sql-ml-features

Use when preparing data for machine learning from SQL Server — feature engineering in T-SQL, building training/test datasets, statistical aggregations for ML pipelines, sampling strategies, data normalization and encoding in SQL, writing queries that feed pandas or scikit-learn, exporting to Parquet or CSV for model training, or when a data scientist asks for a 'feature table' or 'training set' from a SQL Server database.

Ir a la instalación

Repositorio: damusix/skills
Última actividad en el origen: 13 de junio de 2026 a las 20:48
Idioma detectado de SKILL.md: inglés
Estrellas: 59
Forks: 4

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/damusix/skills --skill sql-ml-features

El comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.

¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Explorador de archivos

7 archivos

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

Volver a la búsqueda Volver a la instalación

SQL for Machine Learning

Patterns for extracting ML-ready features from SQL Server — turning normalized relational data into the wide, denormalized, NULL-free datasets that models consume.

When to Use

Building a feature table or training dataset from application data

Feature engineering in T-SQL (rolling aggregations, lag features, RFM scoring)

Encoding categorical variables (one-hot, ordinal, frequency encoding)

Handling NULLs for ML (imputation strategies in SQL)

Sampling and train/test splitting from SQL Server

Exporting large datasets to CSV, Parquet, or pandas

Preventing data leakage in temporal feature queries

Writing queries that feed scikit-learn, XGBoost, or PyTorch pipelines

When NOT to use: application schema design (table design, naming conventions, access control), query performance tuning (execution plans, index tuning, wait stats), BI dashboards and summary reports (GROUPING SETS, pivot tables, dashboard queries), or running R/Python inside SQL Server (SQL Server ML Services).

Feature Table Build Workflow

Follow these steps in order. Each step has a validation checkpoint.

Set snapshot date — Anchor all features to a fixed @SnapshotDate. Never use GETDATE() inside feature queries.

Validate: SELECT @SnapshotDate returns the intended date.

name

sql-ml-features

description

SQL for Machine Learning

Patterns for extracting ML-ready features from SQL Server — turning normalized relational data into the wide, denormalized, NULL-free datasets that models consume.

When to Use

Building a feature table or training dataset from application data
Feature engineering in T-SQL (rolling aggregations, lag features, RFM scoring)
Encoding categorical variables (one-hot, ordinal, frequency encoding)
Handling NULLs for ML (imputation strategies in SQL)
Sampling and train/test splitting from SQL Server
Exporting large datasets to CSV, Parquet, or pandas
Preventing data leakage in temporal feature queries
Writing queries that feed scikit-learn, XGBoost, or PyTorch pipelines

Feature Table Build Workflow

Follow these steps in order. Each step has a validation checkpoint.

Set snapshot date — Anchor all features to a fixed @SnapshotDate. Never use GETDATE() inside feature queries.
- Validate: SELECT @SnapshotDate returns the intended date.

Category	Examples	T-SQL tools
Numeric	Raw values, `LOG(col + 1)` for skewed, ratios, z-scores	`LOG(col + 1)`, `SQRT`, window `AVG`/`STDEV`
Categorical	One-hot encoding, ordinal, frequency	`CASE`, `PIVOT`, `DENSE_RANK`, COUNT ratios
Temporal	Recency, duration, day-of-week	`DATEDIFF`, `DATEPART`, `DATENAME`
Rolling window	7-day sum, 30-day average	`SUM/AVG OVER (ROWS BETWEEN ...)`
Lag / offset	Previous value, delta from prior	`LAG`, `LEAD`
Interaction	Product, ratio of two features	Computed expressions in SELECT
Text signals	Length, keyword presence	`LEN`, `CHARINDEX`, `PATINDEX`
Missingness	Is this value missing?	`CASE WHEN col IS NULL THEN 1 ELSE 0 END`

ML concept	T-SQL pattern
Recency feature	`DATEDIFF(DAY, LastEventDate, @SnapshotDate)`
Frequency feature	`COUNT(*) OVER (PARTITION BY entity ORDER BY dt ROWS BETWEEN 29 PRECEDING AND CURRENT ROW)`
Rolling 7-day revenue	`SUM(Amount) OVER (ORDER BY OrderDate ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)`
Lag feature (t-1)	`LAG(Amount, 1) OVER (PARTITION BY CustomerId ORDER BY OrderDate)`
One-hot encode	`CASE WHEN Category = 'A' THEN 1 ELSE 0 END AS Category_A`
Ordinal encode	`DENSE_RANK() OVER (ORDER BY Category)`
Frequency encode	`COUNT() OVER (PARTITION BY Category) 1.0 / COUNT(*) OVER ()`
Log transform	`LOG(Amount + 1)` — the `+1` offset handles zero values; omit only when zeros are impossible
Quantile bucket	`NTILE(10) OVER (ORDER BY Score)`
Row hash for split	5-step chain: (1) `CAST(Id AS NVARCHAR(20))` → (2) `HASHBYTES('SHA2_256', ...)` → (3) `CAST(... AS BINARY(8))` → (4) `CAST(... AS BIGINT)` → (5) `ABS(...) % 10`
Mean imputation	`COALESCE(col, AVG(col) OVER ())`
Median imputation	`PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY col) OVER ()`
NULL indicator	`CASE WHEN col IS NULL THEN 1 ELSE 0 END AS col_is_missing`
Min-max scaling	`(col - MIN(col) OVER ()) / NULLIF(MAX(col) OVER () - MIN(col) OVER (), 0)`
Date bucketing	`DATE_BUCKET(WEEK, 1, EventDate)` (SQL Server 2022+)
Date truncation	`DATETRUNC(MONTH, EventDate)` (SQL Server 2022+)
First value in series	`FIRST_VALUE(col) OVER (PARTITION BY entity ORDER BY dt ROWS UNBOUNDED PRECEDING)`
Rolling volatility	`STDEV(col) OVER (PARTITION BY entity ORDER BY dt ROWS BETWEEN 29 PRECEDING AND CURRENT ROW)`

Mistake	What goes wrong	Fix
Using `AVG(col)` without understanding NULL exclusion	AVG divides by non-NULL count, not row count — mean is inflated when NULLs represent zeros	Use `AVG(COALESCE(col, 0))` when NULLs mean zero
Rolling window with `RANGE` instead of `ROWS`	Tied timestamps include extra rows, producing different counts per row	Always use `ROWS BETWEEN n PRECEDING AND CURRENT ROW`
Joining to future events in feature query	Leaks information that wasn't available at prediction time	Bound all joins with `AND EventDate <= @SnapshotDate`
Computing global statistics before the train/test split	Mean and variance incorporate test data into training features	Compute statistics only over training rows
Using `LAG` without checking temporal order	LAG requires `ORDER BY` — missing it gives non-deterministic offsets	Always include `PARTITION BY entity ORDER BY timestamp`
Forward-filling sensor NULLs across entity boundaries	PARTITION BY is missing, so last value bleeds across entities	Always `PARTITION BY entity_id` in forward-fill window
HASHBYTES on `NULL` key	`HASHBYTES(algo, NULL)` returns NULL — entity lands in no split	Exclude or handle NULL keys before splitting
UNPIVOT for categorical to indicator columns	`UNPIVOT` silently drops NULL values	Use `CROSS APPLY VALUES` to preserve NULLs as zeros
Random split for time-series data	Test set leaks future patterns into training window	Use a cutoff date for time-series train/test split
Including the label column in lag features	`LAG(label)` leaks ground truth about adjacent rows	Exclude any derivative of the target from feature set

sql-ml-features

Opciones de instalación

Revisa los archivos de origen

SQL for Machine Learning

When to Use

Feature Table Build Workflow

SQL for Machine Learning

When to Use

Feature Table Build Workflow

Feature Engineering Taxonomy

Quick Reference: SQL Pattern → ML Concept

Common Mistakes

Reference Files

sql-ml-features

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

SQL for Machine Learning

When to Use

Feature Table Build Workflow

SQL for Machine Learning

When to Use

Feature Table Build Workflow

Feature Engineering Taxonomy

Quick Reference: SQL Pattern → ML Concept

Common Mistakes

Reference Files