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Agent skill

CSV Data Visualizer

by ailabs-393 in ailabs-393/ai-labs-claude-skills

This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data…

MITAuto-check passedData & Analytics

Install CSV Data Visualizer

skills CLI
$ npx skills add ailabs-393/ai-labs-claude-skills --skill csv-data-visualizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ailabs-393/ai-labs-claude-skills csv-data-visualizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ailabs-393/ai-labs-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/csv-data-visualizer .claude/skills/csv-data-visualizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
csv-data-visualizer
GitHub stars
454
Token cost
~2.4k tokens
SKILL.md length
608 words
Files
7 (incl. scripts, references)
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data…

  • Works in 3 steps: Individual Visualizations → Automatic Data Profiling → Multi-Plot Dashboards
  • Tasks that involve Data analysis
  • SKILL.md covers Overview, When to Use This Skill, Core Capabilities and Workflow Decision Tree, plus 5 more sections
  • Runs Python and JavaScript scripts from its folder; calls python3 and pip

What it does

CSV Data Visualizer is an agent skill from ailabs-393/ai-labs-claude-skills. This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `index.js`, `package.json` and `references/visualization_guide.md`).

It sits in Data & Analytics, covering Data analysis, CSV and tabular files and Data visualization. It works with Plotly. The repository describes itself as: This package is use to remove the hustle of finding claudeskills and shift them into any of the user project. This project become a bridge between user's usage and claude skills. The licence is MIT.

When your agent uses it

  • Tasks that involve Data analysis
  • Tasks that involve CSV and tabular files
  • Tasks that involve Data visualization

Example prompts

  • “/csv-data-visualizer”

Requirements

  • Python 3
  • Node.js

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Individual Visualizations
  2. Automatic Data Profiling
  3. Multi-Plot Dashboards

What it can do on your machine

Read from SKILL.md and the folder at commit 1a12bc7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python and JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

CSV Data Visualizer loads about 2.4k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 608 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ailabs-393/ai-labs-claude-skills at commit 1a12bc7, republished under its MIT licence (© ailabs-393). 608 words, ~2,414 tokens.

Download SKILL.mdSave it as .claude/skills/csv-data-visualizer/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
csv-data-visualizer
description
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.

CSV Data Visualizer

Overview

This skill enables comprehensive data visualization and analysis for CSV files. It provides three main capabilities: (1) creating individual interactive visualizations using Plotly, (2) automatic data profiling with statistical summaries, and (3) generating multi-plot dashboards. The skill is optimized for exploratory data analysis, statistical reporting, and creating presentation-ready visualizations.

When to Use This Skill

Invoke this skill when users request:

  • "Visualize this CSV data"
  • "Create a histogram/scatter plot/box plot from this data"
  • "Show me the distribution of [column]"
  • "Generate a dashboard for this dataset"
  • "Profile this CSV file" or "Analyze this data"
  • "Create a correlation heatmap"
  • "Show trends over time"
  • "Compare [variable] across [categories]"

Core Capabilities

1. Individual Visualizations

Create specific chart types for detailed analysis using the visualize_csv.py script.

Available Chart Types:

Statistical Plots:

bash
# Histogram - distribution of numeric data
python3 scripts/visualize_csv.py data.csv --histogram column_name --bins 30

# Box plot - show quartiles and outliers
python3 scripts/visualize_csv.py data.csv --boxplot column_name

# Box plot grouped by category
python3 scripts/visualize_csv.py data.csv --boxplot salary --group-by department

# Violin plot - distribution with probability density
python3 scripts/visualize_csv.py data.csv --violin column_name --group-by category

Relationship Analysis:

bash
# Scatter plot with automatic trend line
python3 scripts/visualize_csv.py data.csv --scatter height weight

# Scatter plot with color and size encoding
python3 scripts/visualize_csv.py data.csv --scatter x y --color category --size value

# Correlation heatmap for all numeric columns
python3 scripts/visualize_csv.py data.csv --correlation

Time Series:

bash
# Line chart for single variable
python3 scripts/visualize_csv.py data.csv --line date sales

# Multiple variables on same chart
python3 scripts/visualize_csv.py data.csv --line date "sales,revenue,profit"

Categorical Data:

bash
# Bar chart (counts categories automatically)
python3 scripts/visualize_csv.py data.csv --bar category

# Pie chart for composition
python3 scripts/visualize_csv.py data.csv --pie region

Output Formats: Specify output file with desired format extension:

bash
# Interactive HTML (default)
python3 scripts/visualize_csv.py data.csv --histogram age -o output.html

# Static image formats
python3 scripts/visualize_csv.py data.csv --scatter x y -o plot.png
python3 scripts/visualize_csv.py data.csv --correlation -o heatmap.pdf
python3 scripts/visualize_csv.py data.csv --bar category -o chart.svg
2. Automatic Data Profiling

Generate comprehensive data quality and statistical reports using the data_profile.py script.

Text Report (default):

bash
python3 scripts/data_profile.py data.csv

HTML Report:

bash
python3 scripts/data_profile.py data.csv -f html -o report.html

JSON Report:

bash
python3 scripts/data_profile.py data.csv -f json -o profile.json

What the Profiler Provides:

  • File information (size, dimensions)
  • Dataset overview (shape, memory usage, duplicates)
  • Column-by-column analysis (types, missing data, unique values)
  • Missing data patterns and completeness
  • Statistical summary for numeric columns (mean, std, quartiles, skewness, kurtosis)
  • Categorical column analysis (frequency counts, most/least common values)
  • Data quality checks (high missing data, duplicate rows, constant columns, high cardinality)

When to Use Profiling: Always recommend running data profiling BEFORE creating visualizations when:

  • User is unfamiliar with the dataset
  • Data quality is unknown
  • Need to identify appropriate visualization types
  • Exploring a new dataset for the first time
3. Multi-Plot Dashboards

Create comprehensive dashboards with multiple visualizations using the create_dashboard.py script.

Automatic Dashboard: Analyzes data types and automatically creates appropriate visualizations:

bash
python3 scripts/create_dashboard.py data.csv

Custom output location:

bash
python3 scripts/create_dashboard.py data.csv -o my_dashboard.html

Control number of plots:

bash
python3 scripts/create_dashboard.py data.csv --max-plots 9

Custom Dashboard from Config: Create a JSON configuration file specifying exact plots:

bash
python3 scripts/create_dashboard.py data.csv --config config.json

Dashboard Config Format:

json
{
  "title": "Sales Analysis Dashboard",
  "plots": [
    {"type": "histogram", "column": "revenue"},
    {"type": "box", "column": "revenue", "group_by": "region"},
    {"type": "scatter", "column": "advertising", "group_by": "revenue"},
    {"type": "bar", "column": "product_category"},
    {"type": "correlation"}
  ]
}

Dashboard Plot Types:

  • histogram: Distribution of numeric column
  • box: Box plot, optionally grouped by category
  • scatter: Relationship between two numeric columns
  • bar: Count of categorical values
  • correlation: Heatmap of numeric correlations

Workflow Decision Tree

Use this decision tree to determine the appropriate approach:

User provides CSV file
│
├─ "Profile this data" / "Analyze this data" / Unfamiliar dataset
│  └─> Run data_profile.py first
│     Then offer visualization options based on findings
│
├─ "Create dashboard" / "Overview of the data" / Multiple visualizations needed
│  ├─ User knows exact plots wanted
│  │  └─> Create JSON config → run create_dashboard.py with config
│  └─ User wants automatic dashboard
│     └─> Run create_dashboard.py (auto mode)
│
└─ Specific visualization requested ("histogram", "scatter plot", etc.)
   └─> Use visualize_csv.py with appropriate flag

Best Practices

Show full SKILL.md (259 more words)Show less
Starting Analysis
  1. Always profile first for unfamiliar datasets: python3 scripts/data_profile.py data.csv
  2. Review the profiling output to understand:
    • Column data types and ranges
    • Missing data patterns
    • Data quality issues
    • Statistical distributions
Choosing Visualizations

Consult references/visualization_guide.md for detailed guidance. Quick reference:

  • Distribution: Histogram, box plot, violin plot
  • Relationship: Scatter plot, correlation heatmap
  • Time series: Line chart
  • Categories: Bar chart (preferred) or pie chart (use sparingly)
  • Comparison: Box plot grouped by category
Creating Dashboards
  • Automatic dashboard: Good for initial exploration
  • Custom dashboard: Better for presentations or specific analysis goals
  • Limit plots: Keep to 6-9 plots maximum for readability
  • Logical grouping: Group related visualizations together
Output Considerations
  • HTML: Best for interactive exploration (zoom, pan, hover tooltips)
  • PNG/PDF: Best for reports and presentations
  • SVG: Best for publications requiring vector graphics

Dependencies

The scripts require these Python packages:

bash
pip install pandas plotly numpy

For static image export (PNG, PDF, SVG), also install:

bash
pip install kaleido

Example Workflows

Exploratory Data Analysis
bash
# 1. Profile the data
python3 scripts/data_profile.py sales_data.csv -f html -o profile.html

# 2. Create automatic dashboard
python3 scripts/create_dashboard.py sales_data.csv -o dashboard.html

# 3. Dive deeper with specific plots
python3 scripts/visualize_csv.py sales_data.csv --scatter price sales --color region
python3 scripts/visualize_csv.py sales_data.csv --boxplot revenue --group-by product
Report Generation
bash
# Create specific visualizations for report
python3 scripts/visualize_csv.py data.csv --histogram age -o fig1_distribution.png
python3 scripts/visualize_csv.py data.csv --scatter income age -o fig2_correlation.png
python3 scripts/visualize_csv.py data.csv --bar category -o fig3_categories.png

# Generate data summary
python3 scripts/data_profile.py data.csv -f html -o data_summary.html
Interactive Dashboard
bash
# Create custom dashboard for presentation
# 1. First, create config.json with desired plots
# 2. Generate dashboard
python3 scripts/create_dashboard.py data.csv --config config.json -o presentation_dashboard.html

Troubleshooting

"Column not found" errors:

  • Run data profiling to see exact column names
  • CSV columns are case-sensitive
  • Check for leading/trailing spaces in column names

Empty or incorrect visualizations:

  • Verify data types (numeric vs categorical)
  • Check for missing data in plotted columns
  • Ensure sufficient non-null values exist

Script execution errors:

  • Verify dependencies are installed: pip list | grep plotly
  • Check Python version: Python 3.6+ required
  • For image export issues, install kaleido: pip install kaleido

Resources

scripts/
  • visualize_csv.py: Main visualization script with all chart types
  • data_profile.py: Automatic data profiling and quality analysis
  • create_dashboard.py: Multi-plot dashboard generator
references/
  • visualization_guide.md: Comprehensive guide for choosing appropriate chart types, best practices, and common patterns

© ailabs-393, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in packages/skills/csv-data-visualizer of ailabs-393/ai-labs-claude-skills.

  • SKILL.md
  • index.js
  • package.json
  • references/visualization_guide.md
  • scripts/create_dashboard.py
  • scripts/data_profile.py
  • scripts/visualize_csv.py

Open the folder on GitHubat commit 1a12bc7

Compare with similar skills

CSV Data Visualizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

CSV Data Visualizer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
CSV Data Visualizer this skillailabs-393/ai-labs-claude-skills454—~2.4kAutomated safety check: PassMIT
Paper FiguresEvoScientist/EvoSkills4741 repos~4.4kAutomated safety check: PassApache-2.0
CSV Data Analysis5zjk5/prompt-engineering127—~2.6kAutomated safety check: PassNone
Data Analysisfastclaw-ai/fastclaw1.4k—~410Automated safety check: PassCustom licence
Hybrid-Engine Data Analysiscode-yeongyu/oh-my-openagent70k—~1.4kAutomated safety check: PassCustom licence
Ukb Ppp Region FetchClawBio/ClawBio1.2k—~4.6kAutomated safety check: PassMIT

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Works with

Categories

Questions about CSV Data Visualizer

What does CSV Data Visualizer do?

This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data…. CSV Data Visualizer is an agent skill from ailabs-393/ai-labs-claude-skills. This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling.

When should I use CSV Data Visualizer?

CSV Data Visualizer fits situations like: tasks that involve Data analysis; tasks that involve CSV and tabular files; tasks that involve Data visualization.

How do I install CSV Data Visualizer in Claude Code?

Run `npx skills add ailabs-393/ai-labs-claude-skills --skill csv-data-visualizer -a claude-code`. Or copy the skill folder (packages/skills/csv-data-visualizer in ailabs-393/ai-labs-claude-skills) into .claude/skills/csv-data-visualizer in your project. Claude Code loads it when a task matches its description.

How do I install CSV Data Visualizer in Codex?

Run `npx skills add ailabs-393/ai-labs-claude-skills --skill csv-data-visualizer -a codex`. Or copy the skill folder (packages/skills/csv-data-visualizer in ailabs-393/ai-labs-claude-skills) into .agents/skills/csv-data-visualizer in your project. Codex loads it when a task matches its description.

Can I use CSV Data Visualizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ailabs-393/ai-labs-claude-skills --skill csv-data-visualizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/csv-data-visualizer, .gemini/skills/csv-data-visualizer, .github/skills/csv-data-visualizer and .opencode/skills/csv-data-visualizer in your project.

What does CSV Data Visualizer need to run?

Going by SKILL.md and its folder, CSV Data Visualizer needs Python and JavaScript for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3; Node.js.

Does CSV Data Visualizer access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is CSV Data Visualizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does CSV Data Visualizer use?

CSV Data Visualizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does CSV Data Visualizer use?

About 2.4k tokens (SKILL.md is roughly 9.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.

What are the alternatives to CSV Data Visualizer?

Skills that share tags, products or a category with CSV Data Visualizer: Paper Figures (EvoScientist/EvoSkills, 474 stars), CSV Data Analysis (5zjk5/prompt-engineering, 127 stars), Data Analysis (fastclaw-ai/fastclaw, 1.4k stars) and Hybrid-Engine Data Analysis (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains CSV Data Visualizer?

ailabs-393 (a GitHub user) maintains it in ailabs-393/ai-labs-claude-skills, which has 454 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on November 11, 2025.

Source: ailabs-393/ai-labs-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.