chart-rendering
Visualise the result of an analysis as a chart (line, bar, area, scatter, etc.). Use when the user asks to "plot...", "chart...", "show me the trend of...", "visualise...", or when a numerical result has more than ~10 rows and would be easier to read as a picture. Produces an image file plus the script that generated it.
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- 2026年5月26日 16:01
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ソースの指示 · 読み取り専用プレビュー- name
- chart-rendering
- description
- Visualise the result of an analysis as a chart (line, bar, area, scatter, etc.). Use when the user asks to "plot...", "chart...", "show me the trend of...", "visualise...", or when a numerical result has more than ~10 rows and would be easier to read as a picture. Produces an image file plus the script that generated it.
# Chart Rendering Skill
Repeatable SOP for turning a result set into a chart that actually communicates
the point.
## Steps
1. **Match chart type to question.** Decide *before* drawing:
| Question shape | Chart |
|----------------|-------|
| "How does X change over time?" | Line (single series) or multi-line |
| "Composition / share of total" | Stacked bar, or 100% stacked area |
| "Comparison across a small set of categories" | Bar (horizontal if labels are long) |
| "Relationship between two numeric variables" | Scatter |
| "Distribution of a single variable" | Histogram or box plot |
| "Cumulative total" | Area |
If none fit, ask the user which view they want — do not default to "any chart
will do".
2. **Prepare the data.** The query result from the [[sql-analysis]] Skill should
already be tidy (one row per data point, one column per dimension). If not,
reshape first using pandas (`pivot`, `melt`, `groupby`). Save the cleaned
frame to `knowledge/cache/<topic>.csv` so it can be re-rendered.
3. **Render with matplotlib.** Write the script to a file under
`scratch/charts/<topic>.py` and execute it via `shell_run`. Conventions:
- **Title** — one sentence answering the question (not "Untitled chart").
- **Axes** — label both, include units (`Active users (count)`,
`Date (UTC)`).
- **Legend** — include only if there is more than one series.
- **Colours** — use the matplotlib default palette unless the user has a
stated brand colour in `knowledge/style.md`.
- **Output** — save as PNG (`bbox_inches='tight'`, `dpi=150`) under
`knowledge/charts/<topic>.png`.
4. **Show and explain.** In the assistant reply, link the saved image with a
markdown image embed (``) and write
**2–3 sentences interpreting it** — what the chart shows, the one thing the
user should take away, and any caveat (incomplete latest data point,
outliers excluded, etc.).
5. **Keep the script reproducible.** The chart file is the artefact; the script
under `scratch/charts/<topic>.py` is the source of truth. If the user asks
for a tweak ("can we use a log y-axis?"), edit the script and re-run — do
not regenerate from scratch.
## Anti-patterns
- ❌ Pie charts with > 5 slices.
- ❌ Charting raw counts when proportions are what the user actually asked.
- ❌ A chart whose axes have no labels or unit.
- ❌ Embedding the image without 2 sentences of interpretation.
## When to delegate
If the user wants a multi-chart dashboard with narrative around it (e.g. "build
me a weekly product health deck"), **spawn the `report-writer` sub-agent** —
this Skill is for individual charts.
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