drawio-databricks

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. Delegate the build (preferred when your harness supports it)

If your harness can spawn autonomous subagents that run shell commands AND read images (e.g. Claude Code's Task tool, a general-purpose agent), run the whole build loop in a subagent — the rules, icon searches, and every render/fix iteration then cost this conversation nothing. If it can't (or the subagent can't read images), skip to Inline path below — same loop, same rules.

Before spawning, resolve what the subagent cannot ask about: diagram scope, output directory (absolute path under the user's project), filename. Run the preflight above yourself. For a multi-diagram request, spawn one subagent per diagram in parallel with distinct filenames.

Model routing — if your harness lets you choose the subagent's model, route by task weight: a fast/cheap tier (Claude Haiku-class — must support vision) when the request matches a template from the rules' Templates table (reproduction is mechanical; the validator's advice strings teach every fix), your default strong model for free-hand or novel architectures. If a cheap subagent returns VALIDATE not ok or ITERATIONS > 3, respawn ONCE on the strong model before taking over inline. Multi-diagram requests: route each diagram independently.

Subagent prompt (fill every <...>):

Build a Databricks lakehouse architecture .drawio diagram with the drawio-ai CLI.
Request: <user's request + clarifications, verbatim>
Output: <ABS_PROJECT_DIR>/<NAME>.drawio — never write inside the Kit, never into cwd.
Follow exactly:
1. Set ROOT="$(drawio-ai root)". Read $ROOT/docs/api-cheatsheet.md — the full layout-engine
   API in one file; never read library source.
2. Run `drawio-ai workflow` and `drawio-ai principles --mode databricks` — the source of
   truth. (Fallback if a command is blocked: read $ROOT/rules/*.md directly.)
3. Look up every icon with ONE batched `drawio-ai search "a, b, c"`; never recolor icons.
4. Scaffold, don't write: `drawio-ai scaffold --list`, pick the closest template, then
   `drawio-ai scaffold <name>.mjs -o <dir>/build.mjs` — the script arrives runnable and
   self-checking. Edit only the deltas; Write a new script only if no template is close
   AND you'd change more than half of it.
5. Each `node build.mjs` run prints validate JSON AND the render's machine-readable
   `issues` list. Fix from THAT checklist — all issues in one Edit round — then re-run.
   Loop until issues is empty.
6. Only when issues is empty: Read the PNG once as final visual confirmation. Target <= 2
   PNG reads total. Then render once WITHOUT --check for the final deliverable PNG.
Do NOT invoke any drawio skill — this prompt already contains the full procedure.
Do not ask questions — make the standard choice and record it under ASSUMPTIONS.
Return EXACTLY this block, nothing else:
DRAWIO: <absolute path to .drawio>
PNG: <absolute path to .png>
VALIDATE: <verbatim final validate JSON>
ICONS: <comma-separated icon names used>
ITERATIONS: <number of render/fix cycles>
SUMMARY: <one sentence describing the diagram>
ASSUMPTIONS: <choices made without asking, or "none">

Relay DRAWIO, PNG and SUMMARY to the user verbatim; do NOT re-read the .drawio or PNG in this conversation — the subagent already ran the vision self-check. If VALIDATE is not ok, take over via the Inline path (the build .mjs and .drawio are on disk at the returned paths).

Inline path (no subagent support)

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)"     # absolute path to the installed Kit

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)

• Built with the layout engine — no hand-written coordinates.
• drawio-ai validate → ok, no warnings, no advice.
• Every icon came from drawio-ai search (category colors intact).
• drawio-ai render vision self-check passed.
• Output written under the user's project, not the Kit.