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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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agentscope-ai/agentscope-java
Dernière activité de la source
26 mai 2026 à 16:01
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SKILL.md
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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 (`![title](knowledge/charts/<topic>.png)`) 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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