Data Visualization
Create effective data visualizations with matplotlib, seaborn, and plotly. Use when building charts, dashboards, or communicating data insights visually.
Install
$ agentstack add skill-ihatesea69-kiro-kit-data-visualization ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-ihatesea69-kiro-kit-data-visualization) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Data Visualization
Activate this skill when creating charts, plots, or visual data presentations.
When to Use
- Creating exploratory data analysis plots
- Building publication-quality figures
- Designing interactive dashboards
- Communicating model results visually
- Comparing distributions and relationships
Libraries
- matplotlib: Foundation, full control
- seaborn: Statistical visualization, clean defaults
- plotly: Interactive charts, dashboards
- altair: Declarative, grammar of graphics
Patterns
import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.histplot(data=df, x="value", hue="category", ax=axes[0])
sns.scatterplot(data=df, x="feature_1", y="target", ax=axes[1])
plt.tight_layout()
plt.savefig("analysis.png", dpi=150, bbox_inches="tight")Rules
- Always label axes and add titles
- Use colorblind-friendly palettes
- Choose chart type based on data relationship
- Keep visualizations simple and focused
- Save figures at appropriate resolution (150+ DPI)
- Include units in axis labels
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ihatesea69
- Source: ihatesea69/kiro-kit
- License: MIT
- Homepage: https://www.npmjs.com/package/kiro-kit
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.