Skill UI

scikit-bio provides a Python-native biological data toolkit for reading/writing FASTA/Newick files, manipulating sequences, building alignments and phylogenies, and calculating diversity metrics for microbiome and ecology workflows.

This skill provides structured methods for detecting credential stuffing attacks by analyzing authentication logs. It focuses on identifying complex patterns such as login velocity anomalies, high IP diversity, password spray techniques, and suspicious geographical distributions of failed logins. It is essential for SOC analysts conducting threat hunting or building robust detection rules against account takeover attempts.

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 guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.