Agent-Skills.md

Agent Skills: Plotly Essentials

Plotly visualization patterns for statistical and scientific charts. Use when creating interactive visualizations, statistical plots (scatter, box, violin, heatmaps), UpSet plots for set intersections, network graphs, or exporting figures to HTML/PNG/PDF/SVG formats. Covers both Plotly Express (high-level) and Graph Objects (low-level) APIs.

UncategorizedID: CodingKaiser/claude-kaiser-skills/plotly

Repository

CodingKaiser/claude-kaiser-skills
CodingKaiser
31

Install this agent skill to your local

pnpm dlx add-skill https://github.com/CodingKaiser/kaiser-skills/tree/HEAD/plotly

Skill Files

Browse the full folder contents for plotly.

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plotly/SKILL.md

Skill Metadata

Name
plotly-compact
Description
Compact Plotly visualization patterns. Express for quick plots, Graph Objects for control.

Plotly Essentials

Express vs Graph Objects

  • Express: Quick exploratory plots from DataFrames
  • Graph Objects: Fine-grained control, complex multi-trace figures

Common Charts (Express)

import plotly.express as px

px.scatter(df, x="x", y="y", color="cat", size="val", hover_data=["name"])
px.line(df, x="date", y="value", color="series")
px.bar(df, x="cat", y="val", color="group", barmode="group")  # or "stack"
px.histogram(df, x="val", color="group", barmode="overlay", opacity=0.7)
px.box(df, x="group", y="val", points="all")
px.violin(df, x="group", y="val", box=True)
px.imshow(corr_matrix, text_auto=".2f", color_continuous_scale="RdBu_r")
px.scatter_matrix(df, dimensions=["a", "b", "c"], color="cat")

Graph Objects (when needed)

import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode="lines+markers", name="series"))
fig.update_layout(title="Title", template="plotly_white")
fig.update_traces(marker=dict(size=10))

Subplots

fig = make_subplots(rows=2, cols=2, subplot_titles=["A", "B", "C", "D"])
fig.add_trace(go.Scatter(x=x, y=y), row=1, col=1)

# Secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Scatter(...), secondary_y=False)
fig.add_trace(go.Bar(...), secondary_y=True)

Customization

fig.update_layout(
    template="plotly_white",  # or plotly_dark, ggplot2, seaborn
    legend=dict(orientation="h", yanchor="bottom", y=1.02)
)
fig.add_hline(y=threshold, line_dash="dash")
fig.add_annotation(x=2, y=5, text="Note", showarrow=True)
fig.update_xaxes(type="log", title="X Label")

Export

fig.write_html("plot.html")
fig.write_image("plot.png", scale=2)  # requires kaleido
fig.write_image("plot.svg")

Network Graphs

import networkx as nx

G = nx.karate_club_graph()
pos = nx.spring_layout(G, seed=42)

# Edges
edge_x, edge_y = [], []
for u, v in G.edges():
    x0, y0 = pos[u]; x1, y1 = pos[v]
    edge_x.extend([x0, x1, None])
    edge_y.extend([y0, y1, None])

edge_trace = go.Scatter(x=edge_x, y=edge_y, mode="lines", line=dict(width=0.5, color="#888"))
node_trace = go.Scatter(x=[pos[n][0] for n in G.nodes()], y=[pos[n][1] for n in G.nodes()],
                        mode="markers", marker=dict(size=10, color=list(dict(G.degree()).values())))

fig = go.Figure([edge_trace, node_trace])
fig.update_layout(showlegend=False, xaxis=dict(showgrid=False, showticklabels=False),
                  yaxis=dict(showgrid=False, showticklabels=False))

Tips

  • Large data: px.scatter(..., render_mode="webgl")
  • Colorblind-safe: color_discrete_sequence=px.colors.qualitative.Safe