# Chart Builder > Use this skill whenever the user asks to visualize, chart, plot, or graph data (bar, line, scatter, histogram, pie) from a CSV, table, or DataFrame. It gives the agent prebuilt, parameterized matplotlib functions so charts are produced faster and more reliably — with consistent styling and sane defaults — instead of hand-writing matplotlib each time. - Skill: `kody-w/chart-builder` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add kody-w/chart-builder` - Raw SKILL.md: https://api.skillmd.com/api/skills/kody-w/chart-builder/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: kody-w (https://skillmd.com/u/kody-w) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kody-w/chart-builder --- Generate charts from tabular data via the bundled `scripts/charts.py` toolkit. Each function takes a DataFrame or a file path plus the columns to plot, and saves a PNG (returning the path). It handles theming, figure sizing, label rotation, NaN dropping, legend placement, and saving. ## Instructions 1. Provide the data source: a pandas DataFrame, or a path to a `.csv` / `.tsv` / `.json` file. 2. Call the function for the chart you need, passing the column names: - `bar(data, x, y)` — one value per category (`horizontal=True` for barh) - `grouped_bar(data, x, y, group)` — value per category split by `group` (`stacked=True` to stack) - `line(data, x, y, group=None)` — value over an ordered axis, one line per `group` - `scatter(data, x, y, group=None, size=None)` — two numeric columns - `histogram(data, y, bins=20, group=None)` — distribution of one column - `pie(data, x, y, donut=False)` — share of `y` per category `x` 3. Optional keyword args on every chart: `title`, `xlabel`, `ylabel` (derived from column names if omitted), `out` (default `"chart.png"`; pass `None` to skip saving), `dpi` (default `150`), `figsize`, `palette`. 4. The function returns the saved image path. See `references/cheatsheet.md` for full signatures and a chart-selection table. ## Usage Import: ```python from charts import bar, line, pie bar("sales.csv", x="region", y="revenue", title="Revenue by region", out="revenue.png") ``` CLI: ```bash python scripts/charts.py grouped_bar sales.csv \ --x region --y revenue --group quarter --stacked --out by_quarter.png ``` ## Bundled files - `scripts/charts.py` — the toolkit: `load_data`, `apply_theme`, `save_fig`, and six chart functions (`bar`, `grouped_bar`, `line`, `scatter`, `histogram`, `pie`), plus a CLI. Depends on `matplotlib` and `pandas`; runs headless. - `references/cheatsheet.md` — chart-selection table, full signatures, and copy-paste CLI examples. - `assets/sample_sales.csv` — a small demo dataset that exercises every chart. ## Defaults - Colorblind-friendly palette and a shared theme across charts. - Figure size, x-label rotation, and legend placement adapt to the data. - Rows with missing values in the plotted columns are dropped before drawing. - Output is a saved PNG at 150 DPI by default (override `out` and `dpi`). ## Run this — do not improvise This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as `chart_builder_agent.py` and embedded as the fenced Python below (sha256 f0e78a1c34b818da…; a byte-exact copy is also vaulted in the capsule comment at the end of this file). On a host with sandbox execution, run the linked file directly — if it is missing, write the fence contents verbatim to `chart_builder_agent.py` first: ```bash python3 chart_builder_agent.py '{"key": "value"}' # arguments as one JSON object echo '{"key": "value"}' | python3 chart_builder_agent.py # or on stdin python3 chart_builder_agent.py --tool # emit the JSON tool contract ``` Treat stdout as a tool result. If it reports missing or unresolved inputs, stop and collect them. If it returns `steps`, execute those steps in order exactly as returned; if it returns `instructions`, follow them with the supplied inputs. Otherwise use the result verbatim. Do not invent behavior beyond that output. On a host without code execution, treat the Parameters schema and the code below as the exact specification and never paraphrase a step. Never edit inside the generated markers; a converter-equipped host can instead restore the original file checksum-verified with the installed `rapp-agent-converter/scripts/toast.py convert SKILL.md --to agent`. ````python # rapp:deterministic """ChartBuilder -- Use this skill whenever the user asks to visualize, chart, plot, or graph data (bar, line, scatter, histogram, pie) from a CSV, table, or DataFrame. It gives the agent prebuilt, parameterized matplotlib functions so charts are produced faster and more reliably — with consistent styling and sane defaults — instead of hand-writing matplotlib each time. Generated by the rapp skill from chart-builder. The RCI capsule at the bottom of this file carries the full original; `toast.py convert` restores it byte-exact.""" import json import re import sys try: from agents.basic_agent import BasicAgent except ImportError: # running OUTSIDE a brainstem -- stay executable anyway. class BasicAgent: # noqa: D101 - minimal stand-in, same contract def __init__(self, name=None, metadata=None): if name: self.name = name if metadata: self.metadata = metadata def perform(self, **kwargs): return "Not implemented." def system_context(self): return None def to_tool(self): return {"type": "function", "function": { "name": self.name, "description": self.metadata.get("description", ""), "parameters": self.metadata.get("parameters", {})}} # The procedural layer, verbatim from the source capability. INSTRUCTIONS = 'Generate charts from tabular data via the bundled `scripts/charts.py` toolkit.\nEach function takes a DataFrame or a file path plus the columns to plot, and\nsaves a PNG (returning the path). It handles theming, figure sizing, label\nrotation, NaN dropping, legend placement, and saving.\n\n## Instructions\n1. Provide the data source: a pandas DataFrame, or a path to a `.csv` / `.tsv` /\n `.json` file.\n2. Call the function for the chart you need, passing the column names:\n - `bar(data, x, y)` — one value per category (`horizontal=True` for barh)\n - `grouped_bar(data, x, y, group)` — value per category split by `group`\n (`stacked=True` to stack)\n - `line(data, x, y, group=None)` — value over an ordered axis, one line per\n `group`\n - `scatter(data, x, y, group=None, size=None)` — two numeric columns\n - `histogram(data, y, bins=20, group=None)` — distribution of one column\n - `pie(data, x, y, donut=False)` — share of `y` per category `x`\n3. Optional keyword args on every chart: `title`, `xlabel`, `ylabel` (derived\n from column names if omitted), `out` (default `"chart.png"`; pass `None` to\n skip saving), `dpi` (default `150`), `figsize`, `palette`.\n4. The function returns the saved image path. See `references/cheatsheet.md` for\n full signatures and a chart-selection table.\n\n## Usage\nImport:\n```python\nfrom charts import bar, line, pie\nbar("sales.csv", x="region", y="revenue", title="Revenue by region",\n out="revenue.png")\n```\n\nCLI:\n```bash\npython scripts/charts.py grouped_bar sales.csv \\\n --x region --y revenue --group quarter --stacked --out by_quarter.png\n```\n\n## Bundled files\n- `scripts/charts.py` — the toolkit: `load_data`, `apply_theme`, `save_fig`, and\n six chart functions (`bar`, `grouped_bar`, `line`, `scatter`, `histogram`,\n `pie`), plus a CLI. Depends on `matplotlib` and `pandas`; runs headless.\n- `references/cheatsheet.md` — chart-selection table, full signatures, and\n copy-paste CLI examples.\n- `assets/sample_sales.csv` — a small demo dataset that exercises every chart.\n\n## Defaults\n- Colorblind-friendly palette and a shared theme across charts.\n- Figure size, x-label rotation, and legend placement adapt to the data.\n- Rows with missing values in the plotted columns are dropped before drawing.\n- Output is a saved PNG at 150 DPI by default (override `out` and `dpi`).' # Ordered commands lifted verbatim from the capability's own documentation. STEPS = [] class ChartBuilderAgent(BasicAgent): def __init__(self): self.name = 'ChartBuilder' self.metadata = { "name": "ChartBuilder", "description": "Use this skill whenever the user asks to visualize, chart, plot, or graph data (bar, line, scatter, histogram, pie) from a CSV, table, or DataFrame. It gives the agent prebuilt, parameterized matplotlib functions so charts are produced faster and more reliably \u2014 with consistent styling and sane defaults \u2014 instead of hand-writing matplotlib each time.", "parameters": { "type": "object", "properties": {}, "required": [] } } super().__init__(name=self.name, metadata=self.metadata) def perform(self, **kwargs): # toaster:generated-perform return json.dumps({"status": "ok", "instructions": INSTRUCTIONS, "inputs": kwargs, "note": "Prose-only capability: follow INSTRUCTIONS " "with the given inputs."}, indent=2) if __name__ == "__main__": # echo '{"arg": "value"}' | python3 chart_builder_agent.py # python3 chart_builder_agent.py '{"arg": "value"}' # python3 chart_builder_agent.py --tool # emit the JSON tool contract _a = sys.argv[1:] if _a and _a[0] == "--tool": print(json.dumps(ChartBuilderAgent().to_tool(), indent=2)) else: _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}") print(ChartBuilderAgent().perform(**json.loads(_raw))) # rci-capsule:v1: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 ````