render

name	render
description	Render a SmartArt graphic to PNG using SVG, Mermaid, Vega-Lite, or matplotlib. Accepts a structured spec and produces a rasterised PNG.
argument-hint	--spec-file PATH | --graphic-type TYPE --items "item1;item2" [--output PATH] [--width INT] [--height INT]
allowed-tools	Bash(python *), Bash(npx *)

/render

Render a SmartArt graphic to PNG using the appropriate engine (custom_svg, mermaid, vega_lite, or matplotlib).

Step 1: Locate plugin root

PLUGIN_ROOT=$(python3 -c "
from pathlib import Path
import sys, os
if os.environ.get('JACK_TAR_CUSTOM_SMARTART_ROOT'):
    print(os.environ['JACK_TAR_CUSTOM_SMARTART_ROOT']); sys.exit()
home = Path.home()
for base in [home / '.claude' / 'plugins' / 'cache']:
    for p in base.rglob('jack-tar-custom-smartart/.claude-plugin/plugin.json'):
        print(str(p.parent.parent)); sys.exit()
dev = Path.cwd() / 'plugins' / 'jack-tar-custom-smartart'
if dev.exists():
    print(str(dev)); sys.exit()
print('NOT_FOUND')
" 2>/dev/null)
if [ -z "$PLUGIN_ROOT" ] || [ "$PLUGIN_ROOT" = "NOT_FOUND" ]; then echo "ERROR: jack-tar-custom-smartart not found" && exit 1; fi
echo "PLUGIN_ROOT=$PLUGIN_ROOT"

Step 2: Resolve spec

If --spec-file PATH was given, read the spec JSON from that file.

If inline arguments were given (--graphic-type, --items, etc.), build a minimal spec:

import json, sys, os

# Inline spec construction example
graphic_type = "$GRAPHIC_TYPE"   # e.g. "flowchart"
items_raw = "$ITEMS"             # semicolon-separated, e.g. "Plan;Build;Ship"
output_path = "$OUTPUT_PATH"     # default: /tmp/<graphic_type>.png
width = int("$WIDTH") if "$WIDTH" else 1920
height = int("$HEIGHT") if "$HEIGHT" else 1080

items = [i.strip() for i in items_raw.split(";") if i.strip()]

spec = {
    "slide_number": 1,
    "graphic_type": graphic_type,
    "engine": "custom_svg",        # default; override in spec file for mermaid/vega_lite/matplotlib
    "enrichment_tier": "pure_programmatic",
    "data": {"items": items},
    "comparator_engines": [],
    "dimensions": {"width": width, "height": height},
    "alt_text": f"{graphic_type} diagram",
}

output_dir = os.path.dirname(output_path) or "/tmp"
print(json.dumps({"spec": spec, "output_dir": output_dir}))

If --spec-file was given, load the spec directly and extract output_dir from the spec's output_dir key, or default to /tmp.

Step 3: Render

PYTHONPATH="$PLUGIN_ROOT" python3 -c "
import json, sys, os

spec_json = '''$SPEC_JSON'''
spec = json.loads(spec_json)

output_dir = '$OUTPUT_DIR'
os.makedirs(output_dir, exist_ok=True)

# Minimal style_guide — renderer uses defaults when palette is absent
style_guide = {}
phase = 'draft'

from src.smartart_renderer import render
entry = render(spec, style_guide, phase, output_dir)

print(json.dumps(entry, indent=2))
"

Step 4: Report

Parse the returned manifest entry and report:

RENDER COMPLETE
  graphic_type:   flowchart
  engine_used:    custom_svg
  status:         rendered
  output:         /tmp/smartart_flowchart_slide1.png
  dimensions:     1920 x 1080
  alt_text:       flowchart diagram
  content_hash:   abc123...

If status is failed, report the failure clearly and suggest checking:

• The engine field matches an available engine (custom_svg, mermaid, vega_lite, matplotlib)
• The data field matches the engine's expected shape
• Node.js CLI tools are present (/verify to check)

Notes

• spec['engine'] must be one of: custom_svg, mermaid, vega_lite, matplotlib
• custom_svg supports graphic types: flowchart, decision_tree, timeline, venn, swot, feature_matrix, pipeline_funnel, radar_chart, gantt
• mermaid and vega_lite require Node.js CLIs — run /verify first if unsure
• data shape varies by engine:
  • custom_svg: {"items": ["Step 1", "Step 2", ...]} (flat list) or engine-specific keys
  • mermaid: {"diagram": "graph LR\n A --> B"} (raw Mermaid DSL)
  • vega_lite: {"spec": {...}} (Vega-Lite spec object)
  • matplotlib: {"labels": [...], "values": [...]} (chart data — prefer /chart skill for these)
• comparator_engines can be a list of additional engines to compare against; the manifest entry will include comparator_results
• enrichment_tier controls AI enrichment: pure_programmatic, ai_background, ai_element_icons, full_ai_render