Skill UI

Comprehensive Python toolkit for molecular biology: sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access. Ideal for batch processing, custom bioinformatics pipelines, and BLAST automation.

Analyze, edit, compare, annotate and visualize phylogenetic or hierarchical trees with ETE 4. Supports Newick/Nexus I/O, rooting and pruning, Robinson-Foulds topology comparison, gene-tree evolutionary events, NCBI/GTDB taxonomy queries and SmartView rendering.

Perform bounded, local exploratory analysis on supported scientific files like CSV, JSON, and HDF5. Inspect metadata, check missingness, and audit outliers without executing code or accessing networks. Generates deterministic reports for data validation before modeling.

Geniml validates genomic interval workflows, BED files, and model compatibility for ML/statistical analysis. Plan Region2Vec/scEmbed runs, inspect tokenizers, and assess consensus universes locally without network requests. Requires Python 3.10+ and uv for reproducible bioinformatics ML pipelines.

Process, clean, compare, and search tandem mass spectra with matchms 0.33.1. Covers MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity scoring, library matching, score matrices, and molecular-similarity networks in Python.

Create, analyze, and visualize complex networks in Python with NetworkX 3.x. Build graphs from data, run algorithms like shortest paths, PageRank and centrality, detect communities, generate synthetic networks, and read/write GraphML, JSON or edge-list files.

This skill enables the construction and analysis of phylogenetic trees using tools like MAFFT for alignment, IQ-TREE 2 for maximum likelihood inference, and FastTree for large datasets. It supports evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies. Visualization is handled via ETE3 or FigTree. Ideal for inferring evolutionary relationships, detecting horizontal gene transfer, and reconstructing ancestral sequences in biological research.

Python toolkit for biological data: sequence manipulation, pairwise and multiple alignment, phylogenetic trees, alpha/beta diversity and UniFrac metrics, ordination (PCoA), PERMANOVA, and FASTA/Newick/BIOM I/O for microbiome analysis.

This comprehensive guide details best practices for securing a ClickHouse deployment. It covers essential security measures including role-based access control (RBAC) for granular user permissions, implementing row-level security for multi-tenancy, configuring network IP restrictions, enabling TLS encryption, and setting up detailed audit logging. Use this when hardening a production ClickHouse instance or enforcing strict data governance.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

A comprehensive, advanced playbook for conducting deep network traffic analysis and PCAP forensics. Covers essential skills like PCAP repair, advanced Wireshark filtering (HTTP, DNS, TLS), data extraction (file carving, credential harvesting), and detecting covert channels. It guides users through sophisticated techniques, including TLS decryption workflows and DNS tunneling identification.