Exploring Data
by oaustegard
Exploratory data analysis using ydata-profiling. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Generates interactive HTML or JSON reports with statistics, visualizations, correlations, and quality alerts.
Skill Details
Repository Files
11 files in this skill directory
name: exploring-data description: Exploratory data analysis using ydata-profiling. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Generates interactive HTML or JSON reports with statistics, visualizations, correlations, and quality alerts. metadata: version: 0.0.3
Exploring Data
Workflow
1. Check if installed (instant)
bash /mnt/skills/user/exploring-data/scripts/check_install.sh
Returns: installed or not_installed
2. Install if needed (one-time, ~19s)
if [ "$(bash check_install.sh)" = "not_installed" ]; then
bash /mnt/skills/user/exploring-data/scripts/install_ydata.sh
fi
3. Run analysis (always generates JSON + HTML by default)
bash /mnt/skills/user/exploring-data/scripts/analyze.sh <filepath> [minimal|full] [html|json]
Defaults: minimal + html (also generates JSON)
Output:
eda_report.html- Interactive report for usereda_report.json- Machine-readable for Claude analysis
4. If Claude needs to analyze (user asks "what do you think?" etc.)
python /mnt/skills/user/exploring-data/scripts/summarize_insights.py /mnt/user-data/outputs/eda_report.json
Reads: eda_report.json (comprehensive ydata output)
Writes: eda_insights_summary.md (condensed for Claude)
Outputs to stdout: Formatted markdown summary
Claude should read the stdout markdown summary, NOT the full JSON report.
Invocation Examples
# Standard workflow (user views HTML)
bash analyze.sh /mnt/user-data/uploads/data.csv
# Produces: eda_report.html + eda_report.json
# Link user to: computer:///mnt/user-data/outputs/eda_report.html
# User asks Claude to analyze
bash analyze.sh /mnt/user-data/uploads/data.csv
python summarize_insights.py /mnt/user-data/outputs/eda_report.json
# Claude reads the stdout markdown summary
# Claude can then provide analysis based on patterns/insights
# Full mode for comprehensive analysis
bash analyze.sh /mnt/user-data/uploads/data.csv full
# JSON-only output (skip HTML generation)
bash analyze.sh /mnt/user-data/uploads/data.csv minimal json
Modes
Minimal (default, 5-10s): Dataset overview, variable analysis, correlations, missing values, alerts
Full (10-20s): Everything in minimal + scatter matrices, sample data, character analysis, more visualizations
User Triggers for Full Mode
"comprehensive analysis", "detailed EDA", "full profiling", "deep analysis"
Otherwise use minimal.
Related Skills
Xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Analyzing Financial Statements
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Data Storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Kpi Dashboard Design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Dbt Transformation Patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Sql Optimization Patterns
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
Anndata
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Xlsx
Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.
