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Hash tables (chaining vs open addressing) and Bloom filters: complexity, trade-offs, and implementation patterns.

Graph representations — adjacency matrix, adjacency list, edge list — with complexity tables, implementation patterns, and biological network use cases.

BST operations and complexity, with a clean implementation using parent pointers to support all standard operations.

Foundational algorithms — Euclidean GCD, Newton's method for cube root — with complexity analysis and clean implementations.

Multi-pattern string matching in O(n + m + z) via a trie augmented with KMP-style failure links.

Zero-Shot Mutation Effect Prediction with NumPy

Spawn unlimited AI agents. Configure once, deploy forever. No CLI. No boilerplate. Just open your browser and go.

Core Python for bioinformatics: types, bio-string manipulation (codons, reverse complement), functions, and file I/O for FASTA/GenBank.

This skill automatically calculates and semi-automatically submits YSS company's overtime slips. It processes Zentao OA attendance data, applies company rules and Chinese holidays to determine reportable hours, and submits them to Kingdee s-HR Cloud.

RNA-seq differential expression analysis and normalization workflows.

Bio.PDB parsing, distance/RMSD/TM-score calculation, DSSP, Ramachandran, PWM/PROSITE, GO enrichment, and KEGG REST API utilization.

RNA-seq differential expression using DESeq2 and edgeR, covering normalization strategies (RPKM/TPM/DESeq2 size factors), exploratory PCA, volcano plots, GSEA, and STAR/Salmon/featureCounts pipelines.