# Datajunction Semantic Model

> | Activate this skill for DataJunction (DJ) semantic modeling decisions — choosing the right node shape (fact, dimension, transform, metric, cube), turning a draft SQL query into well-designed nodes, and the cross-cutting conventions (ownership, naming, namespace organization). Format-agnostic modeling guidance. Keywords: - semantic modeling - decompose query, model query, query to nodes - how should I model this metric, what shape should this node be - design a cube, what belongs in this cube - ratio metric, derived metric, base metric - composable metrics - metric query constraints - node ownership, metric ownership - metric naming, namespace organization - grain, fact vs dimension - dimension link, not JOIN

## Facts
- Page: https://tashan.sh/capability/skill-datajunction-datajunction-semantic-model
- tashan id: skill:DataJunction/datajunction-semantic-model
- Source: https://github.com/DataJunction/dj
- Type: skill
- Category: ai
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 97.0
- Freshness: 93.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: MIT
- Official: no

## Install

```sh
cp -r datajunction-semantic-model ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-09-12 by tashan (https://tashan.sh) from public evidence. Scorer s5.