| name | tldraw |
| description | Generate hand-drawn whiteboard-style diagrams (.tldr) from natural language and export to PNG/SVG via tldraw-cli, with vision-based self-check and an iterative review loop. Covers flowcharts, sequence diagrams, ERDs, UML sketches, and ML model diagrams. Triggers on: "whiteboard diagram", "sketch this out", "tldraw diagram", "hand-drawn diagram", "flowchart", "sequence diagram", "ERD diagram", "UML sketch", "ML model diagram", "draw a diagram". NOT for polished business/infra diagrams, use architecture-diagram. |
| metadata | {"version":"1.0.0","category":"visualization","tags":["diagram","whiteboard","tldraw","flowchart","sequence-diagram","erd","uml","visualization"],"difficulty":"intermediate","complements":["architecture-diagram"]} |
tldraw Whiteboard Diagrams
Overview
Generate hand-drawn whiteboard-style diagrams as .tldr JSON files and export to PNG/SVG using @kitschpatrol/tldraw-cli. tldraw produces a hand-drawn aesthetic with rich shape libraries and smooth arrow routing — well-suited for casual, sketch-style visualizations rather than polished business diagrams.
Format: .tldr JSON
Export: PNG, SVG (via @kitschpatrol/tldraw-cli)
Aesthetic: Hand-drawn whiteboard style by default; switchable to clean fonts via font prop.
When to Use
| User need | Use tldraw | Use instead |
|---|
| Whiteboard / hand-drawn-style flowchart, sequence, ERD, or UML sketch | Yes | — |
| ML / deep-learning model diagram with tensor-shape annotations | Yes | — |
Freehand or figurative sketching (the draw shape) | Yes | — |
| Polished business/infra/deployment architecture diagram | No | architecture-diagram |
| Logos, solid-color graphics, or filled icons | No | tldraw has no opaque fill (solid = light tint); use the original vector source |
| Interactive HTML dashboard or infographic | No | static-web-artifacts-builder |
| Data charts, plots, sparklines | No | chart-clarity |
Proactive triggers: explaining a system with 3+ interacting components, describing a multi-step process or data flow, showing relationships between services/modules, decision trees, or ML model layers.
Skip when: a simple list or table suffices, or the user is in a quick Q&A flow.
Known constraints (route elsewhere or set expectations if these matter):
- No opaque fill —
solid renders as a light tint, so white-on-dark artwork can't be reproduced.
- Manual coordinates only — no automatic layout of many nodes.
- Arrowheads are filled triangles/diamonds, not the hollow heads strict UML notation uses.
- PDF export isn't supported by
tldraw-cli (PNG/SVG only).
Prerequisites
Uses @kitschpatrol/tldraw-cli — a third-party, MIT-licensed export tool maintained independently of the tldraw.dev project. It is not the official create-tldraw project-scaffolding CLI; don't confuse the two. It renders .tldr files to PNG/SVG via a headless Chrome instance (puppeteer).
npm install -g @kitschpatrol/tldraw-cli
tldraw --version
Works identically on macOS, Windows, and Linux.
First-export note: tldraw export renders through a pinned Chrome build via puppeteer. The first export can fail with Could not find Chrome (ver. <x>). The error names the exact version it needs — install it once, then exports work:
npx puppeteer browsers install chrome@<version-from-error>
(Installs to ~/.cache/puppeteer; only needed once per CLI version.)
Workflow
Before starting, assess whether the user's request is specific enough. If key details are missing, ask 1-3 focused questions:
- Diagram type — which preset? (Architecture, Flowchart, Sequence, ML/DL, ERD, UML, or general)
- Output format — PNG (default), SVG?
- Output location — default is the user's working dir; honor any explicit path the user gives (e.g. "put it in
./artifacts/"). Don't ask if they didn't mention one.
- Scope/fidelity — how many components? Any specific technologies or labels?
Skip clarification if the request already specifies these details or is clearly simple (e.g., "draw a flowchart of X").
- Check deps — verify
tldraw --version succeeds; if missing, run npm install -g @kitschpatrol/tldraw-cli.
- Plan — identify shapes (geo type per node), connections (arrows with source/target), and layout (TB or LR, group by tier/role). Sketch a coordinate grid before writing JSON. See
references/diagram-presets.md for layout rules, index ordering, and per-diagram-type shape/color conventions.
- Generate — write the
.tldr JSON file using the record formats in references/tldr-format.md (shapes) and references/arrows-and-containers.md (arrows, frames, notes). Default output dir is the user's working dir; if the user specified a path or directory (e.g. ./artifacts/), mkdir -p it first and write there. Apply the same dir choice to PNG/SVG exports in steps 4 and 7.
- Export draft — run CLI to produce a PNG for preview. See
references/troubleshooting.md for export command syntax.
- Self-check — use the agent's built-in vision capability to read the exported PNG, catch obvious issues, auto-fix before showing the user (requires a vision-enabled model such as Claude Sonnet/Opus). If vision is unavailable, skip this step.
- Review loop — show image to user, collect feedback, apply targeted JSON edits, re-export, repeat until approved.
- Final export — export the approved version to all requested formats; report file paths for both the
.tldr source and exported image(s).
Step 5: Self-Check
After exporting the draft PNG, use the agent's vision capability (e.g., Claude's image input) to read the image and check for these issues before showing the user. If the agent does not support vision, skip self-check and show the PNG directly.
tldraw's own AI agent flags exactly three structural defects — text overflow (a box too small for its label), overlapping text, and friendless arrows (an arrow with an unbound end). The first three rows below target those; size boxes correctly up front (see the sizing formula in references/diagram-presets.md) and they rarely occur.
| Check | What to look for | Auto-fix action |
|---|
| Text overflow | Label spills past the shape's border, or the box looks taller than you set (tldraw auto-grows an undersized box) | Increase w/h to fit the label — see the sizing formula in references/diagram-presets.md |
| Overlapping text | Two text-bearing shapes' labels touch or overlap, hurting legibility | Shift shapes apart by ≥200px |
| Friendless arrow | An arrow with one end not connected to a shape (floats loose) | Bind both ends: every arrow's start and end need a boundShapeId matching an existing shape |
| Off-canvas shapes | Shapes at negative coordinates or far from the main group | Move to positive coordinates near the cluster |
| Arrow-shape overlap | An arrow visually crosses through an unrelated shape | Adjust bend value or move endpoints to a different normalizedAnchor side |
| Stacked arrows | Multiple arrows overlap each other on the same path | Distribute normalizedAnchor across the shape perimeter (use different x/y values) |
- Max 2 self-check rounds — if issues remain after 2 fixes, show the user anyway.
- Re-export after each fix and re-read the new PNG.
Step 6: Review Loop
After self-check, show the exported image and ask the user for feedback.
Targeted edit rules — for each type of feedback, apply the minimal JSON change:
| User request | JSON edit action |
|---|
| Change color of X | Find shape by props.text matching X, update props.color |
| Add a new node | Append a new shape record with next available index, position near related nodes |
| Remove a node | Delete the shape record and any arrow records bound to it |
| Move shape X | Update the shape's x/y fields |
| Resize shape X | Update props.w/props.h |
| Add arrow from A to B | Append a new arrow record binding to A and B's shape ids |
| Change label text | Update props.text on the matching shape or arrow |
| Change layout direction | Full regeneration — replan the grid and rebuild |
Rules:
- For single-element changes: edit the existing JSON in place — preserves layout tuning from prior iterations.
- For layout-wide changes (e.g., swap LR↔TB, "start over"): regenerate full JSON.
- Overwrite the same
{name}.png each iteration — do not create v1, v2, v3 files.
- After applying edits, re-export and show the updated image.
- Loop continues until user says approved / done / LGTM.
- Safety valve: after 5 iteration rounds, suggest the user open the
.tldr file in tldraw.com or the desktop app for fine-grained adjustments.
References
references/tldr-format.md — .tldr file skeleton, geo shape record, geo types, color palette, style optionsreferences/arrows-and-containers.md — arrow record, connection rules, arrowheads, distributing arrows, frames, notesreferences/diagram-presets.md — index ordering, layout tips, box-sizing formula, per-diagram-type shape/color presets (architecture, flowchart, sequence, ML, ERD, UML)references/troubleshooting.md — export commands, common mistakes, fallback chain when tools/vision are unavailablereferences/upstream/provenance.md — upstream source, pinned commit, what was vendored/adapted/skipped
This skill adapts Agents365-ai's MIT-licensed tldraw-skill into armory as tldraw.
