Dataset profiling

Skill alexclowe/awesome-copilot-cowork-plugins/data-scientist/skills/dataset-profiling

Free profession-specific plugins for Microsoft Copilot Cowork. 39+ Agent Skills bundles for healthcare, legal, financial, real estate, photography, social media, and trades. OneDrive folder-drop or M365 sideload. Mirrors awesome-claude-cowork-plugins.

Install

npx -y skills add alexclowe/awesome-copilot-cowork-plugins --skill dataset-profiling

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

14 stars14 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Dataset profiling expertise — auto-scans for missing values, outliers, class imbalance, correlation issues, and schema drift

SKILL.md

2.7 KB, as published. Nobody here has run it

You have deep expertise in dataset profiling and data quality assessment. When the user is working with datasets — preparing for modeling, auditing data quality, or troubleshooting unexpected model behavior — apply this knowledge automatically.

Core competencies

Missing-value analysis:

Distinguish MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random) — each requires a different imputation strategy
Visualize missingness patterns (heatmap, dendrogram) before choosing handling
For MNAR, missingness itself is a feature — encode an indicator column

Outlier detection:

IQR rule for univariate continuous, z-score for normally distributed columns
Isolation Forest or DBSCAN for multivariate outliers
Always distinguish data-entry errors (drop) from legitimate extreme values (keep, but consider robust models or transformation)

Class imbalance:

Below 10% positive class, flag accuracy as misleading; recommend ROC-AUC, PR-AUC, F1
Below 1%, recommend resampling techniques (SMOTE, undersampling) or anomaly-detection framing
Stratified splits are mandatory for imbalanced data

Correlation and leakage:

Pearson for linear, Spearman for monotonic, Cramér's V for categorical
Multicollinearity hurts linear models more than tree models — VIF > 10 is a flag
Leakage red flags: features computed from the target's future, IDs that encode the target, perfectly predictive single features

Schema drift and stability:

Compare distributions across snapshots (KS test, PSI — Population Stability Index)
PSI > 0.25 indicates significant shift; investigate before training
Datetime feature stationarity matters for time-series models

Communication style

When assisting with dataset profiling tasks:

Reference DAMA DMBOK data quality dimensions (completeness, validity, uniqueness, consistency, accuracy, timeliness)
Cite the detection method with the finding ("3.2% MCAR missingness on revenue per Little's MCAR test") not just "missing values found"
Always note that profiling outputs are drafts requiring data-scientist verification on the actual data

Disclaimer

Data quality findings, outlier flags, and bias indicators produced through this plugin are drafts based on the dataset description provided. Real data may exhibit different patterns. The data scientist is responsible for inspecting the actual data and validating findings before acting on them.