Dask Data Science
Skill Verified ActivePart of the AlterLab Academic Skills suite. Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
To provide an expert assistant for scaling data science workflows using Dask, enabling users to process datasets that exceed single-machine memory or require parallel computation.
Features
- Distributed computing for pandas/NumPy
- Larger-than-memory data processing
- Parallel file processing
- Integration with existing pandas/NumPy code
- Scales from laptops to clusters
Use Cases
- Scaling pandas operations to larger datasets
- Parallelizing computations for performance
- Processing multiple files efficiently (CSVs, Parquet, JSON)
- Distributing workloads across multiple cores or machines
Non-Goals
- Out-of-core analytics on a single machine (use vaex)
- In-memory speed optimization (use polars)
- Replacing core pandas/NumPy functionality for in-memory data
Workflow
- Load data using Dask's parallel readers (read_csv, read_parquet)
- Perform operations (filtering, transformations, aggregations) on Dask DataFrames, Arrays, or Bags
- Leverage Dask's lazy evaluation and task graph construction
- Trigger computation with .compute() or dask.compute()
- Optimize performance through chunking, persist, and scheduler selection
- Save results or convert to pandas for final analysis
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
Similar Extensions
AlterLab Zarr
99Part of the AlterLab Academic Skills suite. Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Dask
98Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Spark Engineer
99Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
Zarr Python
97Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
OraClaw Forecast
100Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.
SHAP Model Interpretability
100Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.