Plugins
4 plugins@ai-builder-club
AI Builder Club Skills
AI Builder Club Skills from AI-Builder-Club/skills.
8 skills · plugin
curated
Deploy GKE Cluster
Creates a GKE cluster, configures networking, sets up observability, and applies reliability patterns.
5 skills · plugin
curated
GKE Cost & Multitenancy
For platform engineers optimizing GKE costs and managing multi-tenant clusters with resource isolation.
2 skills · plugin
curated
Analyze Single-Cell RNA-Seq
Analyze single-cell RNA-seq data using Scanpy, including quality control, normalization, clustering, marker gene identification, and visualization.
9 skills · plugin
Results for “clu”
41 skillspolars-bio
Perform fast genomic interval operations (overlap, nearest, merge, coverage, cluster, complement, subtract, count-overlaps), multi-format bioinformatics I/O, DataFusion SQL, and pileup on Polars DataFrames via the polars-bio library, serving as a scalable alternative to bioframe and bedtools.
17 · bundle
dask
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.
1 · bundle
dask
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.
3 · bundle
alterlab-vaex
Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.
60 · bundle
alterlab-networkx
Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.
60 · bundle