data-exploration
Explore and analyze pilot data sets to uncover patterns, anomalies, and initial insights. Use when performing ad-hoc data investigations, validating data quality, or preparing exploratory visualizations for hypothesis generation.
npx skills add majiayu000/claude-skill-registry --skill data-exploration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Exploration ## Overview The data-exploration skill enables systematic investigation of datasets to uncover patterns, validate quality, and surface insights that inform decision-making. This skill is used when you need to understand a new dataset, validate data quality before analysis, or generate initial findings for hypothesis formation. ## Quick Start Use this skill when you need to: - **Understand a new dataset**: Get initial feel for structure, quality, and contents - **Validate data quality**: Check for completeness, accuracy, and consistency - **Find patterns**: Identify trends, correlations, or anomalies - **Generate insights**: Surface findings that could inform strategic decisions - **Prepare for hypothesis testing**: Establish baseline understanding before formal analysis ## Workflow Decision Tree ``` New Dataset → Data Profiling → Quality Assessment → Pattern Discovery → Insight Generation ↓ ↓ ↓ ↓ [Statistical Summary] [Quality Report] [Visual Analysis] [Intelligence Brief] ``` ## Data Profiling ### Step 1: Initial Assessment When you receive a new dataset, start with basic profiling: ```python import pandas as pd import numpy as np def profile_dataset(df: pd.Data
- Overview
- Quick Start
- Workflow Decision Tree
- Data Profiling
- Step 1: Initial Assessment
- Step 2: Statistical Summary
- Quality Assessment
- Data Quality Checks
- Duke Data Governance Alignment
- Pattern Discovery
- Visual Analysis
- Anomaly Detection
- Insight Generation
- Generate Intelligence Brief
pip install pandas numpy matplotlib seaborn scikit-learn
What does the data-exploration skill do?
Explore and analyze pilot data sets to uncover patterns, anomalies, and initial insights. Use when performing ad-hoc data investigations, validating data quality, or preparing exploratory visualizations for hypothesis generation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-exploration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.