Update scikit-bio

Author: TKassis

docs/skills.md

@@ -61,7 +61,7 @@
 
 ### Data Management & Infrastructure
 - **LaminDB** - Open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). Provides unified platform combining lakehouse architecture, lineage tracking, feature stores, biological ontologies (via Bionty plugin with 20+ ontologies: genes, proteins, cell types, tissues, diseases, pathways), LIMS, and ELN capabilities through a single Python API. Key features include: automatic data lineage tracking (code, inputs, outputs, environment), versioned artifacts (DataFrame, AnnData, SpatialData, Parquet, Zarr), schema validation and data curation with standardization/synonym mapping, queryable metadata with feature-based filtering, cross-registry traversal, and streaming for large datasets. Supports integrations with workflow managers (Nextflow, Snakemake, Redun), MLOps platforms (Weights & Biases, MLflow, HuggingFace, scVI-tools), cloud storage (S3, GCS, S3-compatible), array stores (TileDB-SOMA, DuckDB), and visualization (Vitessce). Deployment options: local SQLite, cloud storage with SQLite, or cloud storage with PostgreSQL for production. Use cases: scRNA-seq standardization and analysis, flow cytometry/spatial data management, multi-modal dataset integration, computational workflow tracking with reproducibility, biological ontology-based annotation, data lakehouse construction for unified queries, ML pipeline integration with experiment tracking, and FAIR-compliant dataset publishing
-- **Modal** - Serverless cloud platform for running Python code with minimal configuration, specialized for AI/ML workloads and scientific computing. Execute functions on powerful GPUs (T4, L4, A10, A100, L40S, H100, H200, B200, B200+), scale automatically from zero to thousands of containers, and pay only for compute used. Key features include: declarative container image building with uv (recommended)/pip/apt package management, automatic autoscaling with configurable limits and buffer containers, GPU acceleration with multi-GPU support (up to 8 GPUs per container, up to 1,536 GB VRAM), persistent storage via Volumes (v1 and v2) for model weights and datasets, secret management for API keys and credentials, scheduled jobs with cron expressions, web endpoints for deploying serverless APIs (FastAPI, ASGI, WSGI, WebSockets), parallel execution with `.map()` for batch processing, input concurrency and dynamic batching for I/O-bound workloads, and resource configuration (CPU cores, memory, ephemeral disk up to 3 TiB). Supports custom Docker images, Micromamba/Conda environments, integration with Hugging Face/Weights & Biases, and distributed multi-GPU training. Free tier includes $30/month credits. Use cases: ML model deployment and inference (LLMs, image generation, speech, embeddings), GPU-accelerated training and fine-tuning, batch processing large datasets in parallel, scheduled compute-intensive jobs, serverless API deployment with autoscaling, protein folding and computational biology, scientific computing requiring distributed compute or specialized hardware, and data pipeline automation
+- **Modal** - Serverless cloud platform for running Python code with minimal configuration, specialized for AI/ML workloads and scientific computing. Execute functions on powerful GPUs (T4, L4, A10, A100, L40S, H100, H200, B200, B200+), scale automatically from zero to thousands of containers, and pay only for compute used. Key features include: declarative container image building with uv (recommended)/pip/apt package management, automatic autoscaling with configurable limits and buffer containers, GPU acceleration with multi-GPU support (up to 8 GPUs per container, up to 1,536 GB VRAM), persistent storage via Volumes (v1 and v2) for model weights and datasets, secret management for API keys and credentials, scheduled jobs with cron expressions, web endpoints for deploying serverless APIs (FastAPI, ASGI, WSGI, WebSockets), parallel execution with `.map()` for batch processing, input concurrency and dynamic batching for I/O-bound workloads, Sandboxes for isolated execution of untrusted or agent-generated code with network (CIDR) restrictions, and resource configuration (CPU cores, memory, ephemeral disk up to 3 TiB). Supports custom Docker images, Micromamba/Conda environments, integration with Hugging Face/Weights & Biases, and distributed multi-GPU training. Free tier includes $30/month credits. Use cases: ML model deployment and inference (LLMs, image generation, speech, embeddings), GPU-accelerated training and fine-tuning, batch processing large datasets in parallel, scheduled compute-intensive jobs, serverless API deployment with autoscaling, protein folding and computational biology, scientific computing requiring distributed compute or specialized hardware, and data pipeline automation
 - **Optimize for GPU** - GPU-accelerate Python code using the NVIDIA RAPIDS ecosystem and related libraries for 10x–1000x speedups on suitable workloads. Covers 12 GPU libraries with decision framework for choosing the right tool: CuPy (drop-in NumPy/SciPy replacement for array operations, FFT, linear algebra), Numba CUDA (custom GPU kernels with fine-grained thread/block/shared-memory control), Warp (JIT-compiled simulation kernels with built-in spatial types for physics, mesh ray casting, differentiable programming, and robotics), cuDF (drop-in pandas replacement for dataframe ETL, groupby, joins), cuML (drop-in scikit-learn replacement for classification, regression, clustering, dimensionality reduction, preprocessing), cuGraph (drop-in NetworkX replacement for PageRank, centrality, community detection, shortest paths), KvikIO (GPUDirect Storage for high-performance file IO bypassing CPU memory, S3/HTTP direct-to-GPU reads, Zarr GPU backend), cuxfilter (GPU-accelerated interactive cross-filtering dashboards with Bokeh, Datashader, and Deck.gl), cuCIM (drop-in scikit-image replacement for image filtering, morphology, segmentation, plus fast whole-slide image reading for digital pathology), cuVS (GPU-accelerated vector/similarity search with CAGRA, IVF-Flat, IVF-PQ for RAG and recommender systems), cuSpatial (GPU-accelerated GeoPandas replacement for spatial joins, point-in-polygon, trajectory analysis), and RAFT/pylibraft (sparse eigensolvers, device memory management, multi-GPU communication). All libraries interoperate via CUDA Array Interface for zero-copy data sharing. Includes optimization workflow (profile first, assess GPU suitability, start with drop-in replacements, minimize host-device transfers), code transformation patterns for each library, memory management principles, and common pitfalls. Use cases: accelerating NumPy/pandas/scikit-learn/NetworkX/scikit-image/GeoPandas/Faiss workloads, physics simulation, differentiable rendering, particle systems, vector search for RAG pipelines, GPUDirect Storage file IO, interactive data exploration dashboards, geospatial analysis, medical imaging, and sparse eigenvalue problems
 
 ### Cheminformatics & Drug Discovery