consistent column and measure references
- 02Tiered query optimization workflow
- 03DAX patterns and engine references
- 04Guidance for local and remote profiling
Use cases
- 01Write expressions for semantic models
- 02Debug slow DAX queries
- 03Profile local or remote models
- 04Review DAX before model changes
MCP support
The skill notes that Power BI Modeling MCP can be used for traces and queries as an MCP tool.
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/daxin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/dax-patterns.mddax-performance-optimization.mdengine-internals.mdmodel-optimization.mdDetails
From the project READMEDAX
Skills and references for writing, debugging, and optimizing DAX in semantic models.
Object references
Write column references as 'Table'[Column]: the table name is always present and always in single quotes. Write measure references as [Measure], never prefixed with a table. This applies everywhere DAX or model metadata names an object: measure, calculated column and calculation item expressions, RLS filters, format string expressions, DAX queries (te query, pbir model -q), and descriptions or annotations that mention an object.
Why: a measure belongs to the model rather than to its home table, so moving it between tables must not change any expression that uses it; a bare [Measure] also tells the reader at a glance that it is a measure and not a column. An unqualified column is ambiguous as soon as two tables share a column name, and quoting every table name keeps one consistent form whether or not the name needs quotes.
Optimization
For systematic DAX query performance optimization, read the workflow reference first:
references/dax-performance-optimization.md — Tiered framework (4 tiers), phased workflow, decision guide, and error handling.
Detailed reference files (progressive disclosure — consult as directed by the workflow):
references/engine-internals.md— FE/SE architecture, xmSQL, compression/segments, SE fusion, trace diagnosticsreferences/dax-patterns.md— Tier 1 DAX patterns (DAX001–DAX021) + Tier 2 query structure (QRY001–QRY004)references/model-optimization.md— Tier 3 model patterns (MDL001–MDL009) + Tier 4 Direct Lake (DL001–DL002)
Trace capture and performance profiling:
- Local models (Power BI Desktop): Use the Tabular Editor CLI
te query(see thete-cliskill) first; as an alternative, theconnect-pbidskill covers FE/SE timing (performance-profiling.md) and intermediate result inspection (evaluateandlog-debugging.md). - Remote models (Fabric Service / XMLA): Run DAX with the Tabular Editor CLI
te query(-s <workspace> -d <model>) against the workspace XMLA endpoint; see thete-cliskill (tabular-editor plugin). - Power BI Modeling MCP: also available for trace and query if you prefer an MCP tool; reach for it after the options above.
Related Skills
semantic-model— Model design, build, and auditing including DAX anti-patterns and best practicesconnect-pbid(pbi-desktop plugin) — Trace capture, performance profiling, EVALUATEANDLOG debugginglineage-analysis— Impact analysis before model changes