| name | drawio-databricks |
| version | 1.0.0 |
| description | Use when the user asks for a Databricks lakehouse architecture diagram — medallion architecture (Bronze/Silver/Gold), Delta Lake, Unity Catalog, workspace deployment, data-plane/control-plane, or any diagram built with Databricks icons. Builds with the declarative layout engine using ground-truth stencils, validates (stencils/colors/nesting/geometry), runs a render-based vision self-check. Default output is .drawio; PNG/SVG only on request. |
| license | MIT |
Draw.io Databricks
Produce correct Databricks lakehouse architecture diagrams in draw.io. This skill
is a thin frontend; the deterministic engine, validator, and rules live in the
drawio-ai-kit package, reached via the drawio-ai CLI.
0. Preflight — the CLI must be installed
command -v drawio-ai >/dev/null 2>&1 || echo "Install the Kit first: npm i -g github:sparklabx/drawio-ai-kit"
If drawio-ai is not on PATH, stop and tell the user to run
npm i -g github:sparklabx/drawio-ai-kit. Never run npm i -g yourself — nothing mutates the
user's global environment without their say-so.
1. Shared Workflow
drawio-ai workflow
Prints the build → validate → render → write-to-project-path loop every diagram
follows. Read it; it is the source of truth for the process.
2. Domain rules
drawio-ai principles --mode databricks
Returns the Databricks rules + shared principles + catalog categories.
3. Build with the engine, then validate + render
Resolve the Kit's install dir, then import the engine by absolute path (the
Shared Workflow shows the exact pattern):
ROOT="$(drawio-ai root)"
Build with the declarative layout engine (NO hand-written coordinates), then:
drawio-ai validate <file> → drawio-ai render <file> -o <file>.png (Read
the PNG for the vision self-check) → write the .drawio to an absolute path
under the user's project (never the Kit, never cwd).
Domain notes
Logical layers: medallion architecture Bronze (raw) → Silver (cleaned) → Gold (business-ready). Deployment split: the Databricks control plane is managed
by Databricks (no diagram representation needed); the data plane (compute)
lives in the customer's cloud account via PrivateLink or VNet injection — show
it nested inside the customer's VPC/cloud boundary. Unity Catalog governs
metadata across workspaces.
Self-check (before delivering)
