dj

Activate this skill when authoring DataJunction (DJ) nodes via the REST API directly (curl, HTTP clients) — typically for exploration, ad-hoc prototyping, or namespaces that aren't repo-backed. For modeling decisions and the decomposition workflow, invoke `datajunction-semantic-model`. For repo-backed YAML authoring (the production path), invoke `datajunction-repo`. For concepts, invoke `datajunction`. Keywords: - DJ API, REST API, curl - POST nodes/metric, POST nodes/dimension - create metric, create dimension, create cube - API approach, direct API changes - prototyping, exploration

Activate this skill for querying DataJunction (DJ) — finding nodes, generating SQL, fetching metric data, exploring lineage, visualizing results — via the DJ UI, MCP tools, or REST/GraphQL APIs. For core DJ concepts and vocabulary, invoke `datajunction`. For modeling decisions (what shape something should take), invoke `datajunction-semantic-model`. For authoring nodes, invoke `datajunction-repo` (YAML) or `datajunction-api` (REST). Keywords: - query metric, query metrics - generate SQL, build metric SQL - get metric data, fetch metric - available dimensions, common dimensions - search_nodes, get_node_details, get_node_lineage - get_common, build_metric_sql, get_metric_data - visualize metrics - MCP tools, DJ API, GraphQL - DJ UI, web UI, browse

Activate this skill when authoring DataJunction (DJ) nodes via YAML files in a git repository — the repo-backed workflow. Covers YAML schemas per node type, branch-based development, temporal partitions on cubes, and the full PR-driven deployment flow. For modeling decisions (how to structure metrics, decomposition workflow), invoke `datajunction-semantic-model`. For direct API authoring, invoke `datajunction-api`. For concepts, invoke `datajunction`. Keywords: - YAML nodes, YAML definitions - repo-backed namespace, repo-backed workflow - git workflow, branch development, feature branch - cube YAML, metric YAML, dimension YAML, transform YAML - create metric, create dimension, create cube, build cube - temporal partition, partition pushdown - pre-commit, push.sh

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; for YAML schemas and the repo-backed authoring flow, invoke `datajunction-repo`; for direct API examples, invoke `datajunction-api`. For DJ concepts (node types, dim links), invoke `datajunction`. For querying existing metrics, invoke `datajunction-query`. Keywords: - semantic modeling - decompose query, model query, query to nodes - create metric, create dimension, create node, create cube, build 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

Activate this skill whenever working with DataJunction (DJ) semantic layer. Core concepts and shared vocabulary used by every DJ workflow. For querying metrics, invoke `datajunction-query`. For modeling decisions (what shape something should take), invoke `datajunction-semantic-model`. For authoring nodes, invoke `datajunction-repo` (YAML in a git repo) or `datajunction-api` (REST API for exploration / prototyping). Keywords: - DataJunction, DJ - semantic layer - dimension link, dimension links - star schema - node types - source, transform, dimension, metric, cube - metric, metrics - mode, status, valid, invalid, draft, published - namespace