Data Visualization Creator

Generate plots/charts/graphs from CSV or JSON via vl2svg (Vega-Lite), with column-type detection and chart-type recommendation.

When to use

• User wants a chart/plot/graph from tabular data (file or pasted table).
• Do not use for architecture/sequence diagrams → diagram.
• Do not use for generative art, posters, or non-data images.

Prerequisites

• python3 and this skill's main.py (path: skill directory next to this file).
• vl2svg on PATH: npm install -g vega-cli vega-lite.
• Files ≥10MB also need duckdb on PATH.

Procedure

Work phases in order. Do not skip. Do not invent column names — only use fields from analyze output (or exact user overrides that exist in that output).

Phase 1 — Materialize data

1. If data is already a file path the user gave, use it as --data_path.
2. If the user pasted a table / inline JSON/CSV, write it to a file in the session scratch directory (temp). Do not write into the project working directory unless the user explicitly asks to save there.
3. Completion: a real filesystem path exists and is readable.

Phase 2 — Analyze

1. Run (from skill dir or with absolute path to main.py):

   python3 <skill-dir>/main.py analyze --data_path <path>

2. Read the JSON stdout. On "status": "error", report reason and STOP (or fix path/format and re-run once).
3. Note recommended_chart_type, recommended_x, recommended_y, column type / cardinality / null_count. For large files note "engine": "duckdb" and size_mb.
4. Completion: you have recommended chart + axes (or a clear error reported).

Phase 3 — Choose encoding

1. Default to recommended chart/x/y from analyze.
2. If the user named a chart type or axes, prefer their choice only if those fields appear in columns. If not, re-check analyze and ask once.
3. Chart intents: bar = category vs measure; line = temporal vs measure; scatter = two numerics; pie = few categories (≤6) + measure.
4. Completion: concrete chart_type, x_axis, y_axis (y may be null only if analyze allowed it and user wants category counts — otherwise require y).

Phase 4 — Render

1. Pick --output_path in scratch (or user-requested path). Prefer .svg.

   python3 <skill-dir>/main.py render \
     --data_path <path> \
     --chart_type <bar|scatter|line|pie> \
     --x_axis <field> \
     --y_axis <field> \
     --output_path <out.svg>

2. On success JSON: keep output_path. On error JSON: report reason / stage; if invalid field, re-run Phase 2 — do not blind-retry.
3. If "aggregated": true, you must tell the user what was aggregated or sampled (aggregation field). Never imply every row was plotted.
4. Completion: SVG exists at output_path, or structured error reported.

Phase 5 — Respond

1. Report chart type, axes, and SVG path.
2. Mention aggregation/sampling when present.
3. PNG only if user asked: rasterize SVG separately (rsvg-convert or vl2png); this skill does not emit PNG by default.
4. Completion: user has path + one-line interpretation of the chart.

Edge cases (summary)

• Empty file / no rows → analyze error; stop and say so.
• Missing vl2svg / duckdb → report install hint from error JSON; stop.
• Unknown columns → list columns from analyze; do not guess.
• Wide tables: recommend using analyze picks; do not plot all columns at once.
• Details and decision table: load references/chart-selection.md only if recommendation is ambiguous or user asks why a type was chosen.

Examples

Happy path

• User: "Make a bar chart of revenue by region from sales.csv"
• You: analyze → render bar with region/revenue → return SVG path.

Inline data

• User pastes a markdown table → write temp CSV → analyze → render → SVG.

Non-trigger

• User: "Draw the checkout service architecture" → do not use this skill (use diagram).