plotly-example-1-3d-scatter-plot

Sub-skill of plotly: Example 1: 3D Scatter Plot (+5).

5 stars

Best use case

plotly-example-1-3d-scatter-plot is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of plotly: Example 1: 3D Scatter Plot (+5).

Teams using plotly-example-1-3d-scatter-plot should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/example-1-3d-scatter-plot/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/visualization/plotly/example-1-3d-scatter-plot/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/example-1-3d-scatter-plot/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How plotly-example-1-3d-scatter-plot Compares

Feature / Agentplotly-example-1-3d-scatter-plotStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Sub-skill of plotly: Example 1: 3D Scatter Plot (+5).

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Example 1: 3D Scatter Plot (+5)

## Example 1: 3D Scatter Plot


```python
import plotly.express as px
import numpy as np

# Generate 3D data
n = 500
x = np.random.randn(n)
y = np.random.randn(n)
z = np.random.randn(n)
colors = np.random.rand(n)

*See sub-skills for full details.*

## Example 2: Statistical Box Plot with Violin


```python
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd

df = pd.read_csv('../data/processed/measurements.csv')

# Create subplots
fig = make_subplots(
    rows=1, cols=2,

*See sub-skills for full details.*

## Example 3: Animated Time Series


```python
import plotly.express as px
import pandas as pd

df = pd.read_csv('../data/processed/timeseries_multi.csv')

fig = px.line(
    df,
    x='date',
    y='value',

*See sub-skills for full details.*

## Example 4: Interactive Heatmap with Annotations


```python
import plotly.graph_objects as go
import numpy as np

# Generate correlation matrix
data = np.random.rand(10, 10)
labels = [f'Var {i+1}' for i in range(10)]

fig = go.Figure(data=go.Heatmap(
    z=data,

*See sub-skills for full details.*

## Example 5: Multi-Axis Chart


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

fig = make_subplots(specs=[[{"secondary_y": True}]])

# Temperature (primary y-axis)
fig.add_trace(
    go.Scatter(x=[1, 2, 3, 4, 5], y=[20, 22, 25, 23, 24],
               name="Temperature (°C)", mode='lines+markers'),

*See sub-skills for full details.*

## Example 6: Large Dataset with WebGL


```python
import plotly.graph_objects as go
import numpy as np

# Generate large dataset (100,000 points)
n = 100000
x = np.random.randn(n)
y = np.random.randn(n)

# Use Scattergl for performance

*See sub-skills for full details.*

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