vizdantic-runner

name	vizdantic-runner
description	Run Vizdantic visualization workflows in this repo: load user datasets (CSV/Parquet/JSON/Excel), draft and validate Vizdantic specs, and render Plotly charts. Use when the user asks to visualize data, generate charts from a file/DataFrame, or execute Python to validate/render Vizdantic schema outputs.

Vizdantic Runner

Overview

Turn user data and intent into a validated Vizdantic spec and a rendered Plotly figure using this repo's schema and plugins.

Workflow

1. Confirm data source, file path, intended chart/insight, and whether a custom theme is desired.
2. Inspect data minimally (columns, dtypes, head) to choose the correct spec fields.
3. Build a Vizdantic spec dict and validate it with vizdantic.validate.
4. Render with vizdantic.plugins.plotly.render.
5. Write outputs (HTML/PNG) and summarize what was generated.

Data loading rules

• Use pandas to read data.
• Supported formats: CSV, Parquet, JSON (including JSONL), Excel.
• Treat user data as read-only.
• For large files, avoid full loads when possible:
  • Check file size first.
  • Use --inspect with --nrows to view columns and a small sample.
  • Use --usecols to limit columns.
  • If the file is huge and limits are not supported (e.g., some JSON/Parquet cases), ask the user for a smaller sample or filtered extract.

Theme handling

• If the user wants a custom theme, ask how they want to provide it:
  • A quick description (brand colors, fonts, template name)
  • A JSON snippet for Plotly layout
  • A JSON file path
• Apply themes via fig.update_layout(...) using the script flags.
• To remember a theme for future runs, save it with --remember-theme (stored at artifacts/vizdantic/theme.json by default).
• Use references/theme-quickstart.json as a starting point when a user wants a custom theme but hasn't provided one.

Spec guidance

• Use references/vizdantic-spec-quickref.md for required fields and chart options.
• If the schema is needed, print it with a short Python snippet:

python - <<'PY'
from vizdantic import schema
import json
print(json.dumps(schema(), indent=2))
PY

Automation script

Use the bundled script for repeatable runs:

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --spec '{"kind":"cartesian","chart":"bar","x":"month","y":"revenue"}'

Apply a custom theme (layout JSON):

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --spec '{"kind":"cartesian","chart":"bar","x":"month","y":"revenue"}' \
  --theme '{"template":"plotly_dark","colorway":["#1f2937","#0ea5e9"],"font":{"family":"Inter"}}'

Remember the theme for future runs:

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --spec '{"kind":"cartesian","chart":"bar","x":"month","y":"revenue"}' \
  --theme-file /path/to/theme.json \
  --remember-theme

Use a built-in Plotly template name:

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --spec '{"kind":"cartesian","chart":"bar","x":"month","y":"revenue"}' \
  --theme-template plotly_dark

Start from the bundled quickstart theme:

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --spec '{"kind":"cartesian","chart":"bar","x":"month","y":"revenue"}' \
  --theme-file skills/vizdantic-runner/references/theme-quickstart.json

Inspect a large file safely (columns, dtypes, head):

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --inspect --nrows 200

Limit columns and rows:

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.parquet \
  --usecols month,revenue \
  --nrows 5000 \
  --spec '{"kind":"cartesian","chart":"line","x":"month","y":"revenue"}'

Write both HTML and PNG:

python skills/vizdantic-runner/scripts/render_viz.py \
  --data /path/to/data.csv \
  --spec-file /path/to/spec.json \
  --html --png \
  --output-dir artifacts/vizdantic

Dependency guardrails

• If plotly or pandas is missing, ask before installing.
• Preferred install for this repo: pip install -e '.[plotly]' pandas.

Output expectations

• Default output directory: artifacts/vizdantic/.
• Summarize: data source, spec used, output paths, and any validation fixes.