visual-mockup

name	visual-mockup
version	2.1
description	This skill should be used when the user asks to 'mockup the layout', 'sketch the diagram', 'show me the layout before coding', 'draft the positions', 'quick visual of the layout', 'matplotlib mockup', 'interactive mockup', 'drag and drop layout', 'draw a rough layout', 'prototype the diagram', 'let me arrange the nodes', 'mockup the chart before CeTZ', or when you're about to write a diagram with 4+ nodes or a CeTZ chart and want to confirm the visual with the user first.
user-invocable	false

Announce: "I'll create a mockup so you can see the layout before I code the real diagram."

Why This Exists

Diagram code (CeTZ, Fletcher, TikZ) is slow to iterate on — you write coordinates, compile, discover the layout is wrong, rewrite. A 30-second sketch lets the user see and approve the spatial layout before any real code gets written. This saves 3-5 compile-fix cycles on complex diagrams.

When to Use

• Before coding a new diagram with 4+ nodes, regions, or non-trivial arrow routing
• Before coding a CeTZ chart where the data shape matters (stock prices, timelines)
• When the user describes a layout change and you want to confirm before implementing
• When an ASCII sketch isn't enough to convey spatial relationships (overlapping regions, diagonal arrows, nested containers)
• When you and the user are iterating on where things should go

You don't need this for simple diagrams (2-3 nodes in a line). Use your judgment — if the layout is obvious, skip the mockup and go straight to code.

Choosing a Mode

Mode	When	Output
Interactive (default for node diagrams)	Fletcher diagrams, 4+ nodes, precise positioning	Drag-and-drop HTML → Fletcher JSON
Matplotlib (for charts/plots)	CeTZ charts, stock prices, timelines, data series	Static PNG → iterate data → translate to CeTZ

Default to interactive for node-edge diagrams. Use matplotlib for anything with axes, data series, or time-based plots.

---

Interactive Mockup (Fletcher Diagrams)

1. Gather the Layout

From conversation context or the user's request, identify:

• Nodes: labeled boxes with approximate positions
• Edges: arrows between nodes, with labels and directionality
• Regions: background containers grouping nodes (dashed borders, light fills)
• Constraints: "X must be above Y", "no crossing arrows", "these two side by side"

2. Generate the Interactive HTML

Write the layout as JSON and run the generator:

cat > /tmp/layout.json << 'LAYOUT'
{
  "title": "Diagram Name",
  "nodes": [
    {"id": "n1", "label": "Issuer", "x": 200, "y": 100, "color": "#89b4fa"},
    {"id": "n2", "label": "Buyer", "x": 200, "y": 300, "color": "#a6e3a1"}
  ],
  "edges": [
    {"from": "n1", "to": "n2", "label": "§11 claim"}
  ],
  "regions": [
    {"id": "r1", "label": "Primary Market", "x": 140, "y": 60, "w": 250, "h": 280, "color": "#89b4fa"}
  ]
}
LAYOUT
uv run python3 ${SKILL_DIR}/scripts/interactive_mockup.py /tmp/layout.json --open

${SKILL_DIR} resolves to this skill's base directory (e.g., skills/visual-mockup).

The browser opens with a Catppuccin-themed drag-and-drop canvas:

• Drag nodes and regions to reposition
• Drag edge labels to offset from midpoint; R / Shift+R to rotate 15 degrees
• Double-click any element to rename its label
• "+ Node" / "+ Edge" / "+ Region" buttons to add elements
• Delete/Backspace to remove selected element
• Bottom-right handle on regions to resize
• "Export as" dropdown: Fletcher (grid coords, y-down) or Raw pixels
• "Copy Layout JSON" button copies transformed positions to clipboard

Best for Fletcher diagrams. The exported grid coords drop directly into node((x, y), ...) calls. CeTZ diagrams require manual arrow routing that the mockup can't capture — use matplotlib mode for CeTZ instead.

3. Get the Layout Back

Tell the user: "Drag nodes where you want them, select Fletcher in the Export dropdown, then click Copy Layout JSON and paste it here."

The user pastes JSON with Fletcher grid coordinates. Use these positions directly in node((x, y), ...) calls — no coordinate transform needed.

4. Iterate if Needed

If the user wants further changes, regenerate with updated JSON and --open again. When approved, proceed to real diagram code.

---

Matplotlib Mockup (CeTZ Charts & Simple Layouts)

1. Gather the Data

Identify the chart's data series, axes, annotations, and shaded regions. For node diagrams, identify nodes, edges, regions.

2. Generate the Mockup

Write a Python script that uses matplotlib/seaborn to sketch the visual:

For charts/plots:

• Plot data series with appropriate line styles
• Add shaded regions (class periods, lookback windows, etc.)
• Add annotations (damage brackets, labels, axis markers)
• Match the target palette if known

For node diagrams:

• matplotlib.patches.FancyBboxPatch with rounded corners for nodes
• ax.annotate with arrowprops for edges
• Low-alpha patches with dashed borders for regions

Output to /tmp/visual-mockup.png at 150 DPI, then open it:

plt.savefig('/tmp/visual-mockup.png', dpi=150, bbox_inches='tight')

open /tmp/visual-mockup.png

3. Iterate with the User

Show the mockup and ask if the data shape / layout is right. This is where the value lives — catching problems like "the stock price doesn't bounce back" or "the damage bracket is in the wrong place" takes seconds in matplotlib vs. minutes in CeTZ compile cycles.

When approved, translate to CeTZ. For charts, the data arrays transfer directly — matplotlib and CeTZ use the same coordinate space, just different syntax.

Style Guide (Matplotlib Mode)

• figsize=(12, 6) for side-by-side, (8, 6) for single diagrams
• boxstyle="round,pad=0.1" for nodes
• Use color to distinguish categories (red for danger/fraud, blue for normal flow)
• ax.set_aspect('equal') and ax.axis('off') for node diagrams

---

Mockup Facts

• Morrison: the layout you "knew" had crossing arrows the user spotted instantly; skipping the mockup produced 3+ failed compile-fix cycles. A 30-second sketch beats 3 failed compiles — skipping is slower, not faster.
• The value-line incident: editing CeTZ data arrays "real quick" without a mockup ("just shift the prices down") produced a chart that told the wrong story for weeks. Data-shape problems are invisible until compile; a 30-second matplotlib plot makes them visible to the user instantly.
• The mockup is a throwaway sketch — 2 minutes max, boxes/arrows/labels/data shape only. Polishing it is anti-helpful: the user is waiting for a sketch, and polish delays the real diagram.

Red Flags

• Spending 10+ minutes polishing the mockup → "good enough to discuss," then open it.
• Adding data, formulas, or precise styling → you're building the real diagram in the wrong tool.
• Skipping the mockup for a 4+ node diagram because "I know the layout" → sketch it.
• Going straight to CeTZ for a chart, or editing CeTZ data arrays without a mockup → matplotlib first, iterate with the user, then translate.

What This Skill is NOT

• Not a render-verify loop (use visual-verify for that)
• Not a replacement for the actual diagram code
• Not for standalone data visualization (use ds workflow for charts/plots)
• Not for pixel-perfect output — it's a spatial sketch for layout approval