data-quality
✓Diagnose and fix data quality problems in datasets. Use when working with dirty data, finding duplicates, handling missing values, detecting outliers/anomalies, validating constraints (functional dependencies, referential integrity), profiling datasets, or cleaning data for analysis or ML. Covers the full data quality lifecycle - define, detect, clean, measure.
Installation
SKILL.md
| Data overview | dataprofiling.py | profiledataframe(df) | | Find quality issues | dataprofiling.py | detectglitches(df) | | Missing values | missingdata.py | analyzemissing(df) | | Imputation | missingdata.py | imputemean/median/regression() | | Duplicates | duplicatedetection.py | findduplicates(df, cols) |
| Deduplication | duplicatedetection.py | deduplicate(df, cols) | | Outliers | anomalydetection.py | detectanomalies(df) | | Constraint check | constraintchecking.py | validateconstraints(df, rules) | | String matching | similaritymetrics.py | jarowinklersimilarity() |
Re-run profiling and constraint checks on cleaned data to verify improvements.
Diagnose and fix data quality problems in datasets. Use when working with dirty data, finding duplicates, handling missing values, detecting outliers/anomalies, validating constraints (functional dependencies, referential integrity), profiling datasets, or cleaning data for analysis or ML. Covers the full data quality lifecycle - define, detect, clean, measure. Source: masterkram/data-quality-skill.
Facts (cite-ready)
Stable fields and commands for AI/search citations.
- Install command
npx skills add https://github.com/masterkram/data-quality-skill --skill data-quality- Category
- {}Data Analysis
- Verified
- ✓
- First Seen
- 2026-02-01
- Updated
- 2026-02-18
Quick answers
What is data-quality?
Diagnose and fix data quality problems in datasets. Use when working with dirty data, finding duplicates, handling missing values, detecting outliers/anomalies, validating constraints (functional dependencies, referential integrity), profiling datasets, or cleaning data for analysis or ML. Covers the full data quality lifecycle - define, detect, clean, measure. Source: masterkram/data-quality-skill.
How do I install data-quality?
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/masterkram/data-quality-skill --skill data-quality Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor
Where is the source repository?
https://github.com/masterkram/data-quality-skill
Details
- Category
- {}Data Analysis
- Source
- skills.sh
- First Seen
- 2026-02-01