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skillry-data-ml-ai-engineering
Data and ML engineering: data pipelines, ETL/ELT, data quality & validation, feature engineering, notebook hygiene, model training/serving, experiment tracking, and dataset versioning.
What's Inside
Agents2
data-engineer
/data-engineer
Use when you need to build, implement, or repair data pipelines, ETL/ELT loads, data quality checks, and dataset versioning. This agent makes focused changes and applies fixes to pipeline code.
ml-pipeline-reviewer
/ml-pipeline-reviewer
Use when you need a read-only review of an ML training or serving pipeline for reproducibility, data leakage, checkpointing, experiment tracking, serving patterns, versioning, rollback, and drift monitoring.
Skills6
data-pipeline-review
/311-data-pipeline-review
Use when you need to review or design an ETL/ELT data pipeline for idempotency, incremental loads, orchestration (Airflow/Dagster/cron), backfill safety, partitioning, and failure recovery.
data-quality-validation
/312-data-quality-validation
Use when you need to add or review data quality validation — schema checks, null/duplicate/range assertions, Great Expectations or pandera suites, data contracts, freshness, and failure routing.
ml-training-pipeline-review
/313-ml-training-pipeline-review
Use when you need to review an ML training pipeline for reproducibility, data/train/val/test splitting, data leakage, checkpointing, and experiment tracking (MLflow/W&B).
model-serving-and-inference
/314-model-serving-and-inference
Use when you need to review or design model serving and inference — batch vs online patterns, latency/throughput, model versioning, rollback, and monitoring for drift and skew.
notebook-hygiene
/315-notebook-hygiene
Use when you need to review or clean up Jupyter notebooks — out-of-order cells, hidden state, parameterization, nbconvert/papermill execution, output bloat, and secrets that must stay out of notebooks.
Stats
Version0.1.0
Released
LanguagePython
Stars0
MaintenanceGood
LicenseMIT
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Uses power tools
Uses Bash, Write, or Edit tools
